A component- and connector-based approach for end

A component- and connector-based approach for end-user composite w e b
applications development
D. Lizcano a , , F. Alonso b , J. Soriano b , G. López b
Department of Computer Science, Open University of Madrid, Spain
School of Computer Science, Universidad Politécnica de Madrid, Spain
a bs t r a c t
Enabling real end-user development is the next logical stage in the evolution of Internet-wide service-based applications. Successful composite
applications rely on heavyweight service orchestra-tion technologies that raise the bar far above end-user skills. This weakness can be attributed to the
fact that the composition model does not satisfy end-user needs rather than to the actual infrastructure technologies. In our opinion, the best way to
overcome this weakness is to offer end-to-end composition from the user interface to service invocation, plus an understandable abstraction of building
blocks and a visual composition technique empowering end users to develop their own applications. In this paper, we present a visual framework for end
users, called FAST, which fulfils this objective. FAST implements a novel composition model designed to empower non-programmer end users to create
and share their own self-service composite applications in a fully visual fashion. We projected the development envi-ronment implementing this model
as part of the European FP7 FAST Project, which was used to validate the rationale behind our approach.
1. Introduction
The recent evolution of IT and the software business has significant implications for software products, development and use.
Over the past few years, traditional software products, sales and
licence fees have declined, whereas business value and revenues
have shifted to SaaS-based services (Anon., 2006). Software as a
service (SaaS) is a recognized approach that emerged from the traditional application service provider (ASP) delivery method. As a
result, Internet services are becoming more important than product revenues and are also including traditional product terms and
ideas (marketing, support, product concept, package and distribution, and so on) (Anderson, 2006). Since the year 2000, both
the business-to-business (B2B) and business-to-customer (B2C) IT
This often leads to cost savings on purchases of traditional commercial software for performing particular functionalities, like
office packages and desktop applications (Lizcano et al., 2008).
Service-oriented architectures (SOAs) increase asset reuse, reduce
integration expenses and improve the rate at which businesses can
respond to new demands. The main idea is to enable enterprises
and end users to create their own software solutions by composing
and orchestrating heterogeneous Internet services, enabling real
end-user development (Lieberman et al., 2006) within the future
Internet of Services (IoS)(Anon., 2009).
End users should be able to create their own software solution
that exactly meets their requirements within a very short development time by composing a solution from heterogeneous resources
and their front-ends (Davenport, 2005).
There are key proposals offering DIY (Lizcano et al., 2008)
guidanceonevolving SOAsto meet end-user demands and requirements, like iGoogle1 (http://www.google.com/ig), Yahoo! Pipes
and Yahoo! Dapper (http://pipes.yahoo.com/), OpenKapow (http://
kapowsoftware.com) or EzWeb (http://conwet.fi.upm.es/morfeoproject/ezweb blog/?lng=en).Theiraimistoget enduserstoappreciate the benefits of web services by fostering composition, loose
coupling and reuse on the front-end layer, and moving towards
a user-centred service as opposed to the traditional B2B conception (Scaffidi et al., 2005). However, existing solutions have several
weaknesses: one of them is that they fail to provide a user-based
Corresponding author. Tel.: +34 902 02 00 03.
E-mail addresses: david.lizcano@udima.es (D. Lizcano), falonso@fi.upm.es
(F. Alonso), jsoriano@fi.upm.es (J. Soriano), glopez@fi.upm.es (G. López).
As of 1 November 2013, iGoogle became Chrome Productivity Tools (Chrome
PT). In this paper, we use the original term, because we ran our experiment prior to
this date.
front-end to enable the user-centred composition of back-end services. This has been identified as a major shortcoming of the Future
Internet (Anon., 2009). Solutions tend to be limited to the combination of just content rather than applications, overlooking end
users and their participation. A high dependency on the underlying computing infrastructure is another limiting factor (Schneider,
1999). Also, changesinthe original wrapped applications, the backend services or the portal infrastructure may cause a user interface
(UI) failure and disable user-service interaction because there is no
formal user-centred framework.
Our aim, presented inthis paper, wasto design and implement a
visualmashup composition frameworkcapableofsolving these problems (as shown in the experiment reported later). This framework
implements a development model, composition techniques and a
visual compositional language, all of which are centred on the end
user. The development model is based on connectors and components. The composition techniques should help users to solve
their problem by linking components using connectors following
visual guidance that abstracts end users away from programming
language concepts like variables or data types. The visual compositional language should enable the composition of user-centred
front-ends capableofexploiting business services. This framework,
which has some similarities and overlaps with general-purpose
design-by-contract approaches (Lizcano et al., 2008), offers a visual
composition process that aims to enable end users to create their
own real-world solutions, fostering an open innovation process for
software development (Lizcano et al., 2009). The source of the
innovation is merely the result of using a resource (for example,
a service) in a new, unexpected way for a novel purpose or combined with another apparently unrelated resource. This framework
was based on the FAST initiative. FAST was a STREP project partially funded under the European Commission’s 7th Framework
Programme, as part of NESSI (http://www.nessi-europe.com), the
Networked European Software and Services Initiative. FAST is an
acronym for Fast and Advanced Storyboard Tool and is a tool for
merging and filtering data providedby different web services. FAST
proposes a top-down approach whereby users define a high-level
screen flow by way of a storyboard and then use operators, data
sources and wrapped resources to define what each screen will do.
Perhaps the best way to uncover the potential of these DIY proposals is to use a running example of an everyday problem with
which we are all familiar in order to illustrate what type of applications could be built using a user-centred B2C approach. This
example would serve as a benchmark for exploring the strengths
and weaknesses of today’s tools, and the potential of the presented
framework and its internal design. The example is as follows:
A R&D worker has to make frequent national and international
trips, scheduled using a web-based personal organizer shared by
all research group members. All face-to-face meetings are posted
on this personal organizer, specifying the meeting date and time,
venue and agenda. The researcher usually travels with two travel
agencies, one specializing in high-speed trains and the other in
long-distanceflights, and bothmanage all the travel and accommodation options at a full range of hotels. The researcher adds a new
trip to his personal organizer. He wants to look up and locate the
and checks what travel options they offer, as well as their price. He
usually compares the two options and chooses one agency or the
other depending on the travel options, length of stay and price. If
the trip is to last longer than a day, the researcher searches hotels
nearto the meeting venue and checks the prices per room and night
offered by the travel agencies. It is the researcher’s job to calculate
how much the travel and chosen accommodation will cost, check
that there is enough money available for the trip and deduct it from
his personal budget. Then the researcher makes the bookings one
by one. Finally, the researcher checks the Internet for information
about his destination, demographics, weather forecast, etc.
Although the researcher has access to a number of software
solutions to perform this routine task (project agenda, personal
organizer, travel agency services, department cash flow program,
etc.), he has to use each service offering distributed and heterogeneous information separately. If there were an EUD tool
that end users could use to create an architecture integrating a
personal organizer, map, transport search service, hotel booking
service, payment gateway and travel query service, i.e.,a situational
application to satisfy end-user needs, composed of an information system integrating all the above services, then the system
would have a single entry-point for execution: the personal organizer and appointments (date, time and place). It would then be
possible, based on a appointment recorded in the personal organizer,tosynchronize all the services, searching for transport, hotels,
prices, on-line payment, etc., all based on a visually rendered usercomprehensible, unified dataflow. Fig. 1 shows an example of one
such application, built by an end user using the framework proposed in this article.
The remainder of the paper is structured as follows. Section 2
presents the related work and background of our research. This
related work was the source of the specific requirements for
generating the proposed composition model. The following sections describe the main parts of the proposed composition model:
Section3deals with the component model, Section4with the composition technique, and Section5 with the composition languages.
Section6details the executionlanguage usedtodeploy theapplication created by the end user for execution. Section7 then presents
the results of a study that we conducted to test the validity of our
compositionmodel. Finally, Section8discussesthe conclusions and
the limitationsofthe proposed approach and briefly outlines future
2. Related work
Companiesare beginningtofocusonpeopleasthe entry point to
SOA, data sources, disperse resources and web services consumption (Lizcanoet al., 2008). Thus they needa means to bridge the gap
between people and services, which is when they come up against
the traditional shortcomings of composite applications.
2.1. DIY: the web as an ecosystem of user-centred resources
A number of web user-centred composite application frameworks are beginning to proliferate. Worthy of note are IBM’s
solution, named SOA for people (Anon., 2008), and SAP’s proposal,
called SOA-People (http://www.soapeople.com). They focus on a
portal framework acting as a SOA front-end to maximize people’s productivity and collaboration. The increasing interest in this
approach is indicative of the current importance of user-centred
SOA in the business world. However, existing approaches focus
on employing particular Web 2.0-based technologies to deliver a
front-end to SOA rather than lending attention to the composition
process and component modelling.
Other companies are focusing on a different approach. They
highlight the mashup, Web 2.0 and end-user development ideas,
but overlook the exploitation of real back-end business logic. The
potential of these tools like iGoogle, Yahoo! Pipes, OpenKapow, Apple
Dashboards, PopFly, Marmite (Wong and Hong, 2007) and AMICO
(Obrenovic and Gasevic, 2009) is very promising. The construction
of EUD web applications is one of the aims of today’s DIY applications. However, as explained below and demonstrated by our
experiment, non-programmer end users will find it hard to build
an application like this unassisted, even if they do have access to
Fig. 1. Example of a real EUD web application.
Table 1
Comparison of today’s most successful EUD tools.
EUD tool
Main objective
Event model
Dataflow support
Taxonomy of
Design strategy
Yahoo! Pipes and
Create complex
RSS feeds
event handler
HTTP headers,
MIME contents
Based on
and keywords
Create a visual
mashup of
Create an office
suite mashup
Actions on JMI
based on MOM
Pub/sub with XML
Operators, RSS
readers and
Visible or
Based on
and keywords
event handler
Action triggers
over SOAP
Triggers and
handlers of
Based on
and keywords
Based on
and keywords
Knowledge of
recursive calls
Create a
complex portal
from screen
Create a
pipeline on a
Command line
actions and plain
text files
diagrams with
control flow
Prototypes to
be command
functions and
Screens, robots
operating on a
screen, and
nodes and
directed arcs
Create visual
mashup of data
provided from
web services
CRUD based on
Visual annotated
data types to fulfil
pre/post conditions
Screen flows,
screens, forms,
web services,
etc.) conditions
and connectors
Based on
keywords, recommendations
and SEO
guided by
visual support
Apple Dashboards
Based on
and keywords
the necessary components. To give a better idea of these limitations, Table 1 compares the major features of each tool. This table
also summarizes the features of the framework proposed in this
article, which are detailed in the following sections. This should
give a general and precise idea of the framework for comparison
with the other analysed EUD tools.
We analyse the features listed in Table 1 with reference to the
above problem statement and highlight the difficulties that end
users encounter using each tool to solve the problem:
• Yahoo! tools offer very good support for managing RSS feeds. In
the example, the user wouldhavetocreateanoperatortoconvert
his personal organizer into a properly labelled RSS feed where
each personal organizer event would be an RSS item. The part of
the statement regarding the invocation of web service searches
is the most troublesome. The user would have to create an operator to iterate through and decompose items and then send the
right part ofthe itemtoa web service withaYahoo! Pipes API ina
HTTP request header (for example, the destination shouldbe sent
separately to the Google Maps API, and the date and destination
to the flight search engine, etc.). When a data item is useful for
invoking more than one service, the user will havetousean additional operatortoreplicate this data item. The user would have to
enact the process bottom up, which would be quite demanding
because he would have to be familiar with XML and know something about MIME codes and the use ofvariables (string, integers,
• iGoogle provides a simple mechanism for creating a mashup of
several widgets, like a map, a flight search engine, a travel information search engine, etc. The problem is that the user will have
to enter the input data for each widget manually at run time,
as the platform regards the widgets as separate elements which
are not easy to link at design time. In order to connect them up
to form an integrated information system, the user would have
to create a publication/subscription channel for each data type
withan element actingasmessage-oriented middleware (MOM).
This element has to be parameterized, the form of the message
has to be defined (in JMI format) and a Java API has to be managed. There are very comprehensive tutorials, but an aptitude
for object-oriented programming and an understanding of this
programming paradigm will be required.
• PopFlyisusefulfor creatingavisual mashupofpersonal organizer
support tools, and will offer compatibility with Outlook and ISS
management systems. The problem is that it offers a functional
abstraction for services invocation and the user will have to have
an aptitude for recursive calls. For example, if hedecides to search
for flights to a destination offered by more than one company, he
will have to program a flight search function (for which there
is plenty of documentation) and prepare a time variable to be
incorporated into the calls to save the best price found to date
and the nameofthe company offering this price. Additionally, the
user will have to check manually (or by trial and error) that the
invocations satisfy a set of preconditions defined in pseudocode.
Todothis, the user will requireanunderstandingofcomplex data
types (vectors, hash tables, DTDs, etc.).
• OpenKapow is based on screen scraping and ports part of the target code offered by the portal in HTML div tags. The user will
have to substitute any server-side code (php, servlets, etc.) used
by the service for pseudocode in the form of an activity or flow
diagram. In order to send the destination stored in his personal
organizer to several web portals (map, flight, hotel and information search engines), for example, the user will have to codify a
dataflowbetween whatOpenKapowterms“robots”andprogram
those robots: he will have to access data from forms with PHPlike code and inspect and use the names of eachfield, understand
their data types, etc. The tool offers support for these tasks, but
this task is likely to be unintelligible for a non-programmer user.
• Apple Dashboards uses operating system tools, like document
viewers, agenda, contacts and shell commands. In order to solve
the problem, the user will have to access his personal organizer
appointment data and prepare the invocation using curl in order
tosend HTTPrequeststoremote resources and displaythe mapor
invoke a service. This command will require knowledge of HTTP
verbs and the parameters for preparing headers, as well as the
implementation of control flows based on functional diagramlike charts. This can be very hard for non-programmers to do
insofar as their needs are completely unrelated to the examples
explained in the tool tutorials.
The main problem with the above tools is that they do not
have a compositional model suited for end users (Assmann, 2003).
Their weak points are that they do not provide help for discovering composable services or user-centred mechanisms for invoking
services. Also service orchestration is troublesome, and there are
user interface and presentation logic problems. The framework
presented here uses Web 2.0- and EUD-based ideas to exploit the
strengths of existing tools and solve the above problems and weaknesses in order to provide a satisfactory response to problems like
the one stated in the example. The strength of this framework is
that it is composed, as already mentioned, of a component- and
connector-based development model, port type-driven composition techniques that guide users through the selection, definition
and organization of components and connectors, and a visual language that is useful for solving the problem. In the following
sections, we detail work related with each of these three parts.
2.2. User-centred component models
Building block models and their relationships (Myers, 1990)
have been successfully used for many different purposes (e.g., programming, user interaction and visualization) (Pautasso, 2004).
They attempt to provide an effective, graphical, non-linear representation that has been successfully applied to modelling (e.g.,
UML),parallel computing, laboratory simulation,image processing,
workflow description, hypertext design, and even object-oriented
programming. We regard software composition as a potentially
good application domainforagraph-based, visual notation. Instead
of focusing on typical composition issues like how the “spatial”
architecture of a software system can be specified in terms of
components and connectors, we describe how services should be
composed in “time” (Govindaraju et al., 2003). Apart from describing the dataflow structure of the interaction between different
services, we have also included a separate description of their control flow dependencies in the component model, an idea already
reported elsewhere (Pautasso and Alonso, 2003; Fukunaga et al.,
2.3. User-centred composition techniques
As regards data-driven composition techniques, we have analysed existing tools and looked at how end users go about solving
a problem. There are many studies analysing how users expect to
use and build theirownapplications, but therearenostudies investigating which composition technique most closely emulates how
non-programmer users think.
There are not many composition techniques that do the job that
we consider to be necessary in the EUD field. Spreadsheet formula techniques for applying off-the-shelf functions by entering
cells with the right input types are perhaps the closest example. However, as far as we know, there are no techniques capable
of generating the compositional process that an end user should
Fig. 2. FAST component model.
enact tobuild a web application from requirements by establishing
dataflows among components published in a catalogue.
As users do not know how to program a solution, the development techniques have to be based on existing elements, which
users will see as black boxes that serve a particular purpose. Additionally, users will be able to state their objective but will not know
how to achieve their goal so they will not be able to use imperative techniques to compose these elements. Therefore, users will
have to use a declarative technique detailing what they expect to
achieve and the screen and dataflow that they expect to take place.
This is equivalent to what users would do if they had to access each
service, resource or portal.
2.4. User-centred visual composition languages
As far as visual languages are concerned, many graphical formalisms have in the past also been developed in this area. Some
contributions that have been applied to workflow modelling, such
as state charts and Petri Nets, are formalisms that have a natural
order of services invocation.
A limitation of a control flow-based visual language applied to
service composition, however, is that there is no visual notation
for specifying adaptations between mismatching service interfaces
(Aragao and Fernandes, 2003).We have taken adesign-by-contract
approach whereby input and output port types are used to drive
composition based on the target and source data. This solution
overcomes this weakness (Lizcano et al., 2008).
In conclusion, the proposed FAST framework draws on previous
research to create a compositional model based on the above three
elementsinordertohelp non-programmer userstobuild theirown
composite web applications. In the following, we present the component model, composition techniques and languages formulated
in our research.
3. FAST component model
The FAST component model defines the main elements
that users use to develop their solutions: building blocks,
pre/postconditions which building blocks use to interact with each
other, and connectors that are used to create either a dataflow
among components or an execution flow among visual elements
subject to their pre/postconditions. Fig. 2 shows the FAST components and their relationships, which are detailed in the following.
3.1. Building block
FAST building blocks are visual elements that abstract any part
of a composite application. They are perceived by users as black
boxes that process inputs to produce outputs. All end users know
is what inputs they need and what outputs they generate. They do
not have to program their operation which is pre-programmed or
established by the finer-grained building blocks of which they are
composed. These elements are published in catalogues to which
users have access at three levels of abstraction. FAST includes four
element types: screenflows, screens, forms and resources, which
can be operators (for modifying the internal screen dataflow) or
wrapped back-end services (service wrappers and low-level web
resources for adapting services to the visual framework). FAST
makes all these elements available for users who apply a top-down
approach to develop a new solution. First, it displays ready-made
havedeveloped and publishedtheirsolution for usebyotherusers).
These screenflows can often solve all or a large part of use cases. If
none of the screenflows match their needs, users can create their
own based on off-the-shelf screens that they connect in an execution flow using connectors based on the pre/postconditions of the
elements to be connected, as explained below. These screens are
catalogued in the same manner as the screenflows. If users cannot
find the screen they require,they canbuilditfrom atomic resources
that appear at the third level of abstraction of the catalogue. Unless
these resources already exist, end users will be unable to solve the
problem, because they are incapable of doing what for programmers would be the rather straightforward task of building these
Eachbuilding block typeisdetailedbelowinthe top-downorder
in whichthey willbetackledbyend usersinadevelopment process
using FAST.
3.1.1. Screenflow
A screenflow is a meaningful aggregation of screens endowed
with business logic. Business logic comes from the combination
of each screen’s inner logic plus the composition logic. For users,
they are a flow of visual elements, like forms, which can be used to
carry out a functionality, like select and purchase a product from a
supplier. This functionality involves visualizing the available items
on screen, selecting the item, visualizing item data, adding the item
to a shopping cart and paying for the item.
Following on with the running example, an end user will first
to book a hotel, a screenflow to display tourist information about
the destination, etc. If these screenflows are already available in
the catalogue, all the user has to do is add the screenflow to his
or her design, prepare their data inputs (using connectors) to satisfy their preconditions and analyse their postconditions in order
to use the final outputs produced by the screenflow, if necessary.
This applies to the screenflow for booking airline tickets, already
availablein many catalogues from suppliers like Expedia, see Fig. 3.
Fig. 3. Example of off-the-shelf screenflow to book a flight. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)
These off-the-shelf components make the end user’s job easier
and are described in natural language in a catalogue, specifying
their inputs, outputs and functionality. At run time, they provide a
screenflow withinacanvas positioned and sizedtothe users’ liking.
At design time, users manage the screenflow by means of a visual
icon, as illustrated at the top of Fig. 3, which colour codes screenflow reachability (whetherornot its inputs have been connected to
a data source with the specified data types), and the expected input
types and output types.Thescreenflowintheexamplerequires two
“location” (L) data types and two “date” (D) data types.
If users cannot find the screenflow that they need, they have
to define the prototype building block that they require (required
input types, expected output types), which they will build based
on screens lower down in the catalogue. FAST will suggest screens
that manage the data types defined in the prototype to expedite
the keyword search of the catalogue for screens. In order to create a new screenflow, users will have to search for its constituent
screens, create a dataflow between the screens and set up an execution flow in order to create the transitions between the visual
elements on screen. Fig. 4 shows the creation of the screenflow
for booking a hotel based on its constituent screens: initial search
(product search), screen with details (product details) if the user
selects a particular hotel from the list (product list), purchase order
and booking confirmation.
The input and output types of these screens are connected by
means of dataflow connectors and have an execution order based
on the execution flow connectors used. The execution order is not
necessarily related to the dataflow between screens. In the example, the end user has almost fully defined the data and execution
Fig. 4. Example of end-user designed screenflow to book a hotel. (For interpretation of the references to colour in the text, the reader is referred to the web version of this
Fig. 5. Example of off-the-shelf screen.
The approach to screen integration in a screenflow is driven by
pre/postconditions. Both input and output port types are used as constraints to drive the dataflow between screens and the transitions
during screenflow execution. This technique will be explained in
Section 4.
3.1.2. Screen
Screens are probably the most important component of our
visual composition model. They are the smallest visual functional
blocks that canbeexecuted independently. They include both business logic and graphical user interfaces interconnected with each
other by dataflow connectors. Screens have a pre/postconditionbased visual interface. This interface will play a key role in their
composition to create screenflows, as discussed in the previous section. Bearing these constraints in mind, screens can be created in
two different ways: (a) by linking several resources, operators and
a form together in compliance with the FAST composition technique,or(b)bydevelopingamonolithic andadhoc pieceofcode on
the condition that it conforms to the screen interface. Only option
(a) is open end users, whereas software suppliers have two alternatives ((a) and (b)) for developing a screen for publication in the
EUD catalogue.
Screens can be standalone or part of a larger screenflow. In the
running example, the Google Map is a screen that is published in
the catalogue and has standalone functionality.This screen displays
a standard map of a location (input as a text string) (see Fig. 5). It
can, if necessary, also extract the location of a point identified by a
mouse click or by entering an address for use by other screens.
Usersmayoftenneedtouse screensthat are notinthecatalogue
tobuildascreenflow.Insuch cases, FASThasascreendesigntool for
building screens from their constituent building blocks: a form and
resources (operators and wrapped services) to create the required
functionality. In the running example, users have to create a screen
to search and list hotels provided by a web service in response to a
search criterion based on the destination and dates specified on a
form filled on an earlier screen. This screen is called product list in
Fig. 4.This screen will be part of the screenflow necessary for booking ahotel. If itdoes not exist,itwill have to bedesigned basedon its
constituent resources: a form that lists the results, a wrapped web
Fig. 6. Example of end-user design of the Product List screen.
service for searching the target agency, and the necessary operators, for example, an operator to sort the results by price. Fig. 6
shows how this screen would be designed using FAST.
Two remote resources, wrapped web services for searching
hotels on Expedia.com and Bookit.com, are used in Fig. 6. The input
of both services is a search criterion that is provided by the product search screen. The above criterion is a hotel location and an
arrival date. These data types are used as input for the searches in
both agencies, and the lists (“PL”, product list) are merged using a
binary operator called merge. This operator can also be parameterized to create a merged list sorted by price, for example. This list is
connected to a form, which is used to select a particular item, producing a data type called item (“IT”) for use on later screens. Each
input/output port typeislinkedbydataflow connectors. These connectors and the input and output port types are explained later in
this section.
3.1.3. Form
A form can be seen as a generic graphical user interface acting as
a service front end. It is responsible for establishing visual communication with the end user at run time. In our proposed component
model, forms contain both view and presentation logic (i.e., event
management or rendering operations). The forms can perform certain actions. Each action has a set of preconditions. In response
to user-triggered events (like a click on a list item, submit, etc.),
the forms output particular data types described as form postconditions. Forms are considered as black-box components and can
be developed in any (web) technology. Note, however, that they
should be designed as generically as possible to promote reusability acrossdifferentapplicationdomains.FASTincludesawiderange
of these generic forms.
Rememberthat the forms willbetheinterface that end userswill
see and interact with,anditisimportanttooffer the best user experience. But this is not easy to do with just a generic interface. This is
where component parameterization comes in. Parameterization is
useful for delivering customized forms (i.e., enabling internationalization or tailoring general-purpose interfaces to specific domains,
etc.). In some cases, a customized generic form will not meet user
requirements either. To solve this problem, the model supports
domain-specific forms.
An example of a form for the running example would be a table
that lists a set of items offered by a remote web resource enabling
the user to select an item and output a dataflow with the information on this item. This is the case of the form displayed on the
Product List screen in Fig. 4.
3.1.4. Resource
In the context of FAST, web services can be regarded as components that can and should be composed into larger systems
(Papazoglou and Georgakopoulus, 2003). However, web services
are only a particular case of invocable resources for composition.
One of the main advantages of our proposal is that adaptation is not tied to traditional SOAP-based web services. We are
open to all kinds of back-end services, such as databases, legacy
systems and even REST-compliant resources from aROA (resourceoriented architecture Fielding, 2000). Our policy is to wrap all these
back-end elementsasresourcesthatare integrated with other components by means of dataflow connectors. This creates a dataflow
that can solve the business integration problems caused by backend heterogeneity (Anon., 2001). This is a mashup rather than a
SOA composition technology, as it is designed to wrap all types of
resources, including non-dataflow SOA services. In our approach
these resources are converted into dataflow feeds that are easy for
users to use in their mashups.
Bearing this in mind, the component model defines a resource
as the key component required to wrap or adapt services for subsequent composition. On the one hand, resources can be seen as
an abstraction of an invocable method (i.e., a web service-specific
method, a POST method for a POX-RPC service or any other type
of back-end resource conforming to this concept). On the other
output port types using semantic technologies to create dataflows
between components, as explained later.
In ordertowrap any web service orresource for useinFAST, itis
necessarytosetupaURItomanagetheresourceandcreateafac¸ ade,
using Java EE for example, which provides a CRUD API in response
to traditional HTTP verbs (GET, POST, PUT and DELETE) invoked on
the selected URI (see Fig. 7). Additionally, it is necessary to create an XML representation according to an XML Schema defined in
FAST, which provides the pre- and postconditions of the resource
as wrapper metainformation. FAST will send a GET request to the
URI of the resource to get the resource metadata and identify their
inputs and outputs. Each resource functionality willbe represented
as a list of functionalities within the XML template. Each functionality will have a “URI global\ functionality” type URL schema on
which the POSTs whose request header includes the data necessary
for this functionality will be executed. This request must generate
Fig. 7. How to wrap a data source or remote service for inclusion in the EUD.
a response whose body includes the resulting data. The generated
or external web resource (by preparing a SOAP invocation, another
REST call, etc.), and process and prepare the response to this POST.
The fac¸ade will alsoberesponsible for processing anycastings using
the input or output data types.
The hotel search web service is an example of a resource
that launches an internal search and lists the available options in
response to a location and arrival date that the user enters on a
query form. This resource is duplicated in the example illustrated
in Fig.4, astwo resources are used to search hotels on two different
web portals. Other examples of screenflow resources necessary for
booking the hotel are the payment gateway, which manages end
user payment given an item and its price, and the resource that
actually booksthe hotelroomgivenalist item providedbythehotel
search web service and the OK provided by the payment gateway.
3.1.5. Operator
An operator is a subclass of FAST resources. Operators are meant
to transform and/or modify dataflow data according to a process
akin to piping. For example, an operator might be a filter that
removes data items that do not meet a condition from a dataset,
an element that performs a mathematical operation on an input
data list, or an element that merges more than one list into one
or splits one list into more than one depending on a criterion. No
constraints have been placed on operators in FAST, and they are
invoked throughacommon interfaceasiftheyweresimpleadapted
services. FAST defines several instances of general-purpose operators, such as aggregators, filters, selectors or iterators. The designed
interface will also provide the option of extending operators on
In the running example, a “merge” operator, as illustrated in
Fig. 6, can be used to merge the result of the items output by
searching hotels offered by several agencies.
3.2. Pre/postconditions
Scientifically speaking, a pre/postcondition is an objective and
verifiable observation about a matter. From the FAST standpoint,
conditions are relevant assertions characterizing an instance of a
domain concept, that is, a condition met by a specific data item.
For example, an input is of a certain type has a specific length, is
not empty or null, is in a repository known to the resource, is an
instance/subsumption of an ontological concept or has a particular syntax. Until users enter the data at run time, there is no way
of knowing whether or not particular preconditions hold, that is,
whether or not the data item has particular characteristics.
Examples of preconditions are “the input data item is a card
number”, “a Google Map location”, “an integer”, “the ‘card number’
text field is not empty”, “the user is correctly logged in”, etc. Examples of postconditions are “the output is an Expedia Hotel item”, “a
location”, etc. Each FAST building block has a set of preconditions
and a set of postconditions. They generally inform users about the
needs and functionality of this building block and FAST about how
this component can participate in a dataflow.
Conditions enable assisted semantic compositionatdesign time
to help users to enact processes: the components have input and
output port types, expressed/defined in terms of pre- or postconditions. These conditions can be used to recommend valid
dataflow connections among components that users should check
and implement using adataflow connector. The recommendations,
which are checked using RETE, are explained later.
Input port types specify the set of input types (i.e., data syntax
and characteristics) required for components to execute correctly
according to their preconditions, which must be satisfied to guarantee execution. Examples of input port types for the “the input
is a card number” and “the input is a Google Map location” preconditions are “card number” and “location”. They represent the
expected input data types, whereas the specific data values will
not be available until run time.
Output port types are sets of output types that are produced by
the execution of the component, that is, they are a set of assertions handled at design time about the outputs of the component
at run time. An example of the output port types of a component
that has “the user has logged on to the system” and “the component produces a location on a map” postconditions are “Boolean”
and “location”. Again they are the expected output data types of a
building block, and the specific data value will notbeavailable until
run time.
For example, Figs. 3 and 4 show screenflows with “L” and “D” as
input port types (L is a location and D is a date). In the next section
we will see how input and output port types play an important role
in the FAST composition technique.
At run time, pre/postconditions can be stored in any type of
knowledge base or relational database, which can store the application state.
3.3. Connector
Connectors are elements that are used to integrate different
resources with each other, establish dataflows among resources
or define the user-defined component execution sequence at run
time. There are two types of connectors: dataflow connectors and
execution flow connectors.
Dataflow connectors are elements that are used to specify at
design time which component outputs are directed at the inputs
of another component. These connectors are used to generate a
dataflow graph among components, which is often synthesized by
matching input and output port types via FAST. Users receive recommendations on and decide which connections to set up in their
application, as explained in Section6. There are several connectors of this type in our running example, which, as discussed later,
are depicted as a coloured arrow: dataflow connectors are used in
Fig. 4 to define the internal behaviour of the screenflow for booking hotels. The output of the Product Search screen is used as the
input of the Product List screen. If a user clicks on a listed product,
an item data type is output, which is used as input for the Product
Details screen, etc. The dataflow connectors are also used to create the internal dataflow of a screen, as shown in Fig. 6, where the
inputs defined for this screen (a data type “D”, date, and data type
“L”, location) are redirected to two wrapped web services. The web
service outputs are directed to a binary operator and then passed
to a form that displays the product list in table format, etc.
Execution flow connectors are useful for establishing a temporal execution order between screen resources to generate a
screenflow. As explained later, screen execution usually generates
a set of screen outputs, which match the types described by their
output port types. Any screen that has access to the input data that
it requires to execute (whose types will have been defined by its
input port types) could be the next component to be executed in
the application. Users use these connectors at design time in order
to put together the screenflow.
In our running example, the execution flow connectors for the
screenflow for searching and booking a hotel are illustrated as
dashed directed lines in Fig. 4. The Product Search screen starts
the execution of this screenflow. When this screen has finished
running (the postconditions, product of a user-triggered event at
run time, are satisfied), the Product List screen will execute. When
the user selects an item that would satisfy the postcondition of the
Product List screen, it will stop running, and the canvas will display
the Product Details screen and so on. These types of connectors are
used exclusively to create ascreenflow from its constituent screens
and not to design a particular screen.
There are special connectors for establishing complex iterations
among screens, such as loops. These connectors operate like a loop
with a stop condition. If the stop condition is met, the execution
flow stops and an output is produced. If the condition is not met,
the execution flow will be iterated up to n times until the stop condition is met. Unlike building blocks, these connectors do not have
port type used and the expected value for stopping iteration. We
found, however, that these types of connectors were not very often
used to build the EUD applications developed in our experiments,
because end users generate the iterations by manually performing
operations on the lists of target items.
4. FAST composition technique
The aim of the FAST composition technique is to define how
the components described in Section3 can actually be composed.
They are composed at two levels: by creating screens from existing
resource, form and/or operator building blocks, or by composing
screenflows from existing screens.
In this section we present the FAST composition technique that
handles both processes. FAST includes visual support that tells end
users which components they can connect with each other based
on their input and output types. This visual support is very similar
to the visual aids offered by all the studied tools, and is based on
visually highlighting the port types that can be connected to other
port types previously selected by users. Matching port types are
coloured green, whereas port types that do not match but share
the same data types (string, integer, double, float, array of strings,
and so on) in the internal programming language are coloured yellow. For example, one port type may be a location and another
keyword. They are not the same, so they will not be marked green
to indicate that they are connectable. However, both port types are
stored as a basic type string, and a valid dataflow could be generated if specified by the user. In these cases, port types are coloured
yellow. These visual annotations rely on a pre/postcondition-based
input/output port typing mechanism.
4.1. Input/output port typing mechanism
All FAST building blocks have a common interface whose inputs
which are implemented as a set of input and output data types.
Building blocks use input port types to specify which data types
they require to execute. The composition technique is based
on dataflow connectors that link the output port types of particular components to the input port types of others with a
Fig. 8. Component parameterization and adaptation.
compatible data type. FAST uses pre/postconditions to recommend
valid dataflows among components, and end users use dataflow
connectors to establish these flows at design time. Building blocks
that will not execute properly withoutaparticular data type will be
highlighted at design time until they receive the right type of information via a dataflow connector. Building blocks will not execute
until they have received all the data that they require.
At run time, executing building blocks usually generate output
data of a particular type. The output port types define these types
at design time. In screen composition, the output port types will be
propagated by dataflow connectors, which are used to establish
a dataflow among components. They propagate output port types
(the expected type or data semantics) at design time and the actual
dataatrun timetothe next buildingblockinthe compositionchain.
In screenflow composition, on the other hand, users can use executionflowconnectorstoestablishanexecutionorderamongscreens.
If users fail to define an execution flow at design time or the data
that anyofthe screens requiretoexecute the definedflow are missing, FASThasaninference engine that will listthe screen(s)towhich
the output port type couldbedeliveredinordertoeventually trigger
a screen transition. If this happens, the end user will have to select
the preferred screen from the list.
4.1.1. Parameterization and adaptation mechanism
Both the component model and the input/output port typing
First, components should be parameterized. To do this, a component is instantiated by entering specific internal attribute values
(see Fig. 8a). Then, the component has to be adapted to our execution context by tailoring its interfaces, appearance, etc., to output
the target output port types (see Fig. 8b). Finally, pre/postconditions
are connectedby dataflow connectors, thus enabling gluing, that is,
the establishmentofa valid dataflow between twoormore components (see Fig. 8c). Parameterization and adaptation are supported
at design time by visual aids (similar to the visual support offered
by the other studied EUD tools), which help end users to generate
the targeted outputs from the existing components.
An example of component parameterization and adaptation is
the use of general-purpose forms: basic, general-purpose HTML
forms that can be visually adapted by adding or removing fields,
changing names, expected data types, etc. In our example, we
already mentioned a hotel search screen (Fig. 4). We will take a
general-purpose form for establishing search criteria on a screen
called Product Search. The user will parameterize the form to
include the search fields that they require (search location and
date). Then they will adapt the form output port types, specifying
Search Criteria as the output type. Finally, the Search Criteria will be
glued to several wrapped search services, using a dataflow connector between the Product Search and Product List screens. Another
example of form parameterization isthe Product List screen shown
in Fig. 6, where we can define the visual appearance of the hotel hit
list table. The behaviour, appearance or functionality of almost any
building block can be parameterized. The “merge” operator shown
in Fig. 6 can be parameterized to use a sort criterion to be met by
the output of this operator after having merged several different
data sources.
It is the building block’s creators that define the possible parameterizations, which are generally confined to visual appearance,
data listing criteria, options for changing the effect of an input data
item on the internal behaviour of a component (for example, a filter that outputs items including aninput keyword or, alternatively,
removes the items containing that keyword), etc. Visual descriptions of how this parameter affects the building block are offered
to end users, with a default value that the user can alter by means
of a selection from a pull-down list or a text field entry.
4.2. Screen composition
The bottom level of the FAST composition technique refers to
the composition of existing building blocks to create screens. Fig. 6
shows how a screen for searching hotels can be created using this
type of composition. We are now going to show how to create a
somewhat more complex screen including more elements, namely,
a screen that can gather tourist information (for example, from
the TripAdvisor portal) on a specific destination. This screen, called
Tourist Information List screen is illustrated in Fig. 9. In this case, we
explain generally howtosetupdataflows between several building
blocks in order toillustrate how this composition technique works.
This screen uses a search criterion output by previous screens and
the user profile in order to search a wrapped web service provided
by TripAdvisor for points of interest (POIs). In our example, users
can also refine the product search by logging on and using user
preferences specified in their TripAdvisor profile.
Fig. 9. FAST screen view.
To get the screen composition started, end users usually begin
by selecting a form that meets their needs (1). In this case, the user
selects a form displaying a table listing the POIs for the target destination. Users can select one of the table items and gather further
information about the item (tourist sights, transport hubs, eating
places, etc.). The user uses this form to parameterize the component (e.g.,bychoosing the sort criterion), adapt the component (e.g.,
specifying that the form output is an identifier ofthe selected item)
and create the dataflow with the other components. The selected
form may have to satisfy an input port type (2). In this case, the
input port type is “a list of items with name and description”. The
end user is looking fora back-end resource that has the same output
port type as the above input port type, such as a back-end resource
that receives a “text string as a search criterion” and outputs an
“item list” (3). TripAdvisor provides this resource, and, given a text
string specifying a location, outputs a list of POIs at that location.
Supposethat,apartfromthe nameanddescription,the listresource
(3) has countless other data about each item. Although the user has
not managed to find exactly the right resource, because the component does not generate just a list of names and descriptions, he
has found another resource that generates an output that is quite
like what he is looking for. In this case, he might want to use a filter
(an operator) (4) to adapt the data to the specific input port type.
In the example, the filter should remove all the fields that are neither the item name nor description. Dataflow connectors will link
back-end resources, operators and the form. These connectors will
semantically match data port types to guarantee the validity of the
data and their data type.
Thanks to the input/output port typing mechanism, back-end
resources have their own input port types. These input port types
also have to be satisfied in order to get the screen working. If they
can be satisfied by means of other resource output port types, the
user will connect these resources (5). Imagine that the precondition (F1) for the tourist information screen is a location, entered
by the user as a keyword (6), and that the search criterion is used
to search POIs using the TripAdvisor web service. Imagine a TripAdvisor back-end resource that outputs terms that the user has
already searched depending on his or her profile and preferences.
The two lists can be joined by a concatenation operator to produce
a “a comma-separated text string as a search criterion”, and this
output port type enables the resource to execute (3). Any unresolved input port types within a screen (6) will have to be solved by
executing a previous screen in the application screenflow.
Eveniftheform inputporttypeshavebeensatisfied,the formmay
not, depending on the typeof form that the user has selected (interactiveorotherwise), beabletoexecute (7) without aUIevent. Form
execution should create some output data (like an output port type,
that is, “TripAdvisor item identifier” in this case). Depending on the
screen business logic, the output port type could be propagated to
a back-end resource to validate or manage its data. In this case, a
TripAdvisor resource is accessed to validate the selected data item
(8). Finally, if the execution is error free, the screen output port type
will satisfy the postcondition (9), that is, in this case, “the output is
a TripAdvisor item identifier” (F2).
The above-mentioned visual aids supporting the composition
technique direct and recommend all interconnections among components, and are similar to the visual support offered by existing
EUD tools.
At run time, users draw on their experience to enter specific
inputs. Suppose that the value of the input port type “string as a
keyword”is“New York”, which, after executing thefirst resource,is
converted intoanoutputonthe list “New York City, United States of
America”. This is then supplemented with the user preferences, for
example, “Italian restaurants”, “ICT conferences”, “3*, 4*, 5* hotels”
are added as these are search terms that the user has used recently.
The resulting table lists POIs returned by searching TripAdvisor
for these preferences. If the user selects any listed POI, the screen
output will be “POI TripAdvisor item id”, whose details will be displayed on the next screen of the screenflow. This is very like the
Product Details screen used in the screenflow for booking hotels.
When the port typesofthedifferent building blocksmatch,FAST
will visually highlight the matchingreen, recommending end users
tosetupadataflow betweenthebuilding blocks.Ifthe porttypesdo
not match,butthe datatypes representing theitemsinFAST’sinternal programming language are the same (strings, integers, etc.), the
user will receive a visual cue highlighted in yellow, indicating that
these types could be connected to set up a valid dataflow, even if
the components do not appear to have been catalogued as collaborative. If the basic types do not match, the user will not be able
to set up a dataflow and will have to use some other intermediate
operator to make the data types compatible.
4.3. Screenflow composition
Screenflow composition is the top level of the FAST composition
technique. It generates a fully functional composite application. As
expected, every screen has a set of attached input and output port
types that will be used to drive the transition from one screen to
another through a set of output port types during screenflow execution. This way, a screen has two possible states—reachable and
unreachable. If all the input port types of a screen are satisfied by the
conditions output during the screenflow execution, the screen will
be reachable. Otherwise, it will be unreachable.
End users can explicitly state the target screen execution order,
using execution flow connectors that simply serve to establish the
chronological order of screen execution. If execution flow connectors fail to define the execution order or the order cannot be
followed because it includes unreachable screens, the FAST platform input/output port typing mechanism createsalistofthe screens
that can be rendered at any time (since their preconditions are satisfied). This list is displayed for end users to select which screen
they want to execute next.
FAST displays a list with all possible execution flows for the
screens that the users are using (depending on their inputs and
outputs). In this way, users can either create a valid flow using
execution connectors or leave it to the system to create a list of
reachable screens from which they manually select the next screen
to be executed.
Thanks to the input/output port typing mechanism, there should
benoobstacletoaddingascreenwhose inputporttypes arematched
by the current data types present in the screenflow.
5. FAST composition languages
To meet all the composition language requirements,we propose
two different representations with different aims:
• FAST Visual Composition Language (FVCL). FVCL is a visual language enabling end users and programmers to intuitively and
productively compose applications.
• FAST Modelling Format (FMF). FMF is a way of defining an application persistence format using markup languages like JSON or
XML. It is used to define the intermediate storage of FAST compositions. This representation is designed for processing and
transmission purposes not for use directly by users.
As composite applications have to be compiled to executablelanguagesfor deploymentondifferent execution platforms,
another execution language isrequiredontopof FVCL and FMF. This
language is described in Section6.
g) Satisfied and unsatisfied pre/postconditions
h) Form
i) Dataflow connector
Fig. 10. Visual representation of FAST components. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)
The composite applications design process will include a set of
model transformations (Sendall a n d Kozaczynski, 2003) from t h e
visual l a n g u a g e t o t h e e x e c u t i o n l a n g u a g e . H o w e v e r , t h e s e t r a n s f o r m a t i o n s will b e a u t o m a t e d , a n d e n d users will deal w i t h t h e visual
language (FVCL) only. In t h e following w e detail t h e s e languages
a n d t h e transformations.
5.1. FAST Visual Composition Language (FVCL)
FAST Visual Composition Language is t h e language for visually
composing t h e different FAST c o m p o n e n t s . There a r e lots of visual
languages in t h e literature (Rumbaugh e t al., 2 0 0 4 ; Ceriet al., 2007).
Some even describe h o w services a r e composed (Pautasso a n d
Alonso, 2 0 0 3 ; N e s t l e r e t a l . , 2009).However, t h e s e l a n g u a g e s d o n o t
usually target n o n - p r o g r a m m e r users. W e have developed a n e w
language based o n t h e above composition technique. It h a s b e e n
designed t o b e visually simple t o i m p r o v e learnability for users.
One of t h e m a i n issues w h e n defining visual languages is h o w
t o describe t h e t y p e of representations t h a t t h e language uses. This
language deals w i t h what is t o b e represented, how it is t o b e r e p resented, a n d h o w t o associate t h e representation w i t h w h a t it
r e p r e s e n t s (Narayanan a n d Hbscher, 1997).
5.1.1. Visual representation of FAST components
In t h e following, w e describe h o w FVCL r e p r e s e n t s t h e FAST
c o m p o n e n t m o d e l a n d explain t h e graphics used.
Screen. During screenflow design, screens a r e r e p r e s e n t e d as
r o u n d e d corner boxes. They a r e divided into t h r e e areas as illust r a t e d in Fig. 1 0 . There is a caption a t t h e t o p . Input a n d output
port types a r e b o t h e n t e r e d in separate areas, IN area a n d OUT
area, respectively. Reachable a n d unreachable screens a r e coloured
green (Fig. 10a) a n d r e d (Fig. 10b), respectively.
If w e a r e composing other FAST c o m p o n e n t s t o create a screen,
t h e screen view will b e very similar t o t h e illustration s h o w n in
Fig. 9.
R e s o u r c e s . Resources a r e r e p r e s e n t e d in very m u c h t h e s a m e
w a y a s screens. In fact, b o t h c o m p o n e n t s h a v e a caption a n d input
a n d output port type areas. However, t h e layout is different as illust r a t e d in Fig. 10d a n d e. As for screens, resource colour d e p e n d s o n
their reachability.
O p e r a t o r s . Fig. 10f illustrates t h e visual syntax for a binary opera t o r . A s s h o w n , operators a r e r e p r e s e n t e d a s a d i a m o n d divided into
t w o halves. The t o p half contains output port types, w h e r e a s t h e
b o t t o m part contains t h e set of input port types.
P r e / p o s t c o n d i t i o n s . The visual syntax of a pre/postcondition is
a small circle (see Fig. 10g). The circle will contain either t h e initial letter of its associated concept or a m o r e elaborate acronym.
For preconditions, a solid (and green) circle m e a n s t h a t t h e p r e condition is satisfied, w h e r e a s a (red) outline indicates t h a t t h e
precondition is n o t satisfied. A precondition is satisfied by using
dataflow connectors t h a t supply t h e building block w i t h t h e d a t a
types necessary for its execution. For postconditions, a (red) outline
indicates t h a t t h e building block still h a s unsatisfied preconditions
a n d is, therefore, unable t o execute a n d m e e t its postcondition.
W h e n all t h e preconditions of a building block a r e satisfied, this
building block can execute, a n d its postcondition is also r e p r e s e n t e d as a solid (and green) circle.
F o r m s . Forms a r e n e e d e d t o create screens. As illustrated in
Fig. 10h, t h e y a r e r e p r e s e n t e d a s a rectangle w h o s e background
s h o w s a t h u m b n a i l of t h e associated user interface a n d s o m e input
a n d output port types.
E x e c u t i o n flow c o n n e c t o r s . At screenflow composition level,
FVCL defines t h r e e execution flow connectors just in case t h e user
w a n t s t o set t h e first or last screen of t h e screenflow or fix a t r a n sition b e t w e e n t w o particular screens. Fig. 10c illustrates b o t h t h e
start, e n d a n d transition connector symbols. The execution flow
connectors a r e u s e d t o define t h e chronological order in which t h e
screenflow screens a r e executed, as if it w e r e a n activity diagram.
Dataflow c o n n e c t o r . At screen composition level w e h a v e a
connector t o create dataflows a m o n g c o m p o n e n t s called dataflow
connector. A dataflow connector is r e p r e s e n t e d by a single a r r o w
as s h o w n in Fig. 10i. It is t h e s e connectors t h a t drive c o m p o n e n t
execution d e p e n d i n g o n t h e dataflow g e n e r a t e d in t h e composite
5.1.2. FVCL views
Although all composition information could b e displayed in
o n e diagram, it w o u l d n o t b e a t all user friendly (Shneiderman,
2003). W i t h o u t special sectional diagrams, called views, focusing
o n smaller p a r t s of t h e composite application, users w o u l d b e able
Fig. 12. Screenshot of the FAST tool’s screen design view.
Fig. 1 1 . Screenshot of the FAST tool’s screenflow design.
to understand only very straightforward composite applications.
Theseviewsshoulddealwith cohesive ratherthanarbitrarysubsets
of the application that are coupled with the rest of the application
as loosely as possible.
FVCL offersseveralviewsdependingonwhethertheuseriscomposing at screenflow or screen level. These views are illustrated in
the screenshots for the running example of a user developing an
application to search travel agency web sites for travel options.
Screenflow view. This view shows the screens that are part of
the composite application under development and the execution
flow connectors that decide their execution order. Fig. 11 shows
the compositional framework generating a screenflow called Hotel
Lookup. It depicts a top-down design process where the user has
created a screenflow to show details of hotels supplied by several
travel agency services. It includes the screen view described below.
The user uses Product Search to enter the location and date he is
looking for, Product List to list hotel search results, Product Details
to show details of any item selected by the user from the list, and
Suggestion List to provide suggestions possibly of interest to the
user based on his preferences (for this purpose, the user will first
have to have logged in).
Screen input and output port types define an implicit screenflow
that is constrained by execution flow connectors. For instance, a
begin symbol attached to a login screen will guarantee that this is
the first screen to be executed.
Screen view. In the screen view, the main area is partitioned
into five different regions, as shown in Fig. 12 illustrating the visual
composition of the screen listing hotels for a particular location and
date (L and D):
• Input port type area (1). This area is on the left and contains
preconditions modelling the input port types for the illustrated
screen. In this case, the port types are L and D, which means the
precondition “string as location” and “string as date”.
• Output port type area (2). This area is on the right and contains
the postconditions satisfied during the execution of the component. In this case, the output port type is IT, which means the
postcondition “hotel catalogue item information”.
• Form area (3). This area, located at the top of the main area must
contain exactlyone form. Thiscomponent providesthe user interface for the whole screen, and is therefore mandatory.Inthis case,
the form is a table listing products. This form is adaptable and
parameterizable for use on this screen. The form input port type
is set to PL “the input is a string containing a list of products with
name and description”. The form output port type is set to IT, D
and L “the output is a hotel item identifier, a string as location
and a string as date”. The results will be listed according to the
preferences that users use to parameterize the form.
• Operators area (4). This area contains data operators, such as filters applicable to lists, operators for merging items provided by
different sources to set up a single list containing the merged
items, operators for ordering a list according to a criterion, arithmetic operators, etc. There is only one operator in the example
used to merge the results from two different travel agencies and
create a common product list.
• Resources area (5). Located at the bottom of the main area, the
resources area stores one or more wrapped resources providing uniform access to business back-end services, web services
and remote resources, etc. They will be invoked when their input
port types are satisfied. As a result, their output port types can
propagate through dataflow connectors, eventually triggering
additional invocations. In this case, wrapped web resources from
two different travel agencies are used. The resource input should
bealocation andanarrival date, and its output shouldbea“string
with a list of products with name and description”.
5.1.3. Visual scaling
Additional auxiliary views are provided for the sake of visual
scaling.Oneillustrative exampleiswhenaninputoroutputporttype
area of a screen contains too many port types. There is no limit on
how many port types a screen can contain. Due to size constraints,
the port types can be stacked and displayed for users by means of
a menu. This is illustrated in Fig. 13.
Other possible views are a screen properties table, input/output
port type inspector and description pop-ups. These auxiliary views
are intuitive, self-explanatory and they pop up on demand (are
user-event triggered).
5.2. FAST Modelling Format (FMF)
FVCL is intended for use by human beings through visual representations of the components and connectors making up this
proposal. Thesevisualrepresentationsdonotnecessarily allhaveto
be the same (geometric forms or colours may vary) and they must
be serialized for computer processing. Therefore, the FAST platform
Fig. 14. Screenflow as an executable object.
Fig. 13. Screen representation of many input/output port types.
requires a machine-friendly representation of the building block
created during design. Apart from serializing the visual composition built from components and connectors, the JSON-based FMF
must meet the following requirements.
On the one hand, FMF must be expressive enough to losslessly
representthe component and connectormodel being designed. Not
only must it ensure model persistence but it must also support
social sharing mechanisms, that is, mechanisms for end userstouse
to publish their developments in a catalogue of EUD solutions for a
particulardomain,whichwill eventuallyease theworkofotherend
users tackling the same problem. On the other hand, resulting artefacts must conform to an unambiguous representation capturing
all the modelling information and, at the same time, making provision for their translation into executable objects. Unlike FVCL, the
focus moves from user-friendlinesstocomputability and execution
performance. This raises different design constraints.
Whenever a user visually adds a new component or connector
to a composition or creates a new dataflow with a dataflow connector between two components, this visual manipulation has an
effect on the model that has a counterpart in the FMF representation. Both representations are kept consistent throughout all the
development steps, since there is a true correspondence which the
tool can manage.
Two representations are used because they each serve a different purpose. The FVCL language is a visual language designed to
enable non-programmer users to visually compose a componentand connector-based application generating a dataflow. And the
FMF aims to describe in a markup language (JSON, XML, or other)
the resulting composite application specifiedinFVCL, including the
service infrastructure and technology required to get the application to run on multiple mashup platforms. It separates the runtime
component or application from the visual tool used in the process.
As a result, other users can use or reuse part of the application on
other platforms.
FMF-modelled documents are intendedtorepresent the following groups of information about a FAST component design:
• Non-functional properties. These properties describe the component that is being designed and include component metadata,
such as author, creation date, semantically enriched tags and so
on. Their purpose is to better identify and describe the component, and they are kept separate from the behaviour that is
covered in more detail and more formally in the remainder of the
• Executable components library. Executable components described
inFMF are defined in termsof lower level binary elements. When
a user visually adds a new component or connector, the library is
updated with the URI that identifies the executable component
and its instantiation data accounting for parameterization, if any,
and renders information for further editing.
• Relationships. The document shows how the components relate
to each other. For instance, a dataflow connection will lead to the
creation of new objects in the document describing the related
entities and relationship type.
• Referenced executable objects. Lowest level components are executable binary components that are also described by FMF
documents, where the components library and the relationship
information is replaced by a reference to the actual implementation.
• Input and output port types. The boundaries of the component as a
wholearedefinedinthe formofinput andoutput porttypes specified as a subset of SPARQL expressions (Prud’Hommeaux et al.,
2006), plus a human-readable description. This kind of representation enables semantic-based recommendations during design
and semantic component matching.
FMF documents couldberepresentedinany hierarchical objectoriented syntax, thus a variety of markup languages, such as XML
or RDF, are suitable. However, the JSON object notation (Crockford,
2006) stands out in terms of interoperability and client-server
round-trip communication. For the working draft definition of the
language, see (Reyes et al., 2010).
The FMF representation of a screenflow, plus the linked representation of its components and the executable objects of the
lowest level components, contains all the required information to
build the composite application. The next section discusses this
6. Execution language
On topofthe composition languages,wehaveneedofalanguage
to execute the application definedinFMF. The main objectiveofthe
FAST composition system is to produce an executable composite
application that is able to run on several existing mashup platforms. The complete application built using FAST will be tailored to
(i.e., JavaScript,HTML and CSS). These well-known technologies are
the foundation of the execution language and are commonplace in
web development.
As Fig.14shows,acompiled screenflow, whichisthe basisofthe
composite application, is made up of the following components.
• A rule-based engine. This engine manages the screen execution
flow. It consists of a knowledge base that represents the current
status of an executing screenflow and contains all the instances
of concepts produced at any time, and a set of rules, each representing a screen or a connector between two screens. The
engine relies on the well-known RETE algorithm (Forgy, 1982).
RETE can be used because the input and output port types are
modelled as rules and conditions. Other contract checking mechanisms, such as OCL or JML, could be used, but RETE returns good
performance results. RETEisabletocheck the underlying types of
each port type and build two types of recommendation rules for
the end user: strong recommendations if the port types match,
conditional recommendations if the port types do not match but
the programming language types do. The engine is tailored to the
platform asfollows: the engine checks the typeof each generated
mashup platform data item, notified by its API, and runs the production rules that contain the generated data type in their input
port type to model the application dataflow at run time. These
rules can then lead some components to output new data and
so on. This is achieved by adding a few additional lines of code
to the executable component in the object language run by the
platform in question.
The rule-based engineis largely mashup-platform agnostic but
somekeyandoptional functionalities areseparatedinaplatformdependent module. Depending on the target platform, a suitable
module will be plugged in.
• Platform-dependent library. This library conforms to a predefined API, providing common features such as AJAX calls or
user preference management. Mashup platforms implement the
above functionality in a non-standard fashion, calling for target
platform-specific libraries.
• Library of executable objects. This library compiles all precompiled
code for all the screens and other screenflow building blocks. The
screen compilation process will be described below.
• Run-time metadata. The metadata include all the information
necessarytoinstantiatethe different executable screenflow components andanexecutablerule set usedbythe rule-based engine.
None of the non-functional properties are needed at this stage,
Rules are independent of the full-blown semantic reasoner, as
they include static mediation information.
On the other hand, screen compilation takes an FMF-based
screen document and produces the executable component for integration into a library of executable objects. To do this, the following
components must be compiled:
• Interface characterization. This metainformation is relevant and
will be exploited during screenflow compilation and execution.
It states the screen input and output port types.
• Building block run-time dependencies. List of references to executable components needed for screen execution that can be run
through recursively in order to output a transitive dependency
list. The combined list of dependencies is output as part of the
screenflow compilation and comprises the form conveying the
user interface, back-end services providing both data and functionality and operators that transform the data.
• Instantiation metadata. Executable objects to be instantiated at
run time can be appropriately parameterized with user information held as part of the instantiation metadata.
• Dataflow information. A dataflow connection states that the output port of one component is connected to the input port of
another component so that the types are matched at design time
in order to assure a valid dataflow between binary components
at run time. Therefore, the execution of a given executable component is dependent on the reception, along their respective
dataflow connections, of all the inputs in compliance with the
constraints established at design time.
This is equivalent to telling an executable component that cannot run without a given data input when these data are output
by a component to which it is connected by a dataflow. If all
the preconditions of an executable component are satisfied, the
associated action is launched.
The screen compilation process will add a new screen to the
catalogue. This screen can be consumed to create new screenflows.
6.1. Platform independence
The composite application development process ends with an
execution language thatistailorabletodifferent mashupplatforms.
This way, the executable components orapplications built are platformindependent.Mostplatformsonthe marketuse theJavaScript,
HTML and CSS execution languages. So, the executable components are based on pieces of HTML code with JavaScript scripts
that receive inputs, perform functions and generate outputs. All
mashup platforms also have a template containing component
metainformation, stating the required inputs, the required outputs, their types and semantics. The proposed composition process
wraps the components with generic templates based on the details
described in FMF (using the JSON language). Thanks to its features,
metainformation generated by the JSON language used in FMF is
very easy to tailor to a particular template schema proper to a
commercial mashup platform. FAST can now export the composite
application as an EzWeb, Netvibes, iGoogle, Yahoo! Pipes or JackBe
(Lizcano et al., 2008) widget or executable component. Tailoring
the executable component to other platforms with templates that
conform to other XML Schemas merely entails adapting the execution language tag syntaxtoanew naming schema. XSLT (Extensible
Stylesheet Language Transformations) or manual customization of
the generated template are solutions (Kovse and Harder, 2002). As
the visual compositionbythe userusingthe FVCL languageistranslated to the intermediate FMF, there is a full list of components,
connectors,and input and outputporttypes foreachdataflow node.
From these, it is possible to generate a composite application template that is then easily convertible to other templates demanded
by specific platforms to execute or publish components.
7. Evaluation of the composition model
We have evaluated the use of the FAST component- and
connector-based composition framework presented here. The
evaluation demonstrates that our premise of enabling nonprogrammer end users to build their own composite applications
is feasible and true. The FAST composition framework is publicly available at http://conwet.fi.upm.es/fast blog. FAST evaluation
aims to test whether end users find the functionality and performanceofthis framework,whichuses theuser-centred composition
model presented in this paper, satisfactory. To do this, we have run
an experiment that aims to answer the following research questions:
• RQ1: Does FAST enable non-programmer users to create a composite web application to solve a real-world problem?
• RQ2: Does FAST outperform other compositional tools in the formulated scenario?
• RQ3: Does FASTmeetend-userexpectations with respecttofunctionality and performance?
To answer these research questions, we recruited 180 end users
to solve a real problem using EUD tools. The 180 users were
consortium participating in the FP7 FAST project development. Of
over 300 users interested in participating in the experiment, we
selected a sample of 180 users in order to form six groups of 30
users that were unbiased with respect to age, sex, training, professional experience, etc. The statistical studies conducted to validate
thesampleandcheckthatallsix groups wereunbiased are reported
laterinthis section. Inorderto answer RQ2, the sampleof180 users
Table 2
Sample characterization.
End users
Group 1
Group 2
Group 3
Group 4
Group 5
Group 6
Under 20 years
21–34 years
35–49 years
50–64 years
Over 65 years
Educational attainment
Secondary School
Vocational Training
Bachelor’s Degree
Master’s Degree
Experience and previous knowledge
Mashup Platforms
Web Services (SOAP, ESB, BPEL, etc.)
JavaScript, HTML, CSS, AJAX
Java, J2EE
Php, ASP
OO Programming
C, C++, C#
Haskell, Prolog
was divided into six groups: five groups worked on five major EUD
tools (Yahoo! Pipes and Dapper, iGoogle, PopFly, OpenKapow and
Apple Dashboard), and the sixth group used the FAST tool under
evaluation. These tools were selected on the grounds of popularity and widespread use. Accordingly, each group specialized in a
particular tool to solve the stated problem. None of the groups had
tackled the problem before or had any previous experience of the
tool that they were using. This guaranteed the validityofthe results
and the comparability of the results across all groups. The size of
each groupis statistically representative, and normality tests can be
run. In this type of study, it is essential to assure that all six groups
are homogeneous and that the allocation of the 180 individuals to
their respective groups was not biased.
The user sample is shown in Table 2. This table also shows the
division of users into different groups.
Only six of the users have programming skills, and are knowledgeable about mashup tools (in this case, iGoogle). These users
were distributed across the groups to assure that their previous
knowledge did not alter the results of any group.
In order to validate this division of users into different groups,
we ran an analysis of covariance (ANCOVA) using the group to
which each end user was allocated as the study variable and the
user characteristics as the explanatory variables. This study builds
a regression modeltoexplain the study variable with respecttothe
other qualitative and quantitative variables. If the ANCOVA study
were to return a well-fitted regression model, then the division
would not be valid, as the groups would be biased with respect to
the most influential explanatory characteristics in the fitted model.
Table 3 shows the results of the ANCOVA study, which suggest that
the model fit is extremely poor. This validates the selected sample
and its distribution.
Looking at Table 3, we find that the coefficient of determination
R2 is very low (0.015). This suggests that there is a high percentageof variability inthe modelled mean variablesothat gender, age,
educational attainment, employment and previous experience (the
quantitative and qualitative variables for each individual) appear
to explain only 1.5% of the division of users into the six groups.
The other values are due to other unknown variables. The R2 and
adjusted R2 values suggest that the group to which each end user
was allocated is largely (98.5%) independent of user characteristics. The model error values, MSE (mean squared error) and MAPE
(mean absolute percentage error), are very high (well above the
ideal value 0), again suggesting that the model does not precisely
explain the behaviour of the variable under study in the sample.
Additionally, DW (Durbin-Watson statistic) values are not close to
0. This implies that there is no autocorrelation among the qualitative variables. If there were, the study would not be valid. Finally,
Cp (Mallows’ Cp statistic) suggests that the model is able to exactly
explain the group to which only one (see df value in the model)
of the 180 individuals was allocated. We have conducted a Type
I and Type III sum of squares analysis. Type I (sequential) analysis provides an incremental improvement in the sum of squared
errors as each effect is added to the model, and Type III (orthogonal) analysis is able to reduce the sum of squared errors by adding
the term after all other terms have been added to the model. Their
combined use means that we do not have to be concerned about
the order in which the factors were added to the regression model.
Taken together, the model results validate the sample, indicating
that there is no bias related to the qualitative and quantitative variables characterizing the users and their recruitment for the study.
Looking at the Pr>F values of the ANCOVA model, we find that the
characteristic that is most related to the allocation of a user to one
groupor another iseducation (the greatest Pr>F in the study, equal
to 0.477). We examined user education and foundno statistical evidence of a direct correlation between education and division into
The validated sample was analysed as follows. In response
to RQ1 and RQ2, all six groups were asked to solve the same
problem as illustrated in the running example, each using one
of the following tools: Yahoo! Pipes and Dapper, iGoogle, PopFly,
Table 3
ANCOVA study to validate the sample recruitment and division into groups.
Goodness of fit statistics
Sum of weights
Adjusted R2
Analysis of variance
Sum of squares
Corrected total
Mean squares
Computed against model=mean (Y)
Sum of squares
Mean squares
Type I s u m of squares analysis
2. Gender
3. Age
4.1. Education
4.2. Employment
5. Experience and previous knowledge
Type III s u m of squares analysis
2. Gender
3. Age
4.1. Education
4.2. Employment
5. Experience and previous knowledge
OpenKapow, Apple Dashboards and FAST. The problem statement is also described at http://apolo.ls.fi.upm.es/eud/problems
description.pdf (see Problem 0). Each tool provides a different
problem-solving approach as outlined in Section 2.
The requested application requires the use of from 22 to 24
components (including screens, screenflows, forms, connectors,
operators, back-end services, etc.), and their assembly requires
the creation of approximately 20 dataflow connections among
components. The problem was carefully defined to assure that
all six tools under evaluation have all the components, composition and dataflow creation techniques necessary to be able to
solve the problem. Before we conducted the study, we personally solved the problem using each tool to check that the task
was feasible. Additionally, we also set up a catalogue of components, resources and operators for each tool before running
the experiment. These catalogues included all the components
and elements necessary to solve the problem, as well as generalpurpose components that were no use for the problem at
hand. All six catalogues contained around 650 components of
different levels of abstraction. Therefore, this is not a straightforward development. For a full and detailed description of
these six catalogues of components and connectors and the
development processes enacted by the sample of users, see
http://apolo.ls.fi.upm.es/eud/solution development process.pdf.
Once the tools and equivalent component catalogues for each
group had been set up, the end user then received basic training
via video tutorials on the tool that they were to use. Each group
was separately given the same number of training sessions. The
schedule for each group was:
Theory session (four hours): introduction and familiarization with component- and connector-based development and
mashup technology for the respective tool that the group was
to use.
Practical session (four hours): basic practical exercises set for
each user group: Yahoo! Dapper and Pipes, iGoogle, PopFly,
OpenKapow, Apple Dashboards and FAST platforms. Two short
videos (see http://www.youtube.com/watch?v=qFt2LBlxkwU
and http://www.youtube.com/watch?v=dpoRhnF8 1A) were
used to describe and introduce the available components and
the tool in question.
Hands-on-workshop (two hours): each user was asked to solve
the stated problem individually.
The training sessions focused on explaining how use each tool
to solve problems akin to the stated problem, explaining the components to be used and the composition techniques provided by
each tool. Accordingly, the design of the sessions for each tool was
We unintrusively supervised the problem-solving workshop,
analysing times taken, problems encountered, sources of conflict
for users, etc. There was at least one supervisor for every 10 users
at all times in order to provide a qualitative and quantitative analysis of this two-hour workshop. Table 4 shows the statistical data on
how many users managed to build a valid solution that met all the
set requirements in each study group, and how long it took them
to do so.
Of the 30 users in the respective groups only two Yahoo! Pipes
and Dapper users, three iGoogle users, three PopFly users, five
OpenKapow users and six Apple Dashboards users managed to find
a solution. These outcomes contrast with the results for the framework described in this paper, as 17 users achieved the goal using
FAST. This experiment clearly reveals the superiority of FAST compared with the other analysed tools, but even so only 17 out of 30
users completed the task despite having received training before
the trial. This is a clear indication that tools like these are often not
suited for use bynon-programmer end users, anda wizard needs to
be built into the environment in order to provide guidance on the
use of the different composition and development techniques and
help userstobuild softwaretomeet specificrequirements.We consider this to be a significant lesson learned from this investigation,
Table 4
Development time taken using FAST, Yahoo!, iGoogle, Microsoft, Kapow and Apple visual languages.
Yahoo! Pipes and Dapper
OpenKapow and RoboMaker
Apple Dashboards
Mean of time
Std. dev.
Std. error
95% lower b.
95% upper b.
which is discussed as a future line of research at the end of this
These results have been subject to several statistical studies,
such as analysing which qualitative and quantitative variables
describe theuser characteristicsthathavemostimpactonthe study
variable, in this case, a Boolean variable indicating whether or not
the user solved the problem. As an additional explanatory variable,
we included the user group (and therefore the tool they used). An
ANCOVA of these qualitative and quantitative variables suggests
that the only factor that appears to affect the success or failure of
the practical workshop is the tool used. This ANCOVA is reported
at http://apolo.ls.fi.upm.es/eud/eud paradigm evaluation.pdf and
is not reproduced here for reasons of space.
The data show that, statistically speaking, FAST is more successful than the other languages and tools. From the qualitative
analyses and observations that we made during the experiment, we can say that this success is due largely to the levels
of abstraction of the FAST components. They make the job
of analysing a problem and developing a solution much easier because users start off with very high-level components.
These coarse-grained parts do not necessarily have to be defined
at this stage, and users can gradually adapt each component. This
facilitates top-down development, where users can focus exclusively on the components that they know how to develop from the
componentsofthe catalogue that they have consulted. All the components of the other tools have the same level of abstraction, and
users have to use very specific detailed components to perform the
task. This can stump end users who are initially unable to devise
a bottom-up solution from concrete catalogue elements. End users
find it very hard to single out these components from all the other
elements and are easily deterred.
Note that we analysed all the compositions that were shown by
We inspected the composition languages and low-level code developed for each application. We ran white-box tests and found that
applications met the set requirements, and all had similar computational efficiency, response times and robustness, irrespective of
the visual language used. These tests and validation are reported in
Hoyer et al. (2010). In some cases, the compositions had dataflows
that the users had added to solve aspects that were not specified
in the statement or were optional. These dataflows led to longer
response times, albeit not to problems of robustness.
As regards the times taken to develop a solution with each tool,
we found that all groups took from 45 to 67min to solve the problem, except the group using FAST, which took less than 40min on
average. Againwe consider that the useof abstract screens (instead
of low-level elements) at the start of the design helps the user to
make faster progress at the early development stages. As the successful samples for the other tools are very small (the number of
users that achieved the goal are nowhere near 30), we cannot use
ANOVA or any other traditional statistical tool to check whether
the time differences are statistically significant. Therefore, we have
used a valid descriptive technique for small data samples, known
as box plotting, shown in Fig. 15.
Fig. 15 shows that the box plots charting the data for each of the
statistical difference in the development times taken using each
tool. The workload was largest for Apple Dashboards, followed by
iGoogle, PopFly, OpenKapow and finally Yahoo! Dapper and Pipes.
The framework that took the least development time was FAST,
which had a 4-min advantage over the best of the existing tools.
This, together with the fact that FAST substantially increased the
number of users that built a successful solution, is a sign of the
efficiency of the proposed approach.
Apart from examining which users did or did not come up
with a solution, how long it took them and how they did it
(observing their work during the experiment), end users were
surveyed about their impressions, opinions and experience. We
designed the questionnaire illustrated in Table 5, which each
member of each group completed at the end of the experiment, stating their individual opinion and impression of the
EUD tool used by their work group. They rated each question
on a five-point Likert scale indicating user satisfaction with the
respective statement. We conducted a questionnaire consistency
study to assure that the survey was valid. This study is reported
in http://apolo.ls.fi.upm.es/eud/survey justification.pdf and is not
reproduced here for reasons of space. This study justified the use
of each item and replicated questions with different statements to
check whether responses are mediated or contradictory, etc. The
Fig. 15. Development time spent with each language.
Table 5
Main questionnaire questions.
The tool was very easy to use first time round
I would imagine that most people would learn to use this tool quickly
I felt confident using the tool
I didn’t need to do a lot of learning before I could use the tool effectively
The relevant visual components were easy to find
The screenflow of a composite application was easy to model
Inputs and outputs were easy to define
The designed composite applications were easy to publish
The system quickly responded to inputs
The system was stable
I was able to create the relevant screens for the problem statement
The task was easy
mean score for each question on the scale of 1–5 is stated at the
The results reveal that all, even the unsuccessful, users have a
positive and better impression of the FAST system than of all the
other tools on the examined points. End users give very positive
responses to all questions about FAST (which they mostly rated
from four to five points), whereas the other tools were rated worse.
FAST usability was rated positively because participants located
the componentsthat theyneeded and found FAST easytouse. Users
of other toolshad theimpression that theywere notknowledgeable
enough to solve the problem using the provided component model
and techniques, as detailed in the responses to the open questions
analysed later.
From a functionality perspective, respondents also rated FAST
higher than the other tools, although they sometimes found it hard
to find the right screen to use. Additionally, most users were satisfied with input and output port definitions. Then again, users found
the procedure for publishing designed composite applications on
a target platform easy. For the other tools, functionality was rated
slightly negatively. Users stated that the componentsto which they
had access were either useless or technically too sophisticated for
them to use.
Regarding performance, most tools received a positive rating.
FAST was stable without any critical exception throughout the
entire evaluation time frame. Participants also felt at ease with the
FAST terminology. This means that our composition system and its
visual language are both intuitive. No stability failures, execution
errors or similar were reported for the other tools. Users stated
that the solutions were hard to create from the fine-grained components used in the existing EUD approaches. Noteworthy is the
fact that users completed many exercises during the eight hours of
training that each group received, and were therefore acquainted
with the respective tools.
Apart from the questions listed in Table 5, qualitative analyses of each work group were conducted: users were asked
open questions about the problems that they had with the tool
they used, stumbling blocks that they were unable to negotiate,
etc. The responses to these open questions are documented at
http://apolo.ls.fi.upm.es/eud. The conclusions of a detailed analysis
of these open questions are as follows:
• Regarding Yahoo! tools, 88% of the group that used the tool
stated that they had great difficulty interconnecting Yahoo!
Dapper widgets with each other, whereas 76% found it very
hard to compose widgets based on finer-grained components.
Eighty-two per cent of the sample highlighted that, apart from
feeds and screen scraping-based information sources, Yahoo!
Pipes failed to provide useful wrapped services for end users. The
analysis carried out by the project supervisors revealed that the
component inputs and outputs were based on MIME and XSD,
which are completely foreign to users. Therefore, the 28 users
that failed to develop a solution did so because they were unable
tointerconnect resources witheachotherorevenbuildaworking
screen of use for problem solving.
Regarding iGoogle, 90% ofthe group that used the tool stated that
iGoogle widgets produce events which can be consumed by particular slots of other widgets. The problem is that, to do this, it has
need of a basic component which it uses in the background. This
component acts like a data-sharing blackboard, message-based
middleware that has tobeconfigured and parameterizedtomanage the intercommunication. A total of 27 users did not manage
to properly parameterize this MOM in order to interconnect the
widgets that they required to solve the problem.
Regarding PopFly, 90% of the group that used this tool criticized
the fact that they had trouble finding the right elements for this
problem in the catalogues. A total of 27 users were unable to
enact the bottom-up process proposed by PopFly for developing EUD. The tool builds data integration schemas based on the
low-level components, which are necessarily the starting point.
Unless users know which operators to use, the schemas do not
provide the necessary solutions to the problem.
Regarding OpenKapow and RoboMaker (an auxiliary tool supporting OpenKapow), over 80% of the sample found that Kapow
component linking and tailoring mechanisms were not handy
(required programming knowledge), whereas 85% found that
the component search, location, parameterization and recommendation mechanisms were hard to use and understand. The
experiment monitors found that the 25 end users that failed to
solve theproblemhaddifficultywiththe specificationofthe component control flow using simple pseudocode. Although the code
is very basic, users are unfamiliar with loop control structures,
stop conditions, etc., and this was why the 25 users were unable
to find a solution.
Regarding Apple Dashboards, 80% of the group that used the
tool stated that the visual composition interface should not be
confined merely to linking visual elements, as it is not possible
to parameterize or adapt the components to new situations or
problems. Additionally, 75% of the sample found it impossible to
establish the correct dataflow among the different components
for the problem. Because they had to internally modify a visual
component and add other operators and basic components to
alter the component behaviour,24 users were unabletogo ahead
rather than a mere prototype, as they were regarded by the tool.
On this ground, they were unable to redesign the components
using other more basic components.
• Regarding FAST, users were also asked about their impressions
and experiences. All 13 users that did not manage to build a successful solution using FAST referred to the same problem: they
were unable to find certain components in order to generate the
necessary dataflows. Although users received visual guidance to
create a dataflow, the monitors found thatit isvery hard for users
to relate specific requirements to components in the catalogue.
Once they have selected the components, users find it easy to get
on with the design, but this choice should be facilitated by an
analysis wizard.
Analysing these results,itappears that Yahoo! Pipes and Dapper
targets programmers with basic knowledge of web programming,
and non-programmer users will come up against insurmountable
obstacles for creating a dataflow among more complex operators. iGoogle offers specific visual widgets that end users can
relate to given functional requirements, but it is very hard for
non-programmer users to create a dataflow between the widgets because this requires additional parameterization. PopFly
relies on a preliminary bottom-up design which is used to build
a solution based on its constituent components. This approach
has proved to be far from straightforward for non-programmer
users who find that the catalogue contains too many similar components or unfamiliar components that they do not know how
to use (filters, operators, RSS, etc.). OpenKapow tools require
knowledge of algorithms, and, although they use pseudocode,
users need to know what a loop is, what a stop condition
is, what a variable is and how to iterate based on a variable
value. Development using Apple Dashboard is based on iterative
and incremental prototyping. However, the prototypes cannot be
adaptedvisually,andXML and J2EE servletcontainer operations are
required. This is all very abstract and complex knowledge for end
The results show statistically that FAST component- and
connector-based visual composition framework usability, functionality and performance are high. The overall impression is that
end users rate FAST positively as returning satisfactory results, suggesting that the composition system has the potential to enable
non-programmer end users to develop composite web applications. We can conclude that the composition model implemented
in FAST (and which includes the component model, composition
techniques and languages described here) has two advantages over
other existing EUD tools: many more non-programmer users using
FAST can successfully create a solutionto a problem, which they do
faster than the users of other tools.
8. Conclusions, limitations and future trends
In this paper, we present a visual composition framework. End
users with problem expertise but without previous programming
knowledge can use this framework to leverage existing web services and resources in order to build their own composite solutions
to their problems.
Our research has revealed that our approach, implemented in
FAST, is more effective (more users are able to solve their problem)
and more efficient (users solve the problem faster) than existing
tools.Itempowers non-programmer userstobuild theirown applications. Such applications are valuable since they leverage user
domain expertise in a short development time and with low development costs.
Theproposed approachhassome limitationsthatshouldbeconsidered in the future with a view to achieving better results as to
software solution development by end users:
• The creation of this visual development tool is only the first
step towards the solution of a broader problem (Anon., 2003):
how to provide full support to help non-programmer users solve
complex problems requiring the use of existing IoS services.
This toolkit should be supported by a formalized groundwork
to validate and standardize the development environment, its
implementation process and the produced resources (Wulf et al.,
2008). Formalization could lead to a common conceptualization of the development process and composition techniques
(Obrenovic and Gasevic, 2009). Ultimately, this specification is
the first step towards a global standardization process that could
enable, through proper interconnection, the joint management
of resources created by different tools and IDEs, no matter what
their source.
• Another limitation is that although the composite application
built using our approach is platform independent, the full software or mashup generated from this application on a mashup
platform in order to solve a more complex problem cannot be
exportedtoother platforms.Inour example, the composite application for searching and booking of hotels can be used on EzWeb,
Netvibes, iGoogle, etc. But if the user generates the full travel
management application on any of these platforms, the generated RIA will only be executable in the environment for which it
was designed. This is because each platform has a different API
and programming resources and technologies. All manufacturers
set out to sell their own solution by making software built on
their platform incompatible with competitor platforms.
With respectto future work, note thatinour approach end users
use a visual language to compose components and connectors and
build a composite application, but these components and connectors must be previously built and published in the composition
tool. Therefore, the success and range of software solutions that
can be created using the tool will be directly related to the range of
available components and connectors. It can be said that the more
successful this type of composite applications are, the more likely
businesses are to take an interest in feeding their catalogues with
components to gain a market share (Anon., 2009). The more components there are, the more likely users are to make use of this
type of tools. A future line of work is to study how to trigger this
escalation of mutual interests, and how to fill existing catalogues
with more and more components, using an automatic component
Also, we are working on the definition of a taxonomy of forms
and operators. Our goal is to find a set of common visual patterns
present in both the actual web application UIs and the service
front ends. These patterns would be a great seed for building a
repository of visual UI components that could be exploited to tailor any service front end to any requirements through reuse and
This work was partially supported by the European Commission under thefirst callofits Seventh Framework Programme (FAST
STREPProject, grant INFSO-ICT-216048) andbytheEuropeanSocial
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