Get Me Here: Using Verification Tools to Answer Developer
Microsoft Research Technical Report 2014-10
Mike Barnett, Robert DeLine, Akash Lal, Shaz Qadeer
{ mbarnett, rdeline, akashl, qadeer }
While working developers often struggle to answer reachability questions (e.g. How can execution reach this line of code?
How can execution get into this state?), the research community has created analysis and verification technologies whose
purpose is systematic exploration of program execution. In
this paper, we show the feasibility of using verification tools
to create a query engine that automatically answers certain
kinds of reachability questions. For a simple query, a developer invokes the “Get Me Here” command on a line of
code. Our tool uses an SMT-based static analysis to search
for an execution that reaches that line of code. If the line is
reachable, the tool visualizes the trace using a Code Bubbles
representation to show the methods invoked, the lines executed within the methods and the values of variables. The
Get Me Here tool also supports more complex queries where
the user specifies a start point, intermediate points, and an
end point, each of which can specify a predicate over the
program’s state at that point. We evaluate the tool on a
set of three benchmark programs. We compare the performance of the tool with professional developers answering the
same reachability questions. We conclude that the tool has
sufficient accuracy, robustness and performance for future
testing with professional users.
Developers often struggle to answer reachability questions
such as How can execution reach this line of code? or How
can execution get into this state? LaToza and Myers recently documented this problem with a series of studies. In
a lab study, they found half the erroneous changes that participants made during development tasks were due to misunderstandings about reachability. In a survey, they found
82% of 460 professional developers rated reachability questions as at least somewhat difficult. Their observational field
study of 17 developers found 9 of the 10 longest development
tasks primarily involved seeking information about reachability [10]. Hence, looking for answers to reachability questions can be both time-consuming and error-prone. This
study expands the results of previous studies on information seeking. Ko, DeLine, and Venolia found that among
the information needs they observed in a field study, the
ones that were hardest and slowest to satisfy included What
code caused this program state? (61% unsatisfied, up to 21
minutes seeking answers) and In what situations does this
failure occur? (41% unsatisfied, up to 49 minutes) [6]. Similarly, Sillito, Murphy and De Volder’s study of information
needs found developers struggling with such questions as
When during execution is this method called?, Which execution path is being taken in this case?, and Under what
circumstances is this method called or exception thrown? [14]
As developers have struggled to answer reachability questions, the research community has invented many technologies, like symbolic execution and model checking, whose purpose is the systematic exploration of program execution. To
date, these technologies have been typically used for program verification, namely proving that a program P upholds
a property φ (P |= φ) or producing an execution trace showing where the property is violated. Instead, in this paper,
we repurpose existing verification tools as a query engine:
given a program P and an execution query Q, we produce
an execution trace that is consistent with Q.
Our query engine, Get Me Here, is named after the simplest
type of query it supports. As an example of our tool in use,
suppose that a user is inspecting a stack class used to store
operands in a desktop calculator. She wonders whether there
are any circumstances in which the program can attempt to
pop an empty stack. To find out, she invokes the “Get Me
Here” menu item on the line of code that throws an exception
on an empty stack (Figure 1). GetMeHere then responds by
displaying a visualization of the execution trace that ends
in the throw statement (Figure 2).
Our visualization is based on the Code Bubbles paradigm
[3]. Each method call in the trace is shown in its own
code bubble, with arrows showing the method calls, green
text highlighting the executed code and code annotations
showing the values of variables. When the user looks at
the lower right corner of this trace, she sees a call to Pop
where its stack data structure (_dataStack) is empty and
therefore the throw statement is executed. She looks left
to the immediate caller ParseTerm and sees that the stack
has two operands when parsing the modulus operator (case
"%":). However, the code that follows mistakenly pops three
operands. While this example happens to reveal a bug in
Figure 2: Get Me Here displays an execution trace, using a code bubble for each called method, arrows for
method calls, green highlights for executed code, and code annotations to show the values of variables.
the code, GetMeHere is a general query engine that searches
for specified execution traces, whether the user’s motivation
is fault finding, feature localization, or program comprehension.
The main contribution of this paper is the demonstration
that verification techniques, in particular an SMT-based
static analysis, can be used as an online query engine in a
development environment to answer reachability questions.
In Section 2, we compare this approach with existing techniques. In Section 3, we describe the Get Me Here tool in
detail, including the full query language it supports and how
the user can change the modeling of the environment to
trade off accuracy for speed. In Section 4, we evaluate Get
Me Here on a set of benchmark programs. Using a lab study
of 12 professional developers, we demonstrate that Get Me
Here is capable of answering reachability questions that people struggle to answer. We also show that Get Me Here is capable of accurately and robustly answering arbitrary queries
about these benchmarks. Section 5 concludes and describes
a few further ways that verification tools could enhance development environments.
Figure 1: Invoking Get Me Here on a line of code.
Get Me Here is most similar to LaToza and Myers’s Reacher
search tool [11]. Current development environments, like
Microsoft Visual Studio and Eclipse, allow the user to traverse a program’s call graph, either through hypertext navigation commands (e.g., go to definition) or through tool
windows presenting hierarchical displays of caller/callee re-
lationships. A developer can use these features to answer
reachability questions by exploring the call graph one call
at a time. Reacher alleviates this tedium by allowing the
user to issue a search for features, like method calls or field
assignments, that are “upstream” or “downstream” of a given
method. The user can then add search results to a central
call-graph visualization.
Both Reacher and Get Me Here provide automated answers
to reachability questions, but the overall user experience
is different. Reacher’s goal is to allow the user to get the
“big picture” of reachability relationships in the program’s
call graph. Hence, Reacher supports iteratively searching
and browsing to build up a visual graph representation that
shows many relationships simultaneously. In contrast, Get
Me Here’s goal is to allow the user to find a single program execution of interest, by iteratively providing more
constrained queries. These differing goals have implications
in key design choices, including the query languages (search
terms versus trace constraints), the visualizations (call graph
versus execution trace), and the static analysis technology
used (modular interprocedural analysis with summaries versus whole-program, SMT-based analysis).
Get Me Here can also be described as an interrogative debugging tool, in the style of Ko and Myers’s WhyLine [7].
The WhyLine and Get Me Here, however, provide dual functionality. With the WhyLine, the user exhibits an execution
of interest by running the program and providing the necessary input. Then, given this concrete execution trace, a
user asks a why or why not query, and the tool automatically shows the relevant parts of the code to demonstrate (or
contradict) the user’s query. In contrast, with Get Me Here,
the user forms a query about parts of the code, and the tool
automatically generates an execution trace, including any
relevant input necessary to reach those parts of the code.
GetMeHere has the same motivation as program slicing [16],
namely to use static analysis to reduce a program’s complexity to aid program comprehension. The main difference is
that a static slicing technique produces a simpler program
that shares a well-defined set of behaviors with the original
program; whereas, Get Me Here focuses on a single program
behavior (execution trace). Focusing on a single behavior
allows Get Me Here to present more details, for example, the
values of program variables, at the cost of possibly excluding
behaviors of interest to the user. (We discuss the iterative
use of Get Me Here to focus on behaviors of interest in Section 3.3.) Get Me Here has less in common with dynamic
slicing techniques [15] and fault localization algorithms like
Delta Debugging [17], which start with the assumption that
the user has a set of interesting execution traces (for example, those that pass and fail tests) and wants to use them to
find relevant code. Get Me Here addresses the dual problem
of synthesizing execution traces based on parts of the code
that are of interest.
GetMeHere is implemented as an extension to Microsoft Visual Studio and uses several existing verification technologies
(Figure 3). It is implemented only for .NET languages, i.e.,
object-oriented managed languages that compile to MSIL
(byte-code) such as C# and Visual Basic. First we describe
the back end that answers the user’s query (the downward
arrows in the figure). The final step is submitting a firstorder formula to an SMT solver, Z3 [4]. Then we explain
how the results are presented to the user (the upward arrows). Finally, we explore further details about more advanced queries and how environment models are built.
Query Engine
Get Me Here uses a whole-program analysis, so it runs as a
post-build step after the user compiles their program. Each
compilation results in a set of .NET assemblies which contain
the program’s metadata and bytecode. We use a byte-code
translator (BCT) [2] to translate the assemblies into an intermediate language called Boogie [1, 12, 13]. The use of an
intermediate language makes the translation easier than directly producing the input language of the underlying theorem prover. In addition, there are many tools, including our
assertion injector (described below), that use Boogie’s object model to effect Boogie-to-Boogie transformations. Boogie is intended for verification rather than execution. It is
not object-oriented. Instead of classes containing (instance)
methods, a Boogie program comprises a set of global procedures where the implicit instance reference (“this”) becomes
an explicit parameter. The type system is very simple, encompassing boolean and integer values and maps (arrays
with an arbitrarily-typed domain). Each procedure consists
of the usual kinds of program constructs: assignment statements, conditionals, and loops. Everything else is encoded
as user-defined datatypes, functions, and axioms. BCT encodes source-language constructs such as dynamic dispatch,
events, and delegates as well as a model of the heap and the
object type hierarchy.
Whenever the user builds the program, Get Me Here runs
a post-build step that uses a byte-code translator (BCT)
to translate the program’s compiled .NET assemblies into
an intermediate language called Boogie [1, 12, 13]. Boogie
is intended for verification rather than execution and relies
on constructs like assert, assume and nondeterminism for
explicitly modeling source-language constructs like dynamic
dispatch, events, and control-flow constructs.
The Boogie program is then modified each time the user issues a query. The Assertion Injector produces a new Boogie
program containing a compiled version of the query. For
a query with a single, condition-less end point, Assertion
Injector places the statement
assert false;
at the line of code the user wants to reach. (We discuss more
complex queries in section 3.3.) All previously existing assertions are turned into assumptions. An assumption places
a logical constraint on a program execution: the theorem
prover adds it to the list of facts that hold on the trace.
Contradictory assumptions prune the search space by making a particular execution trace infeasible.
We illustrate the process with the synthetic fragment of code
shown in Figure 4. The example is meant to illustrate several things. The method Read, which reads a character from
the console and returns it or −1 if there is nothing to be
read, is part of the .NET Framework: it is a library method
that is not considered part of the user’s program. Trans-
Get Me Here
Assertion Injector
anon0 :
call $tmp0 := System . Console . Read () ;
C . x [ obj ] := $tmp0 ;
call $tmp1 := System . Console . Read () ;
y := $tmp1 ;
C . x [ obj ] := C . x [ obj ] + 1;
goto anon15_Then , anon15_Else ;
Figure 3: Get Me Here architecture
anon15_Then :
assume C . x [ obj ] > y ;
goto anon16_Then , anon16_Else ;
anon16_Then :
assume C . x [ obj ] == 10;
assert false ;
call System . Console . WriteLine (
$string_literal_0 ) ;
goto anon17_Then , anon17_Else ;
obj . x = Console . Read () ;
var y = Console . Read () ;
this . x ++;
if ( obj . x > y ) {
if ( obj . x == 10)
Console . Writeline ( " Get Me Here ! " ) ;
// ... more code ...
Figure 4: A small fragment of C# code.
lating the entire framework into Boogie is both impractical
and unreasonable: we want to explore the user’s program
and not get lost in the intricacies of the Framework’s implementation. In terms of the analysis, this means that the
value returned from the method is an arbitrary integer (since
that is the type of the return value). If that is insufficient,
then the user may write a stub, an environment model, that
gives the semantics of the method call. This is explained in
further detail in Section 3.4.
The Boogie program produced for the example is shown in
Figure 5. Boogie does support high-level source constructs
such as conditionals and loops, but we generate instead the
de-sugared form that uses just assignment, procedure call,
and goto statements. The control flow induced by the conditional statements is encoded using assume statements; Boogie’s goto statements are non-deterministic. They take a list
of labels and may branch to any of the target blocks. Fields
are represented as maps whose domain are object references.
Their range is the type of the field. So the value of the map
C.x is some integer for any object, even if its type is not
C. This is of course impractical when actually executing
a program, but does not cause any problems for the theorem prover when it is reasoning about the program. We do
not reason about the contents of strings; string literals are
encoded as unique constants whose value is unknown.
The Boogie program containing the compiled query is then
anon16_Else :
assume C . x [ obj ] != 10;
goto anon17_Else ;
anon17_Else :
// ... more code ...
Figure 5: A simplified view of the Boogie program
resulting from the compiled C# code. The encoding
of exceptional control paths has been elided, as well
as the bookkeeping used to map Boogie source lines
back to source lines in the original program. Line
15 shows the assertion that would be injected if the
user had asked Get Me Here to reach the call to
∃a, b
: C.x0 [obj] = a ∧ y0 = b ∧
C.x1 [obj] = C.x0 [obj] + 1 ∧
C.x1 [obj] > y0 ∧ x1 = 10
Figure 6: A simplified version of the resulting Z3
fomula. It is not written in Z3’s input language.
given to Corral [8, 9]. Corral is a whole-program analyzer
for Boogie programs. It uses an SMT solver (Z3) for fast
and precise reasoning about programs with unbounded data
types such as integers and maps, and operations such as
arithmetic, map select and update, etc. Corral essentially
works by converting a subset S of the program’s beahviors
to a Z3 formula φS such that φS is satisfiable if and only
if S contains an assertion violation. If S is not sufficient,
then Corral issues a different query to Z3 to either expand
S or concludes that the program does not have any assertion
violations. This strategy of iteratively expanding S allows
Corral to be goal-directed. For small programs (single procedure, no loops), Corral simply picks S to be the set of all
program behaviors. For instance, for the Boogie program
shown in Figure 5, the formula shown in Figure 6 is (a simplified version of) the Z3 formula.
reported by Corral consists of a trace in the Boogie program
along with values of variables at various points in the trace.
Get Me Here translates this trace into one that makes sense
for the C# program. The resulting trace has the following
All state changes in the program are encoded by having
multiple incarnations of the same variable with equations
linking the values between states. Incarnations are denoted
with subscripts, thus x1 is the value of x after it has been
incremented from its value of x0 . When this formula is fed
to Z3, it will try and find values of a and b that satisfy the
formula. Thus, overall, Z3’s search for a satisfying assignment essentially corresponds to searching over all possible
executions of the program.
The call and return statements form a call tree, location
indicates that the program executed the source code at the
given location, and state describes a symbolic heap at the
most recent location. The state map assigns values to local
variables, global variables, and symbolic addresses that represent sharing and cycles in the heap graph. For example,
if two local variables x and y both refer to an object with
integer field f with value 3, then the state map contains
{x 7→ ref a0 , y 7→ ref a0 , a0 7→ fieldmap{f 7→ 3}}.
We now briefly describe how Corral works on larger programs (for which generating a single Z3 formula is either
not feasible or too expensive). Corral uses the heuristic that
for most programs, only a few number of variables and a few
number of procedures will be relevant to the assertions in the
program. Consequently, at any point in time, the subset S
is defined as a pair (V, I), where V is a subset of the set
of all Boogie variables, and I is a partially-inlined program.
Initially, V is empty and I is just the entry procedure of
the program. Semantically, S encodes the set of all program
executions that are contained in I and only reason about
variables in V , while abstracting away the rest. As the set
V is increased, Corral gains more precision (trading off time
because the Z3 formulae get more complicated). When I
grows (i.e., more procedures are inlined), then the syntactic
scope of Corral increases and it can find longer counterexamples (again, while trading off time). The actual process
of Corral interacting with Z3 on how to expand V and I is
more technically involved; we refer the interested reader to
publications on Corral [8].
Because Boogie is a turing-complete language, the problem of finding assertion violations in Boogie is undecidable.
Thus, it is possible that the expansion of S inside Corral
may go on forever. In order to counter non-termination,
Corral imposes a bound on I. (The set V is always bounded
by the set of all global varibles in Boogie, which is a finite
number.) This bound limits the number of loop iterations
and recursive calls. For this paper’s case studies, a bound of
one (i.e., a path can have at most one loop iteration per loop
and one recursive call) was sufficient to produce the results
of our case studies.
Since Get Me Here injected a false assertion at the end point,
when Corral produces a counterexample, then it must be a
trace that finishes at the end point. The counterexample
Trace := Statement∗
Statment := call MethodName
| location File, LineNumber
| state {Name 7→ Value}
| return
Name := Identifier | Address
Value := int Integer
| bool Boolean
| fieldmap {Identifier 7→ Value}
| arraymap {Integer 7→ Value}
| ref Address
Trace Visualization
The execution trace visualization is based on Debugger Canvas [5], which is an implementation of the Code Bubbles
paradigm [3] for Visual Studio. For each call in the execution trace, Get Me Here creates a code bubble containing
that method’s text. Get Me Here uses the trace’s location entries to highlight executed code in green and uses the
trace’s state entries to annotate the code with the values of
variables. The visualization draws solid arrows to represent
direct method calls and dashed arrows to represent indirect
method calls (for example, due to events).
Technically, the trace file contains more information than
is displayed in the visualization. In particular, within a
method body, the trace file specifies the order in which statements are executed and interleaved with outgoing calls. To
avoid clutter, the visualization does not present the order
of statement execution, but simply uses background color
to represent whether a statement is executed at all. In the
absence of loops, this loss of information creates no ambiguities, since the programming language semantics dictate
the order of execution. (Recursion is handled by creating
one code bubble for each recursive call.) In the future, to
accommodate loops, we will add a timeline slider to allow
the user to witness the order of execution.
Multiple-Waypoint Queries
Get Me Here also supports a more general kind of query in
which the user can specify multiple waypoints, namely, an
optional start point, zero or more intermediate points, and
an end point. Each waypoint can specify an optional condition, which is a predicate over the program state. The user
interface for these queries is based on Visual Studio’s familiar debugging margin (Figure 7). Just as the user can click
on the debugging margin to create a breakpoint, the user can
click on GetMeHere’s margin (shown in dark gray) to create
We encode user queries with one or more waypoints by instrumenting the program as follows. In the most general
case, let the query have a starting point, N intermediate
points (N ≥ 1) and an end point. Each of these waypoints has a condition Condi . We introduce a new variable
$tracker of type int. At the starting point of the query,
we insert the following peice of code:
assume(Cond0 );
$tracker := 0;
At the nth intermediate point, we insert the code:
assume $tracker == n - 1;
assume(Condn );
$tracker := $tracker + 1;
At the end point, we insert the code:
assume $tracker == N;
assume(CondN +1 );
assert false;
Figure 7: Invoking a Get Me Here query with a specified start line, intermediate line, and end line.
When program exploration begins at the start node, the
nature of this instrumentation ensures that the only way to
reach the assert false is to go through the waypoints in
order before reaching the end node.
a waypoint. A context menu allows the user to specific the
kind of waypoint, plus an optional condition. The user interface borrows familiar visual elements from route finding
services, like Bing Maps or Google Maps. In particular, we
letter the waypoints in the order they must be reached, use
green for the start point and use red for the end point. The
user invokes the “Find Execution Path” menu item (shown
in Figure 1) to launch a search to satisfy a multiple-waypoint
query. Using the “Get Me Here” menu item is a shortcut that
both creates a query (a condition-less end point wherever the
user invoked the menu and a start point at the program’s
entry point) and launches the search. We feel that this type
of query is likely to be very common.
Supporting multiple waypoints is important given that the
static analysis relies on an SMT solver. The solver is free to
choose any counterexample, which means that Get Me Here
can present any execution trace that reaches the end point.
Of course, the solver’s preferred choice may not necessarily
be the user’s preferred choice! Allowing a query to specify
intermediate points and conditions allows the user to search
iteratively for the trace she prefers. For example, if Get Me
Here presents a trace that take a normal path through a
method and the user prefers to see an exceptional path, she
can place an intermediate point inside a catch statement. If
Get Me Here presents a trace in which an environment variable has the value "en-us" and the user prefers the value
"en-uk", she can set a conditional waypoint at the line of
code where that environment variable is read. In short, we
expect that a common usage pattern will be for a user first
to issue a simple endpoint-only query then follow with more
complex multiple-waypoint queries to home in on a desired
execution scenario. Because each code bubble in an execution trace is a full-featured editor with its own Get Me Here
margin, a user can specify and launch a new query directly
from the trace visualization of the previous query.
Trading off Accuracy for Speed
Corral is a whole-program analyzer for Boogie programs.
However, Get Me Here does not give Corral a Boogie translation of the the user’s whole program (including all assemblies used directly or indirectly), since this would be too
much code to analyze in a reasonable amount of time. Instead, we allow the user to make the trade-off between speed
and accuracy at the level of individual methods, by presenting Corral with one of three versions of a method’s body:
• Full code (the slowest and most accurate). BCT provides a complete translation of the method’s body.
• Model code. We provide a simpler, surrogate version of
a method’s body, typically written in a source language
like C#.
• No code (the fastest and least accurate). We provide
no body, in which case Corral assumes that the method
has no side effects and uses nondeterministic values for
the return value and output parameters.
By default, Get Me Here provides full code for all methods
that are part of the user’s project files and no code for the
.NET framework assemblies. Get Me Here uses the model
code for certain framework methods that are necessary to
reach parts of the user’s code. For example, the Tetris program uses the WinForms library for creating its graphical
user interface (GUI). As is typical with GUI libraries, the
Tetris code registers many methods as callbacks for particular UI events and then calls Application.Run to wait for
these methods to be called. If Corral were presented with no
body for Application.Run, none of the registered callbacks
would be reachable.
To solve this problem, we have experimented with two model
versions of the WinForms library. The first version correctly
public static void Run ( Form form )
form . OnLoad ( EventArgs . Empty ) ;
form . OnShown ( EventArgs . Empty ) ;
while (! form . IsClosed )
if ( Picker . PickBool () )
var control =
Control . AllControls . PickOne () ;
control . DoRandomEvent () ;
var menuItem =
MenuItem . AllMenuItems . PickOne () ;
menuItem . OnClick ( EventArgs . Empty ) ;
and used unmodified (except for the seeded bugs in the user
study) to be representative of real programs. To test the
scalability of our approach, they are different orders of magnitude in size. They use three different styles of interaction
with the user (console, GUI and web). They represent problem domains that are familiar to a typical developer, without
the need for domain expertise. Finally, they are all written
in the same programming language so that developers recruited for the study needed to know only one language.
• Calculator, 650 lines of C#, is a console program
that accepts simple arithmetic expressions and evaluates them. It has 249 methods. We seeded a bug
in which the modulus operator pops three operands
rather than two.
var timer = Timer . AllTimers . PickOne () ;
timer . OnTick ( EventArgs . Empty ) ;
form . OnClosed ( EventArgs . Empty ) ;
Figure 8: The model of the UI event loop we used
in the Tetris case study.
models many details of the library, including the organization of the UI into a tree of elements, the collection of these
trees into a stack to implement modal dialogs, and the algorithm for propagating events up the tree at the top of
the stack. The second version (Figure 8) simply keeps a
global set of all UI elements and repeatedly sends an arbitrary event to an arbitrary UI element. The second model
is more permissive that the actual WinForms implementation, but is faster for Corral to explore. We allow the user
to choose between the two models. For those programs,
like Tetris, whose behavior does not depend on such details
as the hierarchical propagation of events, the less accurate
model provides faster query responses.
Our ultimate goal is to test whether developers using Get
Me Here are are more productive and more accurate in completing software maintenance tasks. However, in order to
test our tool with users, it needs to be robust enough to
handle arbitrary queries and responsive enough to answer
queries in a reasonable amount of time. Hence, this initial
case study evaluates the tool’s performance on three benchmark programs, chosen for suitability for user testing. To
measure the extent to which developers struggle to answer
reachability questions on these programs, we ran a formative
user study in which participants used Visual Studio alone
to reproduce and fix one bug in each program. Here, we
describe our benchmark programs, the results of the user
study, and our measurements of Get Me Here on these three
Benchmark Programs
Our three benchmark programs were chosen with several
criteria in mind. They were downloaded from the internet
• Tetris, 3000 lines of C#, is an implementation of the
popular Tetris game, with a graphical user interface
built using the WinForms library. It has 357 methods.
We seeded a bug by removing a case from a switch
statement that chooses which game piece should fall
• Tailspin Toys, 11,000 lines of C# (along with 400
lines of ASPX, 1400 lines of JavaScript), is a sample
retail web site for purchasing toys. It has 1613 methods. This code already had a bug in which adding
an item twice to the shopping cart results in only one
Both Calculator and Tetris contain Main methods as starting points. Tailspin Toys, however, is a web service and
exposes multiple entry points. To accommodate this, we
created a harness that nondeterministically calls methods
marked with the attribute AcceptVerbs(HttpVerbs.Get) or
AcceptVerbs(HttpVerbs.Post). The creation of such harnesses could easily be automated.
Formative User Study
To measure the extent to which developers struggle to answer reachability questions on these benchmark programs,
we ran a user study in which participants were asked to reproduce and fix one bug in each program. We recruited 12
professional developers from the Puget Sound area, all male,
average of 41 years old, with an average of 14 years of professional experience. Two participants completed only the
first task, and there data are not included in the study.
We ran each participant independently in a two-hour session. After completing a demographic questionnaire, each
participant was given three tasks to complete. Each task
consisted of a vague bug report, whose wording is given below. Each participant was first asked to show the experimenter the steps necessary to consistently reproduce the
bug. Then each participant was asked to fix the bug. Each
participant was given roughly a half-hour to complete each
task. All participants completed tasks 1 and 3, and all but
one completed task 2.
Task 1: Your team mate sent you the following email:
When I tested the Tetris game, it would crash at random.
When I ran in in the debugger, it would eventually have an
assert failure with this message: “bad nextFig!”
Task 2: Your tester send you the following email: I forgot
to tell you before I left work that the Calculator game me
a bad error message. It said “not enough arguments” even
though I used it correctly. Sorry I can’t remember exactly
what I typed in since I’m away from my desk.
Task 3: Your tester send you the following email: I spent
the day trying out the Tailspin Toys web site. Mostly, it
works great! I only found one bug. I had entered some stuff
in the shopping cart and I got an uncaught exception that
says “something went wrong”. (Nice exception message!)
I’ve been trying since then, but I haven’t been able to repro
the problem.
Each bug report contains a string that all participants managed to find immediately in its respective program. We call
the corresponding control point — program statement —
the task query. By design, reproducing the bug amounts to
a reachability question: under what circumstance does the
program reach the line of code containing that string? The
participants varied considerably in the amount of time it
took them to reproduce the bug.
The Calculator program proved to be the biggest challenge
for our participants to reproduce, despite its modest size.
There are many call paths that lead to the Pop method,
which means there are many parts of the code to check.
Because developers took considerable time and felt considerable frustration in reproducing bugs in these programs, we
conclude that they are worthy benchmarks.
Performance on the Task Queries
We used Get Me Here to find each of the three task queries
mentioned in Section 4.2. Corral was able to produce a path
in each case. Each time, the path passed through the buggy
portion of code. The displayed path along with the data
values made the bug quite evident. Details on Corral’s performance on these queries is reported in Figure 10. For each
of the task queries, we show: the number of minutes that
Corral took to produce a path (Time); the number of iterations Corral required and the number of variables tracked
(Iters/Vars); and some statistics about the size of the resulting trace, namely, the number of lines of code (LOC),
the number of method calls (Calls), and the size of the stack
trace at the task query (Stack). In our experience, the number of Corral iterations and tracked variables usually give a
measure of the difficuly of a particular query.
For Console Calculator and Tailspin Toys, Corral found the
relevant execution trace considerably faster than the developers did in the lab study. Tetris had the most complex
logic of the three benchmarks (reflected in the large number
of iterations and tracked variables), and consequently gave
Corral a hard time. Even though Corral took longer than
developers did in the lab study, all of this time is automated.
Human time is spent only looking at the trace reported by
Corral (and that time is not reported in these results). Console Calculator had the highest stack depth, and the task
query was further away from Main than for other programs.
This may be one reason why developers found it difficult to
1. Tetris
2. Calculator
3. TailSpin
Time to reproduce bug (minutes)
min. – max.
mean (s.d.)
2:18 (2:13)
3 – 33
15:47 (7:43)
2 – 21
8:12 (5:33)
Figure 9: Developers’ performance on the three
reachability questions.
1. Tetris
2. Calculator
3. TailSpin
6 (35)
4 (11)
2 (4)
Resulting Trace
LOC Calls Stack
Figure 10: Corral’s performance on three reachability questions.
debug Console Calculator. Tailspin Toys, despite its large
size, proved to be the easiest for Corral to explore. It did,
however, produce a long trace that highlighted 586 lines of
code. This experiment shows that automated reasoning and
manual reasoning can be complementary to each other.
Performance on Arbitrary Queries
It is possible that the queries issued in the previous section
may not be representative of the possible queries that a user
of Get Me Here may have issued. This section measures the
performance of Corral on a large set of queries on our benchmark programs. For each program, we generated one GetMe
Here query for each line of code that is either (1) a possible
target of a branch, or (2) the beginning of a method. This
generated 180 queries for Console Calculator, 567 queries
for Tetris and 1278 queries for Tailspin Toys. We ran Corral
on all of these queries with two goals: what is the average
time taken by Corral to answer these queries, and for what
fraction of queries was Corral not able to produce a definite
reasult (i.e., it reached the depth bound, which is set to one
for this study).
The results are shown in Figure 11. This table shows the
overall number of queries issued and the average time taken
(in seconds) on these queries. The other columns divide up
the queries according to the answer that Corral reported:
Reachable (i.e., Corral found a path), Unreachable (i.e.,
there is no path that reaches the target), or Out-of-Bounds
(i.e., Corral reached the depth bound and the result in inconclusive). For reachable queries, the table also gives the
average length of the path (in terms of lines of code).
The results show that most queries were answered in a reasonable amount of time. Furthermore, only a fairly small
number of queries resulted in an inconclusive answer for
Console Calculator (5%) and Tailspin Toys (4%), although
the number is higher for Tetris (21%). This shows that Corral is able to get a good amount of coverage on our benchmarks.
Most of queries that were deemed unreachable by Corral
was because the end point was in dead code (either inside
framework methods that were never called from the program, or inside methods that were not invoked from Main,
Num Time
Time Length
Figure 11: Performance of Corral on all auto-generated Get Me Here queries. All times are in seconds.
given our modeling of the stubs). For instance, the large
number of unreachable queries for Tailspin Toys is because
our harness did not invoke some of the functionality of the
web service. Consequently, Corral did not take much time
on such queries.
Even with the advanced features of today’s development
tools, developers often struggle to answer reachability questions about their code. In this paper, we repurposed existing verification tools to create a prototype query engine, Get
Me Here, that is capable of automatically answering many
reachability questions. In our case study, Get Me Here found
execution traces for three reachability questions that developers struggled to answer in the lab. For two of the three
queries, GetMeHere found the trace considerably faster than
developers did. When given a set of arbitrary queries, Get
Me Here provided accurate answers to most queries in a reasonable amount of time. This suggests that Get Me Here is
sufficiently robust for a future user evaluation.
Our initial experience also suggests some interesting extensions. The first is the ability to transition between static
and dynamic traces. When Get Me Here produces a trace,
it typically contains all the interactions with the environment needed to reach a line of code. These interactions
could be replayed to recreate the trace at runtime. For example, the trace from Console Calculator provides values
for all the parsed tokens, which makes it possible to reconstruct the input needed to witness the same behavior in the
debugger. In the reverse direction, logs of the program’s behavior can be readily turned into multiple-waypoint queries
in order to explore execution traces that are capable of generating the contents of the log. For example, it the user
has a log entry that says EXIT ERROR 0x12345, then she can
find the corresponding code Log.Write("EXIT ERROR {0}",
code) and issue a GetMeHere query with the condition code
== 0x12345. With the current prototype, transitioning between static and dynamic traces is possible, but tedious,
suggesting the possibility of future automation.
Another extension is to increase the interaction between the
programmer and the tool. For instance, if the tool’s search
were to report its progress by showing the frontier of explored code, then the programmer could iteratively (or even
interactively!) guide the search. When Corral gets stuck
because of its bounded search, then the programmer could
locally adjust the bounds.
An easy way to refine the stubs is also crucial. Even with
stubs for the most frequently used Framework classes, programmers will always run into situations where a new environment model is needed. The ability to write them in
the same programming language as the implementation is
written in makes it possible for the intended users of the
system to effectively write stubs. Without stubs for the
Framework’s Stack class, Get Me Here reports an infeasible
error trace that does not respect the semantics of the stack’s
actual behavior. Such error traces reduce the programmer’s
confidence in the tool. It requires only a very simple model
for the Stack class in order to have Get Me Here produce a
realistic trace.
Over the past years, the research community has made considerable progress toward automated program verification.
Our goal with this preliminary paper is suggest a new goal
for the same community: making analysis algorithms an online part of the development environment to keep developers
informed about execution behavior throughout the development process.
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