1,067 research outputs found
An empirical investigation into branch coverage for C programs using CUTE and AUSTIN
Automated test data generation has remained a topic of considerable interest for several decades because it lies at the heart of attempts to automate the process of Software Testing. This paper reports the results of an empirical study using the dynamic symbolic-execution tool. CUTE, and a search based tool, AUSTIN on five non-trivial open source applications. The aim is to provide practitioners with an assessment of what can be achieved by existing techniques with little or no specialist knowledge and to provide researchers with baseline data against which to measure subsequent work. To achieve this, each tool is applied 'as is', with neither additional tuning nor supporting harnesses and with no adjustments applied to the subject programs under test. The mere fact that these tools can be applied 'out of the box' in this manner reflects the growing maturity of Automated test data generation. However, as might be expected, the study reveals opportunities for improvement and suggests ways to hybridize these two approaches that have hitherto been developed entirely independently. (C) 2010 Elsevier Inc. All rights reserved
A Survey of Symbolic Execution Techniques
Many security and software testing applications require checking whether
certain properties of a program hold for any possible usage scenario. For
instance, a tool for identifying software vulnerabilities may need to rule out
the existence of any backdoor to bypass a program's authentication. One
approach would be to test the program using different, possibly random inputs.
As the backdoor may only be hit for very specific program workloads, automated
exploration of the space of possible inputs is of the essence. Symbolic
execution provides an elegant solution to the problem, by systematically
exploring many possible execution paths at the same time without necessarily
requiring concrete inputs. Rather than taking on fully specified input values,
the technique abstractly represents them as symbols, resorting to constraint
solvers to construct actual instances that would cause property violations.
Symbolic execution has been incubated in dozens of tools developed over the
last four decades, leading to major practical breakthroughs in a number of
prominent software reliability applications. The goal of this survey is to
provide an overview of the main ideas, challenges, and solutions developed in
the area, distilling them for a broad audience.
The present survey has been accepted for publication at ACM Computing
Surveys. If you are considering citing this survey, we would appreciate if you
could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing
this survey, we would appreciate if you could use the following BibTeX entry:
http://goo.gl/Hf5Fv
CTGEN - a Unit Test Generator for C
We present a new unit test generator for C code, CTGEN. It generates test
data for C1 structural coverage and functional coverage based on
pre-/post-condition specifications or internal assertions. The generator
supports automated stub generation, and data to be returned by the stub to the
unit under test (UUT) may be specified by means of constraints. The typical
application field for CTGEN is embedded systems testing; therefore the tool can
cope with the typical aliasing problems present in low-level C, including
pointer arithmetics, structures and unions. CTGEN creates complete test
procedures which are ready to be compiled and run against the UUT. In this
paper we describe the main features of CTGEN, their technical realisation, and
we elaborate on its performance in comparison to a list of competing test
generation tools. Since 2011, CTGEN is used in industrial scale test campaigns
for embedded systems code in the automotive domain.Comment: In Proceedings SSV 2012, arXiv:1211.587
SmartUnit: Empirical Evaluations for Automated Unit Testing of Embedded Software in Industry
In this paper, we aim at the automated unit coverage-based testing for
embedded software. To achieve the goal, by analyzing the industrial
requirements and our previous work on automated unit testing tool CAUT, we
rebuild a new tool, SmartUnit, to solve the engineering requirements that take
place in our partner companies. SmartUnit is a dynamic symbolic execution
implementation, which supports statement, branch, boundary value and MC/DC
coverage. SmartUnit has been used to test more than one million lines of code
in real projects. For confidentiality motives, we select three in-house real
projects for the empirical evaluations. We also carry out our evaluations on
two open source database projects, SQLite and PostgreSQL, to test the
scalability of our tool since the scale of the embedded software project is
mostly not large, 5K-50K lines of code on average. From our experimental
results, in general, more than 90% of functions in commercial embedded software
achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in
SQLite achieve 100% MC/DC coverage, and more than 60% of functions in
PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the
runtime exceptions at the unit testing level. We also have reported exceptions
like array index out of bounds and divided-by-zero in SQLite. Furthermore, we
analyze the reasons of low coverage in automated unit testing in our setting
and give a survey on the situation of manual unit testing with respect to
automated unit testing in industry.Comment: In Proceedings of 40th International Conference on Software
Engineering: Software Engineering in Practice Track, Gothenburg, Sweden, May
27-June 3, 2018 (ICSE-SEIP '18), 10 page
Badger: Complexity Analysis with Fuzzing and Symbolic Execution
Hybrid testing approaches that involve fuzz testing and symbolic execution
have shown promising results in achieving high code coverage, uncovering subtle
errors and vulnerabilities in a variety of software applications. In this paper
we describe Badger - a new hybrid approach for complexity analysis, with the
goal of discovering vulnerabilities which occur when the worst-case time or
space complexity of an application is significantly higher than the average
case. Badger uses fuzz testing to generate a diverse set of inputs that aim to
increase not only coverage but also a resource-related cost associated with
each path. Since fuzzing may fail to execute deep program paths due to its
limited knowledge about the conditions that influence these paths, we
complement the analysis with a symbolic execution, which is also customized to
search for paths that increase the resource-related cost. Symbolic execution is
particularly good at generating inputs that satisfy various program conditions
but by itself suffers from path explosion. Therefore, Badger uses fuzzing and
symbolic execution in tandem, to leverage their benefits and overcome their
weaknesses. We implemented our approach for the analysis of Java programs,
based on Kelinci and Symbolic PathFinder. We evaluated Badger on Java
applications, showing that our approach is significantly faster in generating
worst-case executions compared to fuzzing or symbolic execution on their own
Automatically Discovering, Reporting and Reproducing Android Application Crashes
Mobile developers face unique challenges when detecting and reporting crashes
in apps due to their prevailing GUI event-driven nature and additional sources
of inputs (e.g., sensor readings). To support developers in these tasks, we
introduce a novel, automated approach called CRASHSCOPE. This tool explores a
given Android app using systematic input generation, according to several
strategies informed by static and dynamic analyses, with the intrinsic goal of
triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented
crash report containing screenshots, detailed crash reproduction steps, the
captured exception stack trace, and a fully replayable script that
automatically reproduces the crash on a target device(s). We evaluated
CRASHSCOPE's effectiveness in discovering crashes as compared to five
state-of-the-art Android input generation tools on 61 applications. The results
demonstrate that CRASHSCOPE performs about as well as current tools for
detecting crashes and provides more detailed fault information. Additionally,
in a study analyzing eight real-world Android app crashes, we found that
CRASHSCOPE's reports are easily readable and allow for reliable reproduction of
crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on
Software Testing, Verification and Validation (ICST'16), Chicago, IL, April
10-15, 2016, pp. 33-4
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