Test input generation with java PathFinder

Visser, Willem; Păsăreanu, Corina S.; Khurshid, Sarfraz

doi:10.1145/1007512.1007526

Cited by 373 publications

(278 citation statements)

References 65 publications

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“…The empirical evidence on research in this area often focuses on container classes [4,5,6,7,10,12,14,17,19,22,23,26,27,29,30,31,33,34]-containers are an important part of many standard libraries, and bugs in these containers could significantly affect applications, so directing testing efforts to these containers is worthwhile. Testing containers is not only important but also challenging to achieve with some advanced testing technique such as those based on symbolic execution [30].…”

Section: Introductionmentioning

confidence: 99%

Testing Container Classes: Random or Systematic?

Sharma

Gligoric

Arcuri

et al. 2011

Fundamental Approaches to Software Engineering

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Abstract. Container classes such as lists, sets, or maps are elementary data structures common to many programming languages. Since they are a part of standard libraries, they are important to test, which led to research on advanced testing techniques targeting such containers and research on comparing testing techniques using such containers. However, these techniques have not been thoroughly compared to simpler techniques such as random testing. We present the results of a larger case study in which we compare random testing with shape abstraction, a systematic technique that showed the best results in a previous study. Our experiments show that random testing is about as effective as shape abstraction for testing these containers, which raises the question whether containers are well suited as a benchmark for comparing advanced testing techniques.

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Section: Introductionmentioning

confidence: 99%

Testing Container Classes: Random or Systematic?

Sharma

Gligoric

Arcuri

et al. 2011

Fundamental Approaches to Software Engineering

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“…Existing model-checking technology has been applied to test-case generation in a number of other projects, such as Java PathFinder [28] or SAL2 [15]. Recently, model checking and testing were given a more uniform view, combining over-approximating and under-approximating analyses [14] and using interpolation [24].…”

Section: Related Workmentioning

confidence: 99%

Information Reuse for Multi-goal Reachability Analyses

Beyer

Holzer

Tautschnig

et al. 2013

Programming Languages and Systems

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Abstract.It is known that model checkers can generate test inputs as witnesses for reachability specifications (or, equivalently, as counterexamples for safety properties). While this use of model checkers for testing yields a theoretically sound test-generation procedure, it scales poorly for computing complex test suites for large sets of test goals, because each test goal requires an expensive run of the model checker. We represent test goals as automata and exploit relations between automata in order to reuse existing reachability information for the analysis of subsequent test goals. Exploiting the sharing of sub-automata in a series of reachability queries, we achieve considerable performance improvements over the standard approach. We show the practical use of our multi-goal reachability analysis in a predicate-abstraction-based test-input generator for the test-specification language FQL.

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“…Static analysis of the control flow graph is perhaps the oldest, and best known, approach to identify such paths. Recent tools adopting this approach are Java PathFinder (Visser et al, 2004), Exe (Cadar et al, 2006), and Klee (Cadar et al, 2008). …”

Section: Related Workmentioning

confidence: 99%

“…The problem of generating test cases that increase code coverage is being recently tackled by approaches that generate test cases using symbolic and concolic (that is, interwoven concrete and symbolic) execution (Visser et al, 2004;Godefroid et al, 2005;Sen et al, 2005). These approaches explore the executable space of a program, typically in depth-first order, and generate test cases accordingly.…”

Section: Introductionmentioning

confidence: 99%

Enhancing structural software coverage by incrementally computing branch executability

et al. 2011

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Structural code coverage criteria have been studied since the early seventies, and now they are well supported by commercial and open source tools, and are commonly embedded in several advanced industrial processes. Most industrial applications still refer to simple criteria, like statement and branch coverage, and consider more complex criteria, like modified condition decision coverage, only rarely and often driven by the requirements of certification agencies. The industrial value of structural criteria is limited by the difficulty of achieving high coverage, due to both the complexity of deriving test cases that execute specific uncovered elements and the presence of many infeasible elements in the code.In this paper, we propose a technique that both generates test cases that execute yet uncovered branches and identifies infeasible branches that can be eliminated from the computation of the branch coverage. In this way, we can increase branch coverage up to closely approximate full coverage, thus improving its industrial value. The algorithm combines symbolic analysis, abstraction refinement, and a novel technique named coarsening, to execute unexplored branches, identify infeasible ones, and mitigate the state space explosion problem. In the paper, we present the technique, and illustrate its effectiveness through a set of experimental results obtained with a prototype implementation. This paper is an extended version of Baluda et al (2010).

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Test input generation with java PathFinder

Cited by 373 publications

References 65 publications

Testing Container Classes: Random or Systematic?

Testing Container Classes: Random or Systematic?

Information Reuse for Multi-goal Reachability Analyses

Enhancing structural software coverage by incrementally computing branch executability

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