Symbolic search-based testing

Baars, Arthur I.; Harman, Mark; Hassoun, Youssef; Lakhotia, Kiran; McMinn, Phil; Tonella, Paolo; Vos, Tanja E. J.

doi:10.1109/ase.2011.6100119

Cited by 72 publications

(60 citation statements)

References 23 publications

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“…As for any other symbolic execution approach, the results of SUSHI depend on the path exploration strategy. Our evaluation provides additional evidence of the differences and the complementarity between symbolic execution and search-based software testing approaches [2,22,31,35,55].…”

Section: Rq3: Sushi Wrt Complex Heap Inputsmentioning

confidence: 99%

Combining symbolic execution and search-based testing for programs with complex heap inputs

Braione

Denaro

Mattavelli

et al. 2017

Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis

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Despite the recent improvements in automatic test case generation, handling complex data structures as test inputs is still an open problem. Search-based approaches can generate sequences of method calls that instantiate structured inputs to exercise a relevant portion of the code, but fall short in building inputs to execute program elements whose reachability is determined by the structural features of the input structures themselves. Symbolic execution techniques can effectively handle structured inputs, but do not identify the sequences of method calls that instantiate the input structures through legal interfaces. In this paper, we propose a new approach to automatically generate test cases for programs with complex data structures as inputs. We use symbolic execution to generate path conditions that characterise the dependencies between the program paths and the input structures, and convert the path conditions to optimisation problems that we solve with search-based techniques to produce sequences of method calls that instantiate those inputs. Our preliminary results show that the approach is indeed effective in generating test cases for programs with complex data structures as inputs, thus opening a promising research direction. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Formal software verification; KEYWORDSAutomatic test case generation, Symbolic execution, Search-based software engineering ACM Reference format:

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Section: Rq3: Sushi Wrt Complex Heap Inputsmentioning

confidence: 99%

Combining symbolic execution and search-based testing for programs with complex heap inputs

Braione

Denaro

Mattavelli

et al. 2017

Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis

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“…Lakhotia et al [1] propose a fitness function based on symbolic execution. The proposed fitness function analyses and approximates symbolic execution paths: some variables are approximated and any branches involving these variables are ignored.…”

Section: Related Workmentioning

confidence: 99%

“…During solving test target if a target is proved infeasible, then the generator drops it from the set of target to cover. 1 Choco is an open source java constraint programming library. urlhttp://www.emn.fr/z-info/choco-solver/pdf/choco-presentation.pdf…”

Section: Cbt Corementioning

confidence: 99%

Boosting Search Based Testing by Using Constraint Based Testing

Sakti

Guéhéneuc

Pesant

2012

Search Based Software Engineering

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Abstract. Search-Based Testing (SBT) uses an evolutionary algorithm to generate test cases. Traditionally, a random selection is used to generate an initial population and also, less often, during the evolution process. Such selection is likely to achieve lower coverage than a guided selection. We define two novel concepts: (1) a constrained population generator (CPG) that generates a diversified initial population that satisfies some test target constraints; and (2) a constrained evolution operator (CEO) that evolves test candidates according to some constraints of the test target. Either the CPG or CEO may substantially increase the chance of reaching adequate coverage with less effort. In this paper, we propose an approach that models a relaxed version of the unit under test as a constraint satisfaction problem. Based on this model and the test target, a CPG generates an initial population. Then, an evolutionary algorithm uses a CEO and this population to generate test input leading to the test target being covered. Our approach combines constraint-based testing (CBT) and SBT and overcomes the limitations associated with each of them. Using eToc, an open-source SBT tool, we implement a prototype of this approach. We present the empirical results of applying both CPG or CEO on three open-source programs and show that CPG or CEO improve SBT performance in terms of branch coverage by 11% while reducing computation time.

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“…Current research develops both these techniques, and also hybrids that combine the best features of both [9], [63], [71], [110].…”

Section: A Brief History Of Sbstmentioning

confidence: 99%

Achievements, Open Problems and Challenges for Search Based Software Testing

Harman¹,

Jia²,

Zhang³

2015

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

Self Cite

158

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Abstract-Search Based Software Testing (SBST) formulates testing as an optimisation problem, which can be attacked using computational search techniques from the field of Search Based Software Engineering (SBSE). We present an analysis of the SBST research agenda 1 , focusing on the open problems and challenges of testing non-functional properties, in particular a topic we call 'Search Based Energy Testing' (SBET), Multi-objective SBST and SBST for Test Strategy Identification. We conclude with a vision of FIFIVERIFY tools, which would automatically find faults, fix them and verify the fixes. We explain why we think such FIFIVERIFY tools constitute an exciting challenge for the SBSE community that already could be within its reach.

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Symbolic search-based testing

Cited by 72 publications

References 23 publications

Combining symbolic execution and search-based testing for programs with complex heap inputs

Combining symbolic execution and search-based testing for programs with complex heap inputs

Boosting Search Based Testing by Using Constraint Based Testing

Achievements, Open Problems and Challenges for Search Based Software Testing

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