An intelligent method based on state space search for automatic test case generation

被引：1

作者：

机构：

[1] State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing

[2] School of Electronic and Information Engineering, Liaoning Technical University, Huludao

[3] State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing

来源：

| 1600年 / Academy Publisher卷 / 09期

关键词：

Backtrack; Bisection; Branch & Bound; Search-based software testing; State space search; Test case generation;

D O I：

10.4304/jsw.9.2.358-364

中图分类号：

学科分类号：

摘要：

Search-Based Software Testing reformulates testing as search problems so that test case generation can be automated by some chosen search algorithms. This paper reformulates path-oriented test case generation as a state space search problem and proposes an intelligent method Best-First-Search Branch & Bound to solve it, utilizing the algorithms of Branch & Bound and Backtrack to search the space of potential test cases and adopting bisection to lower the bounds of the search space. We also propose an optimization method by removing irrelevant variables. Experiments show that the proposed search method generates test cases with promising performance and outperforms some MetaHeuristic Search algorithms. © 2014 ACADEMY PUBLISHER.

引用

页码：358 / 364

页数：6

共 24 条

[11]

Jalilvand A., Khanmohammadi S., A new method for constructing the search tree in branch and bound algorithm, Proc. the 9th International Multitopic Conference (INMIC 05), IEEE Computer Society, pp. 1-5, (2005)

[12]

Delling D., Goldberg A.V., Razenshteyn I., Werneck R.F., Exact combinatorial branch-and-bound for graph bisection, ALENEX, pp. 30-44, (2012)

[13]

Beydeda S., Gruhn V., BINTEST-search-based test case generation, Computer Software and Applications In Computer Software and Applications Conference (COMPSAC 03), IEEE Computer Society, pp. 28-33, (2003)

[14]

Lisgara E.G., Karolidis G.I., Androulakis G.S., Advancing the backtrack optimization technique to obtain forecasts of potential crisis periods, Applied Mathematics, 3, 30, pp. 1538-1551, (2012)

[15]

McMinn P., Search-based software test data generation: A survey, Software Testing, Verification and Reliability, 14, 2, pp. 105-156, (2004)

[16]

Hickey T., Qun J., van Emden M.H., Interval arithmetic: From principles to implementation, Journal of the ACM, 48, 5, pp. 1038-1068, (2001)

[17]

Szer D., Charpillet F., Zilberstein S., MAA: A heuristic search algorithm for solving decentralized POMDPs, Proc. the 21st Conference On Uncertainty In Artificial Intelligence (UAI 05), pp. 576-583, (2005)

[18]

McMinn P., Harman M., Lakhotia K., Hassoun Y., Wegener J., Input domain reduction through irrelevant variable removal and its effect on local, global, and hybrid search-based structural test data generation, IEEE Transactions On Software Engineering, 38, 2, pp. 453-477, (2012)

[19]

Pattanayak B.K., Patra S.K., Puthal B., Optimizing AST node for Java script compiler a lightweight interpreter for embedded device, Journal of Computers, 8, 2, pp. 349-355, (2013)

[20]

Alba E., Chicano F., Observation in using parallel and sequential evolutionary algorithms for automatic software testing, Computers and Operators Research, 35, pp. 3161-3183, (2008)

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