Standard

Single-objective versus Multi-Objectivized Optimization for Evolutionary Crash Reproduction. / Soltani, Mozhan; Derakhshanfar, Pouria; Panichella, Annibale; Devroey, Xavier; Zaidman, Andy; van Deursen, Arie.

Search-Baed Software Engineering - 10th International Symposium, SSBSE 2018 - Proceedings. ed. / Thelma Elita Colanzi; Phil McMinn. Cham : Springer, 2018. p. 325-340 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11036 LNCS).

Research output: Scientific - peer-reviewConference contribution

Harvard

Soltani, M, Derakhshanfar, P, Panichella, A, Devroey, X, Zaidman, A & van Deursen, A 2018, Single-objective versus Multi-Objectivized Optimization for Evolutionary Crash Reproduction. in TE Colanzi & P McMinn (eds), Search-Baed Software Engineering - 10th International Symposium, SSBSE 2018 - Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11036 LNCS, Springer, Cham, pp. 325-340, SSBSE 2018, Montpellier, France, 8/09/18. DOI: 10.1007/978-3-319-99241-9_18

APA

Soltani, M., Derakhshanfar, P., Panichella, A., Devroey, X., Zaidman, A., & van Deursen, A. (2018). Single-objective versus Multi-Objectivized Optimization for Evolutionary Crash Reproduction. In T. E. Colanzi, & P. McMinn (Eds.), Search-Baed Software Engineering - 10th International Symposium, SSBSE 2018 - Proceedings (pp. 325-340). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11036 LNCS). Cham: Springer. DOI: 10.1007/978-3-319-99241-9_18

Vancouver

Soltani M, Derakhshanfar P, Panichella A, Devroey X, Zaidman A, van Deursen A. Single-objective versus Multi-Objectivized Optimization for Evolutionary Crash Reproduction. In Colanzi TE, McMinn P, editors, Search-Baed Software Engineering - 10th International Symposium, SSBSE 2018 - Proceedings. Cham: Springer. 2018. p. 325-340. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 10.1007/978-3-319-99241-9_18

Author

Soltani, Mozhan ; Derakhshanfar, Pouria ; Panichella, Annibale ; Devroey, Xavier ; Zaidman, Andy ; van Deursen, Arie. / Single-objective versus Multi-Objectivized Optimization for Evolutionary Crash Reproduction. Search-Baed Software Engineering - 10th International Symposium, SSBSE 2018 - Proceedings. editor / Thelma Elita Colanzi ; Phil McMinn. Cham : Springer, 2018. pp. 325-340 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inbook{ccece8a179cd4303adca34a920bf7d14,
title = "Single-objective versus Multi-Objectivized Optimization for Evolutionary Crash Reproduction",
abstract = "EvoCrash is a recent search-based approach to generate a test case that reproduces reported crashes. The search is guided by a fitness function that uses a weighted sum scalarization to combine three different heuristics: (i) code coverage, (ii) crash coverage and (iii) stack trace similarity. In this study, we propose and investigate two alternatives to the weighted sum scalarization: (i) the simple sum scalarization and (ii) the multi-objectivization, which decomposes the fitness function into several optimization objectives as an attempt to increase test case diversity. We implemented the three alternative optimizations as an extension of EvoSuite, a popular search-based unit test generator, and applied them on 33 real-world crashes. Our results indicate that for complex crashes the weighted sum reduces the test case generation time, compared to the simple sum, while for simpler crashes the effect is the opposite. Similarly, for complex crashes, multi-objectivization reduces test generation time compared to optimizing with the weighted sum; we also observe one crash that can be replicated only by multi-objectivization. Through our manual analysis, we found out that when optimizing the original weighted function gets trapped in local optima, optimization for decomposed objectives improves the search for crash reproduction. Generally, while multi-objectivization is under-explored, our results are promising and encourage further investigations of the approach.",
author = "Mozhan Soltani and Pouria Derakhshanfar and Annibale Panichella and Xavier Devroey and Andy Zaidman and {van Deursen}, Arie",
year = "2018",
month = "9",
doi = "10.1007/978-3-319-99241-9_18",
isbn = "78-3-319-99240-2",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "325--340",
editor = "Colanzi, {Thelma Elita} and Phil McMinn",
booktitle = "Search-Baed Software Engineering - 10th International Symposium, SSBSE 2018 - Proceedings",

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RIS

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T1 - Single-objective versus Multi-Objectivized Optimization for Evolutionary Crash Reproduction

AU - Soltani,Mozhan

AU - Derakhshanfar,Pouria

AU - Panichella,Annibale

AU - Devroey,Xavier

AU - Zaidman,Andy

AU - van Deursen,Arie

PY - 2018/9

Y1 - 2018/9

N2 - EvoCrash is a recent search-based approach to generate a test case that reproduces reported crashes. The search is guided by a fitness function that uses a weighted sum scalarization to combine three different heuristics: (i) code coverage, (ii) crash coverage and (iii) stack trace similarity. In this study, we propose and investigate two alternatives to the weighted sum scalarization: (i) the simple sum scalarization and (ii) the multi-objectivization, which decomposes the fitness function into several optimization objectives as an attempt to increase test case diversity. We implemented the three alternative optimizations as an extension of EvoSuite, a popular search-based unit test generator, and applied them on 33 real-world crashes. Our results indicate that for complex crashes the weighted sum reduces the test case generation time, compared to the simple sum, while for simpler crashes the effect is the opposite. Similarly, for complex crashes, multi-objectivization reduces test generation time compared to optimizing with the weighted sum; we also observe one crash that can be replicated only by multi-objectivization. Through our manual analysis, we found out that when optimizing the original weighted function gets trapped in local optima, optimization for decomposed objectives improves the search for crash reproduction. Generally, while multi-objectivization is under-explored, our results are promising and encourage further investigations of the approach.

AB - EvoCrash is a recent search-based approach to generate a test case that reproduces reported crashes. The search is guided by a fitness function that uses a weighted sum scalarization to combine three different heuristics: (i) code coverage, (ii) crash coverage and (iii) stack trace similarity. In this study, we propose and investigate two alternatives to the weighted sum scalarization: (i) the simple sum scalarization and (ii) the multi-objectivization, which decomposes the fitness function into several optimization objectives as an attempt to increase test case diversity. We implemented the three alternative optimizations as an extension of EvoSuite, a popular search-based unit test generator, and applied them on 33 real-world crashes. Our results indicate that for complex crashes the weighted sum reduces the test case generation time, compared to the simple sum, while for simpler crashes the effect is the opposite. Similarly, for complex crashes, multi-objectivization reduces test generation time compared to optimizing with the weighted sum; we also observe one crash that can be replicated only by multi-objectivization. Through our manual analysis, we found out that when optimizing the original weighted function gets trapped in local optima, optimization for decomposed objectives improves the search for crash reproduction. Generally, while multi-objectivization is under-explored, our results are promising and encourage further investigations of the approach.

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DO - 10.1007/978-3-319-99241-9_18

M3 - Conference contribution

SN - 78-3-319-99240-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 340

BT - Search-Baed Software Engineering - 10th International Symposium, SSBSE 2018 - Proceedings

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