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A Guided Genetic Algorithm for Automated Crash Reproduction. / Soltani, Mozhan; Panichella, Annibale; Deursen, Arie van.

Proceedings of the 39th International Conference on Software Engineering (ICSE). IEEE, 2017. p. 209-220.

Research output: Scientific - peer-reviewConference contribution

Harvard

Soltani, M, Panichella, A & Deursen, AV 2017, A Guided Genetic Algorithm for Automated Crash Reproduction. in Proceedings of the 39th International Conference on Software Engineering (ICSE). IEEE, pp. 209-220. DOI: 10.1109/ICSE.2017.27

APA

Soltani, M., Panichella, A., & Deursen, A. V. (2017). A Guided Genetic Algorithm for Automated Crash Reproduction. In Proceedings of the 39th International Conference on Software Engineering (ICSE). (pp. 209-220). IEEE. DOI: 10.1109/ICSE.2017.27

Vancouver

Soltani M, Panichella A, Deursen AV. A Guided Genetic Algorithm for Automated Crash Reproduction. In Proceedings of the 39th International Conference on Software Engineering (ICSE). IEEE. 2017. p. 209-220. Available from, DOI: 10.1109/ICSE.2017.27

Author

Soltani, Mozhan; Panichella, Annibale; Deursen, Arie van / A Guided Genetic Algorithm for Automated Crash Reproduction.

Proceedings of the 39th International Conference on Software Engineering (ICSE). IEEE, 2017. p. 209-220.

Research output: Scientific - peer-reviewConference contribution

BibTeX

@inbook{3490acbb240b4ec28202712a7d1bb64e,
title = "A Guided Genetic Algorithm for Automated Crash Reproduction",
author = "Mozhan Soltani and Annibale Panichella and Deursen, {Arie van}",
year = "2017",
doi = "10.1109/ICSE.2017.27",
pages = "209--220",
booktitle = "Proceedings of the 39th International Conference on Software Engineering (ICSE)",
publisher = "IEEE",
address = "United States",

}

RIS

TY - CHAP

T1 - A Guided Genetic Algorithm for Automated Crash Reproduction

AU - Soltani,Mozhan

AU - Panichella,Annibale

AU - Deursen,Arie van

PY - 2017

Y1 - 2017

N2 - To reduce the effort developers have to make for crash debugging, researchers have proposed several solutions for automatic failure reproduction. Recent advances proposed the use of symbolic execution, mutation analysis, and directed model checking as underling techniques for post-failure analysis of crash stack traces. However, existing approaches still cannot reproduce many real-world crashes due to such limitations as environment dependencies, path explosion, and time complexity. To address these challenges, we present EvoCrash, a post-failure approach which uses a novel Guided Genetic Algorithm (GGA) to cope with the large search space characterizing real-world software programs. Our empirical study on three open-source systems shows that EvoCrash can replicate 41 (82%) of real-world crashes, 34 (89%) of which are useful reproductions for debugging purposes, outperforming the state-of-the-art in crash replication.

AB - To reduce the effort developers have to make for crash debugging, researchers have proposed several solutions for automatic failure reproduction. Recent advances proposed the use of symbolic execution, mutation analysis, and directed model checking as underling techniques for post-failure analysis of crash stack traces. However, existing approaches still cannot reproduce many real-world crashes due to such limitations as environment dependencies, path explosion, and time complexity. To address these challenges, we present EvoCrash, a post-failure approach which uses a novel Guided Genetic Algorithm (GGA) to cope with the large search space characterizing real-world software programs. Our empirical study on three open-source systems shows that EvoCrash can replicate 41 (82%) of real-world crashes, 34 (89%) of which are useful reproductions for debugging purposes, outperforming the state-of-the-art in crash replication.

U2 - 10.1109/ICSE.2017.27

DO - 10.1109/ICSE.2017.27

M3 - Conference contribution

SP - 209

EP - 220

BT - Proceedings of the 39th International Conference on Software Engineering (ICSE)

PB - IEEE

ER -

ID: 11926460