A Guided Genetic Algorithm for Automated Crash Reproduction

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

19 Citations (Scopus)
217 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 39th International Conference on Software Engineering (ICSE)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages209-220
Number of pages12
ISBN (Electronic)978-1-5386-3868-2
DOIs
Publication statusPublished - 2017
EventICSE 2017: 39th International Conference on Software Engineering - Buenos Aires, Argentina
Duration: 20 May 201728 May 2017
Conference number: 39
http://icse2017.gatech.edu/

Conference

ConferenceICSE 2017
CountryArgentina
CityBuenos Aires
Period20/05/1728/05/17
Internet address

Keywords

  • Search-Based Software Testing
  • Genetic Algorithms
  • Automated Crash Reproduction

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  • Cite this

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