Crash Reproduction Using Helper Objectives

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Abstract

Evolutionary-based crash reproduction techniques aid developers in their debugging practices by generating a test case that reproduces a crash given its stack trace. In these techniques, the search process is typically guided by a single search objective called Crash Distance. Previous studies have shown that current approaches could only reproduce a limited number of crashes due to a lack of diversity in the population during the search. In this study, we address this issue by applying Multi-Objectivization using Helper-Objectives (MO-HO) on crash reproduction. In particular, we add two helper-objectives to the Crash Distance to improve the diversity of the generated test cases and consequently enhance the guidance of the search process. We assessed MO-HO against the single-objective crash reproduction. Our results show that MO-HO can reproduce two additional crashes that were not previously reproducible by the single-objective approach.
Original languageEnglish
Title of host publicationGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Place of PublicationCancún, Mexico
PublisherACM DL
Pages309-310
Number of pages2
ISBN (Electronic)9781450371278
DOIs
Publication statusPublished - 2020
EventGenetic and Evolutionary Computation Conference - Cancún, Mexico
Duration: 8 Jul 202012 Jul 2020
Conference number: 2020
https://gecco-2020.sigevo.org/

Publication series

NameGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

ConferenceGenetic and Evolutionary Computation Conference
Abbreviated titleGECCO
CountryMexico
CityCancún
Period8/07/2012/07/20
OtherVirtual/online event due to COVID-19
Internet address

Keywords

  • Crash reproduction
  • MOEA
  • Search-based software testing

Cite this

Derakhshanfar, P., Devroey, X., Zaidman, A., van Deursen, A., & Panichella, A. (2020). Crash Reproduction Using Helper Objectives. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 309-310). (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion). ACM DL. https://doi.org/10.1145/3377929.3390077