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 publicationGenetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion)
Place of PublicationCancún, Mexico
PublisherACM DL
Number of pages3
Publication statusPublished - 2020
EventGenetic and Evolutionary Computation Conference - Cancún, Mexico
Duration: 8 Jul 202012 Jul 2020
Conference number: 2020


ConferenceGenetic and Evolutionary Computation Conference
Abbreviated titleGECCO
OtherVirtual/online event due to COVID-19
Internet address

    Research areas

  • crash reproduction, search-based software testing, MOEA

ID: 72126236