Crash reproduction using helper objectives

Pouria Derakhshanfar, Xavier Devroey, Andy Zaidman, Arie Van Deursen, Annibale Panichella

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

Résumé

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.

langue originaleAnglais
titreGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
EditeurACM Press
Pages309-310
Nombre de pages2
ISBN (Electronique)9781450371278
Les DOIs
Etat de la publicationPublié - 8 juil. 2020
Modification externeOui
Evénement2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexique
Durée: 8 juil. 202012 juil. 2020

Série de publications

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

Une conférence

Une conférence2020 Genetic and Evolutionary Computation Conference, GECCO 2020
PaysMexique
La villeCancun
période8/07/2012/07/20

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