Crash Reproduction Using Helper Objectives

Pouria Derakhshanfar; Xavier Devroey; Andy Zaidman; Arie van Deursen; Annibale Panichella

doi:10.1145/3377929.3390077

Crash Reproduction Using Helper Objectives

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

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

74 Downloads (Pure)

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 language	English
Title of host publication	GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Place of Publication	Cancún, Mexico
Publisher	ACM DL
Pages	309-310
Number of pages	2
ISBN (Electronic)	9781450371278
DOIs	https://doi.org/10.1145/3377929.3390077
Publication status	Published - 2020
Event	Genetic and Evolutionary Computation Conference - Cancún, Mexico Duration: 8 Jul 2020 → 12 Jul 2020 Conference number: 2020 https://gecco-2020.sigevo.org/

Publication series

Name	GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

Conference	Genetic and Evolutionary Computation Conference
Abbreviated title	GECCO
Country/Territory	Mexico
City	Cancún
Period	8/07/20 → 12/07/20
Other	Virtual/online event due to COVID-19
Internet address	https://gecco-2020.sigevo.org/

Bibliographical note

Virtual/online event due to COVID-19

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Crash reproduction
MOEA
Search-based software testing

Access to Document

10.1145/3377929.3390077

3377929.3390077Final published version, 101 KB

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

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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.",

keywords = "Crash reproduction, MOEA, Search-based software testing",

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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. GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, ACM DL, Cancún, Mexico, pp. 309-310, Genetic and Evolutionary Computation Conference, Cancún, Mexico, 8/07/20. https://doi.org/10.1145/3377929.3390077

Crash Reproduction Using Helper Objectives. / Derakhshanfar, Pouria; Devroey, Xavier; Zaidman, Andy et al.
GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Cancún, Mexico: ACM DL, 2020. p. 309-310 (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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

Crash Reproduction Using Helper Objectives

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this