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Crash Reproduction Using Helper Objectives. / Derakhshanfar, Pouria; Devroey, Xavier; Zaidman, Andy; van Deursen, Arie; Panichella, Annibale.

Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion). Cancún, Mexico : ACM DL, 2020.

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

Harvard

Derakhshanfar, P, Devroey, X, Zaidman, A, van Deursen, A & Panichella, A 2020, Crash Reproduction Using Helper Objectives. in Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion). ACM DL, Cancún, Mexico, Genetic and Evolutionary Computation Conference, Cancún, Mexico, 8/07/20. https://doi.org/10.1145/3377929.3390077

APA

Vancouver

Derakhshanfar P, Devroey X, Zaidman A, van Deursen A, Panichella A. Crash Reproduction Using Helper Objectives. In Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion). Cancún, Mexico: ACM DL. 2020 https://doi.org/10.1145/3377929.3390077

Author

Derakhshanfar, Pouria ; Devroey, Xavier ; Zaidman, Andy ; van Deursen, Arie ; Panichella, Annibale. / Crash Reproduction Using Helper Objectives. Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion). Cancún, Mexico : ACM DL, 2020.

BibTeX

@inproceedings{25f23a1b802f4dada56ba506b3ef7cec,
title = "Crash Reproduction Using Helper Objectives",
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, search-based software testing, MOEA",
author = "Pouria Derakhshanfar and Xavier Devroey and Andy Zaidman and {van Deursen}, Arie and Annibale Panichella",
note = "Virtual/online event due to COVID-19; Genetic and Evolutionary Computation Conference, GECCO ; Conference date: 08-07-2020 Through 12-07-2020",
year = "2020",
doi = "10.1145/3377929.3390077",
language = "English",
booktitle = "Genetic and Evolutionary Computation Conference Companion (GECCO {\textquoteright}20 Companion)",
publisher = "ACM DL",
url = "https://gecco-2020.sigevo.org/",

}

RIS

TY - GEN

T1 - Crash Reproduction Using Helper Objectives

AU - Derakhshanfar, Pouria

AU - Devroey, Xavier

AU - Zaidman, Andy

AU - van Deursen, Arie

AU - Panichella, Annibale

N1 - Conference code: 2020

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - crash reproduction

KW - search-based software testing

KW - MOEA

UR - https://youtu.be/GPXsWsjqaHc

U2 - 10.1145/3377929.3390077

DO - 10.1145/3377929.3390077

M3 - Conference contribution

BT - Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion)

PB - ACM DL

CY - Cancún, Mexico

T2 - Genetic and Evolutionary Computation Conference

Y2 - 8 July 2020 through 12 July 2020

ER -

ID: 72126236