Crash Reproduction Using Helper Objectives

Conference Paper (2020)
Author(s)

P. Derakhshanfar (TU Delft - Software Engineering)

Xavier Devroey (TU Delft - Software Engineering)

Andy Zaidman (TU Delft - Software Engineering)

A. Van Deursen (TU Delft - Software Technology)

Annibale Panichella (TU Delft - Software Engineering)

Research Group
Software Engineering
Copyright
© 2020 P. Derakhshanfar, Xavier Devroey, A.E. Zaidman, A. van Deursen, A. Panichella
DOI related publication
https://doi.org/10.1145/3377929.3390077
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 P. Derakhshanfar, Xavier Devroey, A.E. Zaidman, A. van Deursen, A. Panichella
Research Group
Software Engineering
Pages (from-to)
309-310
ISBN (electronic)
9781450371278
Reuse Rights

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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.

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