Crash Reproduction Using Helper Objectives

Crash Reproduction Using Helper Objectives

Derakhshanfar, P. (TU Delft Software Engineering)
Devroey, Xavier (TU Delft Software Engineering)
Zaidman, A.E. (TU Delft Software Engineering)
van Deursen, A. (TU Delft Software Technology)
Panichella, A. (TU Delft Software Engineering)

Software Technology

2020

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.

Crash reproduction
MOEA
Search-based software testing

http://resolver.tudelft.nl/uuid:25f23a1b-802f-4dad-a56b-a506b3ef7cec

https://doi.org/10.1145/3377929.3390077

ACM DL, Cancún, Mexico

2022-07-01

9781450371278

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

Genetic and Evolutionary Computation Conference, 2020-07-08 → 2020-07-12, Cancún, Mexico

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

Virtual/online event due to COVID-19

Institutional Repository

conference paper

© 2020 P. Derakhshanfar, Xavier Devroey, A.E. Zaidman, A. van Deursen, A. Panichella