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.

Publication
Genetic and Evolutionary Computation Conference Companion (GECCO ‘20 Companion)
Xavier Devroey
Xavier Devroey
Postdoctoral Researcher

My research interests include search-based and model-based software testing, test suite augmentation, DevOps, and variability-intensive systems engineering.

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