Replication package of "Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives"

  • Pouria Derakhshanfar (Creator)
  • Xavier Devroey (Creator)
  • Andy Zaidman (Creator)
  • Arie van Deursen (Creator)
  • Annibale Panichella (Creator)

Dataset

Description

The replication package for the study about using new helper objectives (MOHO) for crash reproduction. This study has been accepted at ASE 2020.

Abstract:

Evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called Crash Distance to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called MO-HO, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely test length (to minimize) and method sequence diversity (to maximize), in addition to Crash Distance.

We assessed MO-HO using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 hard-to-reproduce crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for MO-HO.

We evaluated this best-performing algorithm for MO-HO against the state-of-the-art: single-objective approach (Single-Objective Search) and decomposition-based multi-objectivization approach (De-MO). Our results show that MO-HO reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, MO-HO improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-Objective Search and De-MO, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time.
Date made available11 Aug 2020
PublisherZenodo
Date of data production11 Aug 2020
  • Good Things Come in Threes: Improving Search-based Crash Reproduction with Helper Objectives

    Derakhshanfar, P., Devroey, X., Zaidman, A., Van Deursen, A. & Panichella, A., Sep 2020, Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020. ACM Press, p. 211-223 13 p. 9285999. (Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020).

    Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

    Open Access

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