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

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

Ensemble de données


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


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 mise à disposition11 août 2020
Date de la production de données11 août 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., sept. 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).

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