Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives
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.
Tue 22 Sep Times are displayed in time zone: (UTC) Coordinated Universal Time
09:10 - 10:10: Search-Based TestingResearch Papers / Journal-first Papers / Tool Demonstrations at Wombat Chair(s): Maria KechagiaUniversity College London | |||
| 09:10 - 09:30 Talk | Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives Research Papers Pouria DerakhshanfarDelft University of Technology, Xavier DevroeyDelft University of Technology, Andy ZaidmanDelft University of Technology, Arie van DeursenDelft University of Technology, Netherlands, Annibale PanichellaDelft University of Technology DOI Pre-print Media Attached | ||
| 09:30 - 09:50 Talk | Multi-criteria test cases selection for model transformations Journal-first Papers Bader AlkhaziKuwait University, Chaima AbidUniversity of Michigan, Marouane KessentiniUniversity of Michigan, Dorian LeroyJKU Linz, Manuel WimmerJohannes Kepler University Linz Link to publication DOI | ||
| 09:50 - 10:00 Talk | Botsing, a Search-based Crash Reproduction Framework for Java Tool Demonstrations Pouria DerakhshanfarDelft University of Technology, Xavier DevroeyDelft University of Technology, Annibale PanichellaDelft University of Technology, Andy ZaidmanDelft University of Technology, Arie van DeursenDelft University of Technology, Netherlands DOI Pre-print Media Attached | ||
