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An Empirical Study of Boosting Spectrum-Based Fault Localization via PageRank

Zhang, Mengshi ; Li, Yaoxian ; Li, Xia ; Chen, Lingchao ; Zhang, Yuqun ; Zhang, Lingming ; Khurshid, Sarfraz

IEEE transactions on software engineering, 2021-06, Vol.47 (6), p.1089-1113 [Periódico revisado por pares]

New York: IEEE

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  • Título:
    An Empirical Study of Boosting Spectrum-Based Fault Localization via PageRank
  • Autor: Zhang, Mengshi ; Li, Yaoxian ; Li, Xia ; Chen, Lingchao ; Zhang, Yuqun ; Zhang, Lingming ; Khurshid, Sarfraz
  • Assuntos: automated debugging ; Benchmark testing ; Boosting ; Debugging ; Fault location ; Java ; Lightweight ; Localization ; Manuals ; Mutation ; PageRank analysis ; SBFL ; Search algorithms ; Software testing ; Spectral analysis ; spectrum-based fault localization
  • É parte de: IEEE transactions on software engineering, 2021-06, Vol.47 (6), p.1089-1113
  • Descrição: Manual debugging is notoriously tedious and time-consuming. Therefore, various automated fault localization techniques have been proposed to help with manual debugging. Among the existing fault localization techniques, spectrum-based fault localization (SBFL) is one of the most widely studied techniques due to being lightweight. The focus of the existing SBFL techniques is to consider how to differentiate program entities (i.e., one dimension in program spectra); indeed, this focus is aligned with the ultimate goal of finding the faulty lines of code. Our key insight is to enhance the existing SBFL techniques by additionally considering how to differentiate tests (i.e., the other dimension in program spectra), which, to the best of our knowledge, has not been studied in prior work. We present our basic approach, PRFL, a lightweight technique that boosts SBFL by differentiating tests using PageRank algorithm. Specifically, given the original program spectrum information, PRFL uses PageRank to recompute the spectrum by considering the contributions of different tests. Next, traditional SBFL techniques are applied on the recomputed spectrum to achieve more effective fault localization. On top of PRFL, we explore PRFL+ and PRFL_{\mathrm{MA}} MA , two variants which extend PRFL by optimizing its components and integrating Method-level Aggregation technique, respectively. Though being simple and lightweight, PRFL has been demonstrated to outperform state-of-the-art SBFL techniques significantly (e.g., ranking 39.2% / 82.3% more real/artificial faults at Top-1 compared with the most effective traditional SBFL technique) with low overhead (e.g., around 6 minutes average extra overhead on real faults) on 395 real faults from 6 Defects4J projects and 96925 artificial (i.e., mutation) faults from 240 GitHub projects. To further validate PRFL's effectiveness, we compare PRFL with multiple recent proposed fault localization techniques (e.g., Multric, Metallaxis and MBFL-hybrid-avg), and the experimental results show that PRFL outperforms them as well. Furthermore, we study the performance of PRFL_{\mathrm{MA}} MA , and the experimental results present it can locate 137 real faults (73.4% / 24.5% more compared with the most effective SBFL/PRFL technique) and 35058 artificial faults (159.6% / 28.1% more than SBFL/PRFL technique) at Top-1. At last, we study the generalizability of PRFL on another benchmark, Bugs.jar, and the result shows PRFL can help locate around 30 percent more faults at Top 1.
  • Editor: New York: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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