Improving automated fault localization using PageRank

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Zhang, Mengshi

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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. A focus of existing SBFL techniques is to consider how to differentiate program source code 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 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 PRFL, a lightweight technique that boosts spectrum-based fault localization by differentiating tests using PageRank algorithm. Given the original program spectrum information, PRFL uses PageRank to recompute the spectrum information by considering the contributions of different tests. Then, traditional SBFL techniques can be applied on the recomputed spectrum information to achieve more effective fault localization. Although simple and lightweight, PRFL has been demonstrated to outperform state-of-the-art SBFL techniques significantly (e.g., ranking 42% more real faults within Top-1 compared with the most effective traditional SBFL technique) with low overhead (e.g., around 2 minute average extra overhead on real faults) on 332 real faults from 5 Defects4J projects and 30692 artificial (i.e., mutation) faults from 87 GitHub projects, demonstrating a promising future for considering the contributions of different tests during fault localization.


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