CSFL: Fault Localization on Real Software Bugs Based on the Combination of Context and Spectrum

Yan, Yue; Jiang, Shujuan; Zhang, Shenggang; Huang, Ying

doi:10.1007/978-3-030-91265-9_12

Cited by 2 publications

(1 citation statement)

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“…Error localization in the context of automatic program repair has been a widely studied research topic [34] [8] [27] [28] [30]. Evaluation of approaches towards error localization requires the construction of a dataset with known ground-truth.…”

Section: Motivationmentioning

confidence: 99%

BigIssue: A Realistic Bug Localization Benchmark

Paul¹,

Nijkamp²,

Pang³

et al. 2022

Preprint

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As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code? With significant progress being achieved in natural language processing with models like GPT-3 and Bert, the applications of natural language processing techniques to code are starting to be explored. Most of the research has been focused on automatic program repair (APR), and while the results on synthetic or highly filtered datasets are promising, such models are hard to apply in real-world scenarios because of inadequate bug localization. We propose BigIssue: a benchmark for realistic bug localization. The goal of the benchmark is two-fold. We provide (1) a general benchmark with a diversity of real and synthetic Java bugs and (2) a motivation to improve bug localization capabilities of models through attention to the full repository context. With the introduction of BigIssue, we hope to advance the state of the art in bug localization, in turn improving APR performance and increasing its applicability to the modern development cycle.

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Section: Motivationmentioning

confidence: 99%