On the Effectiveness of Unified Debugging: An Extensive Study on 16 Program Repair Systems
Samuel Benton, Xia Li, Yiling Lou, Lingming Zhang
Abstract
Automated debugging techniques, including fault localization and program repair, have been studied for over a decade. However, the only existing connection between fault localization and program repair is that fault localization computes the potential buggy elements for program repair to patch. Recently, a pioneering work, ProFL, explored the idea of unified debugging to unify fault localization and program repair in the other direction for thefi rst time to boost both areas. More specifically, ProFL utilizes the patch execution results from one state-of-the-art repair system, PraPR, to help improve state-of-the-art fault localization. In this way, ProFL not only improves fault localization for manual repair, but also extends the application scope of automated repair to all possible bugs (not only the small ratio of bugs that can be automaticallyfi xed). However, ProFL only considers one APR system (i.e., PraPR), and it is not clear how other existing APR systems based on different designs contribute to unified debugging. In this work, we perform an extensive study of the unified-debugging approach on 16 state-of-the-art program repair systems for thefi rst time. Our experimental results on the widely studied Defects4J benchmark suite reveal various practical guidelines for unified debugging, such as (1) nearly all the studied 16 repair systems can positively contribute to unified debugging despite their varying repairing capabilities, (2) repair systems targeting multi-edit patches can bring extraneous noise into unified debugging, (3) repair systems with more executed/plausible patches tend to perform better for unified debugging, and (4) unified debugging effectiveness does not rely on the availability of correct patches in automated repair. Based on our results, we further propose an advanced unified debugging technique, UniDebug++, which can localize over 20% more bugs within Top-1 positions than state-of-the-art unified debugging technique, ProFL. * This work was mainly done when they are (visiting) PhD students at UT Dallas.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext f518c132-afd8-457a-9f52-d7929c9d4bccCited by top-tier papers14
- Less training, more repairing please: revisiting automated program repair via zero-shot learningChunqiu Steven Xia, Lingming ZhangFSE 2022 · 223 citations
- Boosting coverage-based fault localization via graph-based representation learningYiling Lou, Qihao Zhu, Jinhao Dong, Xia Li et al.FSE 2021 · 157 citations
- Automated Program Repair via Conversation: Fixing 162 out of 337 Bugs for $0.42 Each using ChatGPTChunqiu Steven Xia, Lingming ZhangISSTA 2024 · 105 citations
- Automated Patch Correctness Assessment: How Far are We?Shangwen Wang, Ming Wen, Bo Lin, Hongjun Wu et al.ASE 2020 · 77 citations
- Gamma: Revisiting Template-Based Automated Program Repair Via Mask PredictionQuanjun Zhang, Chunrong Fang, Tongke Zhang, Bowen Yu et al.ASE 2023 · 44 citations
Builds on4
- On the efficiency of test suite based program repair: A Systematic Assessment of 16 Automated Repair Systems for Java ProgramsKui Liu, Shangwen Wang, Anil Koyuncu, Kisub Kim et al.ICSE 2020 · 116 citations
- Can automated program repair refine fault localization? a unified debugging approachYiling Lou, Ali Ghanbari, Xia Li, Lingming Zhang et al.ISSTA 2020 · 99 citations
- Automated Patch Correctness Assessment: How Far are We?Shangwen Wang, Ming Wen, Bo Lin, Hongjun Wu et al.ASE 2020 · 77 citations
- Fast and Precise On-the-fly Patch Validation for AllLingchao Chen, Yicheng Ouyang, Lingming ZhangICSE 2021 · 24 citations
Related papers
- Towards Boosting Patch Execution On-the-FlySamuel Benton, Yuntong Xie, Lan Lu, Mengshi Zhang et al.ICSE 2022 · 10 citations
- Better Automatic Program Repair by Using Bug Reports and Tests TogetherManish Motwani, Yuriy BrunICSE 2023 · 22 citations
- A Bayesian Framework for Automated DebuggingSungmin Kang, Wonkeun Choi, Shin YooISSTA 2023 · 1 citation
- ITER: Iterative Neural Repair for Multi-Location PatchesHe Ye, Martin MonperrusICSE 2024 · 39 citations
- Patch correctness assessment in automated program repair based on the impact of patches on production and test codeAli Ghanbari, Andrian MarcusISSTA 2022 · 26 citations
