On the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs
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Abstract (arXiv)
Test-based automated program repair has been a prolific field of research in software engineering in the last decade. Many approaches have indeed been proposed, which leverage test suites as a weak, but affordable, approximation to program specifications. Although the literature regularly sets new records on the number of benchmark bugs that can be fixed, several studies increasingly raise concerns about the limitations and biases of state-of-the-art approaches. For example, the correctness of generated patches has been questioned in a number of studies, while other researchers pointed out that evaluation schemes may be misleading with respect to the processing of fault localization results. Nevertheless, there is little work addressing the efficiency of patch generation, with regard to the practicality of program repair. In this paper, we fill this gap in the literature, by providing an extensive review on the efficiency of test suite based program repair. Our objective is to assess the number of generated patch candidates, since this information is correlated to (1) the strategy to traverse the search space efficiently in order to select sensical repair attempts, (2) the strategy to minimize the test effort for identifying a plausible patch, (3) as well as the strategy to prioritize the generation of a correct patch. To that end, we perform a large-scale empirical study on the efficiency, in terms of quantity of generated patch candidates of the 16 open-source repair tools for Java programs. The experiments are carefully conducted under the same fault localization configurations to limit biases.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2020.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 2; 2024: 5; 2025: 4.
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- 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repaircited by 85
- 2021A Syntax-Guided Edit Decoder for Neural Program Repaircited by 57
- 2025Evaluating Agent-based Program Repair at Googlecited by 35
- 2025Process-Centric Analysis of Agentic Software Systemscited by 24
- 2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 23
- 2023ITER: Iterative Neural Repair for Multi-Location Patchescited by 16
- 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repaircited by 13
- 2024Enhancing Automated Program Repair with Solution Designcited by 8
- 2025Dissecting the SWE-Bench Leaderboards: Profiling Submitters and Architectures of LLM- and Agent-Based Repair Systemscited by 8
- 2022Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctnesscited by 7
- 2024Automated Program Repair: Emerging trends pose and expose problems for benchmarkscited by 7
- 2024Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugscited by 7
4 more
- 2024Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testingcited by 5
- 2024Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generationcited by 5
- 2021Predicting Patch Correctness Based on the Similarity of Failing Test Casescited by 4
- 2025Using ML filters to help automated vulnerability repairs: when it helps and when it doesn'tcited by 1
- 2025Process-Centric Analysis of Agentic Software Systems2 Dec 2025
- 2025Dissecting the SWE-Bench Leaderboards: Profiling Submitters and Architectures of LLM- and Agent-Based Repair Systems20 Jun 2025
- 2025Using ML filters to help automated vulnerability repairs: when it helps and when it doesn't9 Apr 2025
- 2025Evaluating Agent-based Program Repair at Google13 Jan 2025
- 2024Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation22 Oct 2024
- 2024Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs16 Sep 2024
- 2024Enhancing Automated Program Repair with Solution Design22 Aug 2024
- 2024Automated Program Repair: Emerging trends pose and expose problems for benchmarks8 May 2024
- 2024Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing19 Feb 2024
- 2023ITER: Iterative Neural Repair for Multi-Location Patches24 Apr 2023
- 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair3 Feb 2023
- 2022Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness8 Aug 2022
4 more
- 2021A Survey on Machine Learning Techniques for Source Code Analysis18 Oct 2021
- 2021Predicting Patch Correctness Based on the Similarity of Failing Test Cases28 Jul 2021
- 2021A Syntax-Guided Edit Decoder for Neural Program Repair15 Jun 2021
- 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repair26 Feb 2021
- 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repaircited by 85
- 2021A Syntax-Guided Edit Decoder for Neural Program Repaircited by 57
- 2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 23
- 2023ITER: Iterative Neural Repair for Multi-Location Patchescited by 16
- 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repaircited by 13
- 2024Enhancing Automated Program Repair with Solution Designcited by 8
- 2022Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctnesscited by 7
- 2024Automated Program Repair: Emerging trends pose and expose problems for benchmarkscited by 7
- 2024Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugscited by 7
- 2024Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testingcited by 5
- 2024Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generationcited by 5
- 2021Predicting Patch Correctness Based on the Similarity of Failing Test Casescited by 4
12 most cited of 13 All 13 (JSON)
- 2025Using ML filters to help automated vulnerability repairs: when it helps and when it doesn't9 Apr 2025
- 2024Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation22 Oct 2024
- 2024Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs16 Sep 2024
- 2024Enhancing Automated Program Repair with Solution Design22 Aug 2024
- 2024Automated Program Repair: Emerging trends pose and expose problems for benchmarks8 May 2024
- 2024Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing19 Feb 2024
- 2023ITER: Iterative Neural Repair for Multi-Location Patches24 Apr 2023
- 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair3 Feb 2023
- 2022Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness8 Aug 2022
- 2021A Survey on Machine Learning Techniques for Source Code Analysis18 Oct 2021
- 2021Predicting Patch Correctness Based on the Similarity of Failing Test Cases28 Jul 2021
- 2021A Syntax-Guided Edit Decoder for Neural Program Repair15 Jun 2021
12 newest of 13 All 13 (JSON)