Ultra-Large Repair Search Space with Automatically Mined Templates: the Cardumen Mode of Astor
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Abstract (arXiv)
Astor is a program repair library which has different modes. In this paper, we present the Cardumen mode of Astor, a repair approach based mined templates that has an ultra-large search space. We evaluate the capacity of Cardumen to discover test-suite adequate patches (aka plausible patches) over the 356 real bugs from Defects4J. Cardumen finds 8935 patches over 77 bugs of Defects4J. This is the largest number of automatically synthesized patches ever reported, all patches being available in an open-science repository. Moreover, Cardumen identifies 8 unique patches, that are patches for Defects4J bugs that were never repaired in the whole history of program repair.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2017.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 1; 2025: 4.
9 papers.
- 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repaircited by 86
- 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repaircited by 13
- 2023RunBugRun -- An Executable Dataset for Automated Program Repaircited by 11
- 2024Enhanced Automated Code Vulnerability Repair using Large Language Modelscited by 10
- 2025The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Modelscited by 7
- 2025Repair-R1: Better Test Before Repaircited by 6
- 2021Predicting Patch Correctness Based on the Similarity of Failing Test Casescited by 4
- 2025From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMscited by 1
- 2025Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repaircited by 0
- 2025Repair-R1: Better Test Before Repair30 Jul 2025
- 2025From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs16 Jun 2025
- 2025Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair12 May 2025
- 2025The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models5 May 2025
- 2024Enhanced Automated Code Vulnerability Repair using Large Language Models8 Jan 2024
- 2023RunBugRun -- An Executable Dataset for Automated Program Repair3 Apr 2023
- 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair3 Feb 2023
- 2021Predicting Patch Correctness Based on the Similarity of Failing Test Cases28 Jul 2021
- 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repair26 Feb 2021