Using only 1,000 fine-tuning examples with full fine-tuning improves the number of plausible repairs by up to 78%, and iterative generation strategies help base models most on complex benchmarks like Defects4J.
ITER: Iterative Neural Repair for Multi-Location Patches
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abstract
Automated program repair (APR) has achieved promising results, especially using neural networks. Yet, the overwhelming majority of patches produced by APR tools are confined to one single location. When looking at the patches produced with neural repair, most of them fail to compile, while a few uncompilable ones go in the right direction. In both cases, the fundamental problem is to ignore the potential of partial patches. In this paper, we propose an iterative program repair paradigm called ITER founded on the concept of improving partial patches until they become plausible and correct. First, ITER iteratively improves partial single-location patches by fixing compilation errors and further refining the previously generated code. Second, ITER iteratively improves partial patches to construct multi-location patches, with fault localization re-execution. ITER is implemented for Java based on battle-proven deep neural networks and code representation. ITER is evaluated on 476 bugs from 10 open-source projects in Defects4J 2.0. ITER succeeds in repairing 15.5% of them, including 9 uniquely repaired multi-location bugs.
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The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models
Using only 1,000 fine-tuning examples with full fine-tuning improves the number of plausible repairs by up to 78%, and iterative generation strategies help base models most on complex benchmarks like Defects4J.