Better patching using LLM prompting, via Self-Consistency

• URL: http://arxiv.org/abs/2306.00108v2
• Date: Wed, 16 Aug 2023 21:28:58 GMT
• Title: Better patching using LLM prompting, via Self-Consistency
• Authors: Toufique Ahmed, Premkumar Devanbu
• Abstract summary: Self-consistency (S-C) is an exciting, substantially better technique for generating explanations for problems. This paper describes an application of the S-C approach to program repair, using the commit log on the fix as the explanation. We achieve state-of-the art results, beating previous approaches to prompting-based program repair on the MODIT dataset.
• Score: 5.892272127970584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language models (LLMs) can be induced to solve non-trivial problems with "few-shot" prompts including illustrative problem-solution examples. Now if the few-shots also include "chain of thought" (CoT) explanations, which are of the form problem-explanation-solution, LLMs will generate a "explained" solution, and perform even better. Recently an exciting, substantially better technique, self-consistency [1] (S-C) has emerged, based on the intuition that there are many plausible explanations for the right solution; when the LLM is sampled repeatedly to generate a pool of explanation-solution pairs, for a given problem, the most frequently occurring solutions in the pool (ignoring the explanations) tend to be even more likely to be correct! Unfortunately, the use of this highly-performant S-C (or even CoT) approach in software engineering settings is hampered by the lack of explanations; most software datasets lack explanations. In this paper, we describe an application of the S-C approach to program repair, using the commit log on the fix as the explanation, only in the illustrative few-shots. We achieve state-of-the art results, beating previous approaches to prompting-based program repair, on the MODIT dataset; we also find evidence suggesting that the correct commit messages are helping the LLM learn to produce better patches.

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