Explaining Explanation: An Empirical Study on Explanation in Code
Reviews
- URL: http://arxiv.org/abs/2311.09020v1
- Date: Wed, 15 Nov 2023 15:08:38 GMT
- Title: Explaining Explanation: An Empirical Study on Explanation in Code
Reviews
- Authors: Ratnadira Widyasari, Ting Zhang, Abir Bouraffa, David Lo
- Abstract summary: This study is the first to analyze the types of explanations used in code review.
We analyzed code reviews based on their usefulness and labeled them based on whether they contained an explanation, a solution, or both a proposed solution and an explanation thereof.
We utilize large language models, specifically ChatGPT, to assist developers in getting a code review explanation that suits their preferences.
- Score: 12.934219954530755
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Code review is an important process for quality assurance in software
development. For an effective code review, the reviewers must explain their
feedback to enable the authors of the code change to act on them. However, the
explanation needs may differ among developers, who may require different types
of explanations. It is therefore crucial to understand what kind of
explanations reviewers usually use in code reviews. To the best of our
knowledge, no study published to date has analyzed the types of explanations
used in code review. In this study, we present the first analysis of
explanations in useful code reviews. We extracted a set of code reviews based
on their usefulness and labeled them based on whether they contained an
explanation, a solution, or both a proposed solution and an explanation
thereof.
Based on our analysis, we found that a significant portion of the code review
comments (46%) only include solutions without providing an explanation. We
further investigated the remaining 54% of code review comments containing an
explanation and conducted an open card sorting to categorize the reviewers'
explanations. We distilled seven distinct categories of explanations based on
the expression forms developers used. Then, we utilize large language models,
specifically ChatGPT, to assist developers in getting a code review explanation
that suits their preferences. Specifically, we created prompts to transform a
code review explanation into a specific type of explanation. Our evaluation
results show that ChatGPT correctly generated the specified type of explanation
in 88/90 cases and that 89/90 of the cases have the correct explanation.
Overall, our study provides insights into the types of explanations that
developers use in code review and showcases how ChatGPT can be leveraged during
the code review process to generate a specific type of explanation.
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