Resolving ambiguity in code refinement via conidfine: A conversationally-Aware framework with disambiguation and targeted retrieval

Abstract

Code refinement is a vital aspect of software development, involving the review and enhancement of code contributions made by developers. A critical challenge in this process arises from unclear or ambiguous review comments, which can hinder developers’ understanding of the required changes. Our preliminary study reveals that conversations between developers and reviewers often contain valuable information that can help resolve such ambiguous review suggestions. However, leveraging conversational data to address this issue poses two key challenges: (1) enabling the model to autonomously determine whether a review suggestion is ambiguous, and (2) effectively extracting the relevant segments from the conversation that can aid in resolving the ambiguity.

In this paper, we propose a novel method for addressing ambiguous review suggestions by leveraging conversations between reviewers and developers. To tackle the above two challenges, we introduce an Ambiguous Discriminator that uses multi-task learning to classify ambiguity and generate type-aware confusion points from a GPT-4-labeled dataset. These confusion points guide a Type-Driven Multi-Strategy Retrieval Framework that applies targeted strategies based on categories like Inaccurate Localization, Unclear Expression, and Lack of Specific Guidance to extract actionable information from the conversation context. To support this, we construct a semantic auxiliary instruction library containing spatial indicators, clarification patterns, and action-oriented verbs, enabling precise alignment between review suggestions and informative conversation segments. Our method is evaluated on two widely-used code refinement datasets CodeReview and CodeReview-New, where we demonstrate that our method significantly enhances the performance of various state-of-the-art models, including TransReview, T5-Review, CodeT5, CodeReviewer and ChatGPT. Furthermore, we explore in depth how conversational information improves the model’s ability to address fine-grained situations, and we conduct human evaluations to assess the accuracy of ambiguity detection and the correctness of generated confusion points. We are the first to introduce the issue of ambiguous review suggestions in the code refinement domain and propose a solution that not only addresses these challenges but also sets the foundation for future research. Our method provides valuable insights into improving the clarity and effectiveness of review suggestions, offering a promising direction for advancing code refinement techniques.