Enhancing Software Maintenance: A Learning to Rank Approach for Co-changed Method Identification

TL;DR

This paper introduces a learning-to-rank method leveraging source code features and change history to accurately identify co-changed methods at the pull request level, improving software maintenance efficiency.

Contribution

It presents a novel learning-to-rank approach combining code features and change history, outperforming existing baselines in co-changed method prediction at the pull request level.

Findings

Random Forest model outperforms others by 2.5-12.8% in NDCG@5

Our approach surpasses baselines like file proximity and code clones by up to 537.5%

Bi-monthly retraining maintains high accuracy, with performance declining after 60 days

Abstract

With the increasing complexity of large-scale software systems, identifying all necessary modifications for a specific change is challenging. Co-changed methods, which are methods frequently modified together, are crucial for understanding software dependencies. However, existing methods often produce large results with high false positives. Focusing on pull requests instead of individual commits provides a more comprehensive view of related changes, capturing essential co-change relationships. To address these challenges, we propose a learning-to-rank approach that combines source code features and change history to predict and rank co-changed methods at the pull-request level. Experiments on 150 open-source Java projects, totaling 41.5 million lines of code and 634,216 pull requests, show that the Random Forest model outperforms other models by 2.5 to 12.8 percent in NDCG@5. It also surpasses baselines such as file proximity, code clones, FCP2Vec, and StarCoder 2 by 4.7 to 537.5 percent. Models trained on longer historical data (90 to 180 days) perform consistently, while accuracy declines after 60 days, highlighting the need for bi-monthly retraining. This approach provides an effective tool for managing co-changed methods, enabling development teams to handle dependencies and maintain software quality.