Automatic Clone Recommendation for Refactoring Based on the Present and the Past

TL;DR

CREC is a learning-based approach that leverages extensive features from current and historical software data to recommend code clones for refactoring, significantly outperforming existing methods.

Contribution

Introduces CREC, the largest feature set for clone recommendation, and demonstrates its effectiveness across multiple large projects with high F-scores.

Findings

CREC achieved 83% and 76% F-scores in within-project and cross-project evaluations.

CREC outperformed the state-of-the-art approach with 70% and 50% F-scores.

Different factors and algorithms were analyzed for effectiveness.

Abstract

When many clones are detected in software programs, not all clones are equally important to developers. To help developers refactor code and improve software quality, various tools were built to recommend clone-removal refactorings based on the past and the present information, such as the cohesion degree of individual clones or the co-evolution relations of clone peers. The existence of these tools inspired us to build an approach that considers as many factors as possible to more accurately recommend clones. This paper introduces CREC, a learning-based approach that recommends clones by extracting features from the current status and past history of software projects. Given a set of software repositories, CREC first automatically extracts the clone groups historically refactored (R-clones) and those not refactored (NR-clones) to construct the training set. CREC extracts 34 features to characterize the content and evolution behaviors of individual clones, as well as the spatial, syntactical, and co-change relations of clone peers. With these features, CREC trains a classifier that recommends clones for refactoring. We designed the largest feature set thus far for clone recommendation, and performed an evaluation on six large projects. The results show that our approach suggested refactorings with 83% and 76% F-scores in the within-project and cross-project settings. CREC significantly outperforms a state-of-the-art similar approach on our data set, with the latter one achieving 70% and 50% F-scores. We also compared the effectiveness of different factors and different learning algorithms.