Investigating and Recommending Co-Changed Entities for JavaScript Programs

TL;DR

This paper studies co-changed entities in JavaScript programs, identifies common patterns, and develops a machine learning approach to recommend related code changes, improving maintenance efficiency.

Contribution

It introduces a novel ML-based method, CoRec, that leverages co-change patterns and code features to recommend entities for editing during software maintenance.

Findings

Identified three recurring co-change patterns across projects.

80-90% of co-changed function pairs share invocation, variable access, or similar statements.

CoRec achieved 73-78% accuracy, outperforming existing tools.

Abstract

JavaScript (JS) is one of the most popular programming languages due to its flexibility and versatility, but maintaining JS code is tedious and error-prone. In our research, we conducted an empirical study to characterize the relationship between co-changed software entities (e.g., functions and variables), and built a machine learning (ML)-based approach to recommend additional entity to edit given developers' code changes. Specifically, we first crawled 14,747 commits in 10 open-source projects; for each commit, we created one or more change dependency graphs (CDGs) to model the referencer-referencee relationship between co-changed entities. Next, we extracted the common subgraphs between CDGs to locate recurring co-change patterns between entities. Finally, based on those patterns, we extracted code features from co-changed entities and trained an ML model that recommends entities-to-change given a program commit. According to our empirical investigation, (1) three recurring patterns commonly exist in all projects; (2) 80%--90% of co-changed function pairs either invoke the same function(s), access the same variable(s), or contain similar statement(s); (3) our ML-based approach CoRec recommended entity changes with high accuracy (73%--78%). CoRec complements prior work because it suggests changes based on program syntax, textual similarity, as well as software history; it achieved higher accuracy than two existing tools in our evaluation.