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Investigating and recommending co-changed entities for JavaScript programs
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DOI：10.1016/j.jss.2021.111027.png)
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摘要
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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 at least one change dependency graph (CDG) 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. (C) 2021 Elsevier Inc. All rights reserved.
Keyword:
Multi-entity edit
Change suggestion
Machine learning
JavaScript
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