Automatic method change suggestion to complement multi-entity edits

Jiang, Zijian; Wang, Ye; Zhong, Hao; Meng, Na

doi:10.1016/j.jss.2019.110441

Cited by 8 publications

(8 citation statements)

References 7 publications

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“…Wang et al recently conducted a similar study on multi-entity edits to Java code [69], and developed CMSuggester-an automatic approach to suggest method changes accordingly [41,68]. Although our characterization study focuses on JS instead of Java code repositories, it corroborates the observations made by Wang et al.…”

Section: Co-change Recommendation Systemssupporting

confidence: 85%

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Investigating and recommending co-changed entities for JavaScript programs

Jiang

Zhong

Meng

2021

Journal of Systems and Software

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JavaScript (JS) is one of the most popular programming languages due to its flexibility and versatility, but debugging 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) 50% of the crawled commits involve multientity edits (i.e., edits that touch multiple entities simultaneously); (2) three recurring patterns commonly exist in all projects; and (3) 80-90% of co-changed function pairs either invoke the same function(s), access the same variable(s), or contain similar statement(s); and (4) our ML-based approach CoRec recommended entity changes with high accuracy. This research will improve programmer productivity and software quality.

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Section: Co-change Recommendation Systemssupporting

confidence: 85%

“…Therefore, the imperfect evaluation data set based on developers' edits may influence our empirical comparison between CoRec and ROSE. We share this limitation with prior work [39,41,47,60,67,68,73]. In the future, we plan to mitigate the problem by conducting user studies with developers.…”

Section: Threats To Validitymentioning

confidence: 91%

Investigating and recommending co-changed entities for JavaScript programs

Jiang

Zhong

Meng

2021

Journal of Systems and Software

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“…Therefore, the imperfect evaluation data set based on developers' edits may influence our empirical comparison between CoRec and ROSE. We share this limitation with prior work [5,39,9,41,42,8,7]. In the future, we plan to mitigate the problem by conducting user studies with developers.…”

Section: Threats To Validitymentioning

confidence: 95%

“…To reduce the cost of maintaining software, researchers proposed approaches that recommend code co-changes [5,6,7,8]. For instance, Zimmermann et al [5] and Rolfsnes et al [6] mined co-change patterns of program entities from software version history and suggested co-changes accordingly.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Zimmermann et al [5] and Rolfsnes et al [6] mined co-change patterns of program entities from software version history and suggested co-changes accordingly. Wang et al [7,8] studied the co-change patterns of Java program entities and built CMSuggester to suggest changes accordingly for any given program commit. However, existing tools do not characterize any co-change patterns between JS software entities, neither do they recommend changes by considering the unique language features of JS or the mined co-changed patterns from JS programs (see Section 8.3) for detailed discussions).…”

Section: Introductionmentioning

confidence: 99%

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Investigating and Recommending Co-Changed Entities for JavaScript Programs

Jiang

Zhong

Meng

2021

Preprint

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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 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.

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