Catch &amp; Release: An Approach to Debugging Distributed Full-Stack JavaScript Applications

An, Kyungwon; Tilevich, Eli

doi:10.1007/978-3-030-19274-7_32

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Bug detection tools leverage static and/or dynamic analysis to identify code matches for any predefined bug pattern, in order to reveal data races [58], performance issues [70], API misuse [54], or DOMrelated errors [52]. Change suggestion tools recommend fixes for logic errors and performance bugs either based on predefined patch patterns [27,63] or based on fixes extracted from codebases [50] or technical discussion forums (e.g., StackOverflow [21,48]).…”

Section: Dedicationmentioning

confidence: 99%

“…Some other researchers developed tools to suggest bug fixes or code refactorings [27,32,40,46,48,50,63]. With more details, Vejovis suggests program repairs for DOM-related JS bugs based on two common fix patterns: parameter replacements and DOM element validations [50].…”

Section: Js Bug Detectorsmentioning

confidence: 99%

“…Given a crowd bug, the debugger sends a code query to a server and retrieves all StackOverflow answers potentially related to the bug fix. An and Tilevich built a JS refactoring tool to facilitate JS debugging and program repair [27]. Given a distributed JS application, the tool first converts the program to a semantically equivalent centralized version by gluing together the client and server parts.…”

Section: Js Bug Detectorsmentioning

confidence: 99%

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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: Dedicationmentioning

confidence: 99%

Section: Js Bug Detectorsmentioning

confidence: 99%

Section: Js Bug Detectorsmentioning

confidence: 99%

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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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“…Some other researchers developed tools to suggest bug fixes or code refactorings [57,58,59,60,61,62,63]. With more details, Vejovis suggests program repairs for DOM-related JS bugs based on two common fix patterns: parameter replacements and DOM element validations [60].…”

Section: Js Bug Detectorsmentioning

confidence: 99%

“…Given a crowd bug, the debugger sends a code query to a server and retrieves all StackOverflow answers potentially related to the bug fix. An and Tilevich built a JS refactoring tool to facilitate JS debugging and program repair [63]. Given a distributed JS application, the tool first converts the program to a semantically equivalent centralized version by gluing together the client and server parts.…”

Section: Js Bug Detectorsmentioning

confidence: 99%

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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Facilitating the Evolutionary Modifications in Distributed Apps via Automated Refactoring

2019

Lecture Notes in Computer Science

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Catch & Release: An Approach to Debugging Distributed Full-Stack JavaScript Applications

Cited by 8 publications

References 17 publications

Investigating and recommending co-changed entities for JavaScript programs

Investigating and recommending co-changed entities for JavaScript programs

Investigating and Recommending Co-Changed Entities for JavaScript Programs

Facilitating the Evolutionary Modifications in Distributed Apps via Automated Refactoring

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