Poster: Automatically Fixing Real-World JavaScript Performance Bugs

Selakovic, Marija; Pradel, Michael

doi:10.1109/icse.2015.260

Cited by 8 publications

(5 citation statements)

References 6 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%

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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%

“…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%

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%

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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“…As previously mentioned, most of the papers describing real-world performance bugs specialize in issues related to a particular scope. Another example of a study focused on specific programming languages is the one presented by Selakovic et al [3], which proposed an approach for automatically finding and fixing performance bugs in JavaScript programs. They studied 37 real-world performance bug fixes from eleven popular JavaScript projects and identified several recurring fix patterns.…”

Section: Related Workmentioning

confidence: 99%

“…Preventing performance bugs, or implementing effective tools to detect and fix them, requires a wide understanding of the nature of these issues in real-world programs. On the search for realistic and documented performance bugs, researchers typically resort to mining software repositories and bug tracking systems [3]- [7]. However, this is a timeconsuming and error-prone task that inevitably requires the manual inspection of the issue's records, for example, to detect false positives (e.g., misclassified issues), and to understand the bug, its cause, the source code (if available), and the fix (if any) [8].…”

Section: Introductionmentioning

confidence: 99%

TANDEM: A Taxonomy and a Dataset of Real-World Performance Bugs

et al. 2020

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The detection of performance bugs, like those causing an unexpected execution time, has gained much attention in the last years due to their potential impact in safety-critical and resourceconstrained applications. Much effort has been put on trying to understand the nature of performance bugs in different domains as a starting point for the development of effective testing techniques. However, the lack of a widely accepted classification scheme of performance faults and, more importantly, the lack of well-documented and understandable datasets makes it difficult to draw rigorous and verifiable conclusions widely accepted by the community. In this paper, we present TANDEM, a dual contribution related to real-world performance bugs. Firstly, we propose a taxonomy of performance bugs based on a thorough systematic review of the related literature, divided into three main categories: effects, causes and contexts of bugs. Secondly, we provide a complete collection of fully documented real-world performance bugs. Together, these contributions pave the way for the development of stronger and reproducible research results on performance testing. INDEX TERMS performance bugs, performance testing, dataset, taxonomy.

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Poster: Automatically Fixing Real-World JavaScript Performance Bugs

Cited by 8 publications

References 6 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

TANDEM: A Taxonomy and a Dataset of Real-World Performance Bugs

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