Detecting unknown inconsistencies in web applications

Ocariza, Frolin S.; Pattabiraman, Karthik; Mesbah, Ali

doi:10.1109/ase.2017.8115667

Cited by 8 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ocariza et al [23] propose an inconsistency detection technique for MVC-based JavaScript applications which is evaluated on 18 bugs from 12 web applications (7k LOC). A related work [24] uses 15 bugs in 20 applications (nearly 1M LOC).…”

Section: A Bug-related Studies For Javascriptmentioning

confidence: 99%

BugsJS: a Benchmark of JavaScript Bugs

Gyimesi

Vancsics

Stocco

et al. 2019

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)

Self Cite

View full text Add to dashboard Cite

JavaScript is a popular programming language that is also error-prone due to its asynchronous, dynamic, and loosely-typed nature. In recent years, numerous techniques have been proposed for analyzing and testing JavaScript applications. However, our survey of the literature in this area revealed that the proposed techniques are often evaluated on different datasets of programs and bugs. The lack of a commonly used benchmark limits the ability to perform fair and unbiased comparisons for assessing the efficacy of new techniques. To fill this gap, we propose BUGSJS, a benchmark of 453 real, manually validated JavaScript bugs from 10 popular JavaScript server-side programs, comprising 444k LOC in total. Each bug is accompanied by its bug report, the test cases that detect it, as well as the patch that fixes it. BUGSJS features a rich interface for accessing the faulty and fixed versions of the programs and executing the corresponding test cases, which facilitates conducting highly-reproducible empirical studies and comparisons of JavaScript analysis and testing tools.

show abstract

Section: A Bug-related Studies For Javascriptmentioning

confidence: 99%

BugsJS: a Benchmark of JavaScript Bugs

Gyimesi

Vancsics

Stocco

et al. 2019

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ocariza et al [26] propose an inconsistency detection technique for model-view-controllerbased JS applications which is evaluated on 18 bugs from 12 web applications (7k LOC). A related work [27] uses 15 bugs in 20 applications (nearly 1MLOC).…”

Section: Bug-related Studies For Javascriptmentioning

confidence: 99%

BUGSJS: a benchmark and taxonomy of JavaScript bugs

Gyimesi

Vancsics

Stocco

et al. 2020

Software Testing Verif & Rel

Self Cite

View full text Add to dashboard Cite

JavaScript is a popular programming language that is also error-prone due to its asynchronous, dynamic, and loosely typed nature. In recent years, numerous techniques have been proposed for analyzing and testing JavaScript applications. However, our survey of the literature in this area revealed that the proposed techniques are often evaluated on different datasets of programs and bugs. The lack of a commonly used benchmark limits the ability to perform fair and unbiased comparisons for assessing the efficacy of new techniques. To fill this gap, we propose BUGSJS, a benchmark of 453 real, manually validated JavaScript bugs from 10 popular JavaScript server-side programs, comprising 444k lines of code (LOC) in total. Each bug is accompanied by its bug report, the test cases that expose it, as well as the patch that fixes it. We extended BUGSJS with a rich web interface for visualizing and dissecting the bugs' information, as well as a programmable API to access the faulty and fixed versions of the programs and to execute the corresponding test cases, which facilitates conducting highly reproducible empirical studies and comparisons of JavaScript analysis and testing tools. Moreover, following a rigorous procedure, we performed a classification of the bugs according to their nature. Our internal validation shows that our taxonomy is adequate for characterizing the bugs in BUGSJS. We discuss several ways in which the resulting taxonomy and the benchmark can help direct researchers interested in automated testing of JavaScript applications.

show abstract

“…In prior research, the first author of this work has proposed AUTOFLOX and VEJOVIS, which are automated techniques for localizing and repairing functional issues in client-side JavaScript, particularly those involving the DOM [35,36]. Furthermore, the first author has worked on designs for detecting inconsistencies in JavaScript-based web applications that use Model-View-Controller (MVC) frameworks [37,38]. In addition, Artzi et al [39] and Samimi et al [40] have both worked on techniques for automatically localizating HTML validation errors, while Pradel et al [41] proposed an approach for analyzing type inconsistency in JavaScript.…”

Section: Localizing Software Bugsmentioning

confidence: 99%

Localizing software performance regressions in web applications by comparing execution timelines

Ocariza¹,

Zhao²

2020

Software Testing Verif & Rel

Self Cite

View full text Add to dashboard Cite

A performance regression in software is defined as an increase in an application step's response time as a result of code changes. Detecting such regressions can be done using profiling tools; however, investigating their root cause is a mostly-manual and time-consuming task. This statement holds true especially when comparing execution timelines, which are dynamic function call trees augmented with response time data; these timelines are compared to find the performance regression-causesthe lowest-level function calls that regressed during execution. When done manually, these comparisons often require the investigator to analyze thousands of function call nodes. Further, performing these comparisons on web applications is challenging due to JavaScript's asynchronous and event-driven model, which introduce noise in the timelines. In response, we propose a design -ZAMthat automatically compares execution timelines collected from web applications, to identify performance regression-causes. Our approach uses a hybrid node matching algorithm that recursively attempts to find the longest common subsequence in each call tree level, then aggregates multiple comparisons' results to eliminate noise. Our evaluation of ZAM on 10 web applications indicates that it can identify performance regression-causes with a path recall of 100% and a path precision of 96%, while performing comparisons in under a minute on average. We also demonstrate the real-world applicability of ZAM, which has been used to successfully complete performance investigations by the performance and reliability team in SAP.

show abstract

Detecting unknown inconsistencies in web applications

Cited by 8 publications

References 38 publications

BugsJS: a Benchmark of JavaScript Bugs

BugsJS: a Benchmark of JavaScript Bugs

BUGSJS: a benchmark and taxonomy of JavaScript bugs

Localizing software performance regressions in web applications by comparing execution timelines

Contact Info

Product

Resources

About