Feedback-directed instrumentation for deployed JavaScript applications

Madsen, Magnus; Tip, Frank; Andreasen, Esben; Sen, Koushik; Möller, Anders

doi:10.1145/2884781.2884846

Cited by 19 publications

(15 citation statements)

References 33 publications

(40 reference statements)

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“…Despite its popularity, the intrinsic characteristics of JavaScript-such as weak typing, prototypal inheritance, and run-time evaluation-make it one of the most error-prone programming languages. As such, a large body of software engineering research has focused on the analysis and testing of JavaScript web applications [2,3,4,5,6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Instances of such performance benchmarks are JetStream, 4 Kraken, 5 Dromaeo, 6 Octane, 7 and V8. 8 In this work, however, we refer to benchmark as a collection of JavaScript programs and artifacts (e.g., test cases or bug reports) used to support empirical studies (e.g., controlled experiments or user studies) related to one or more research areas in software analysis and testing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

BugsJS: a Benchmark of JavaScript Bugs

Gyimesi

Vancsics

Stocco

et al. 2019

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

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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)

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“…This overhead is relatively small compared with the recent scalable dynamic analysis techniques. For example, the feedback-directed instrumentation technique for computing crash paths [37] imposed 100%-800% overhead in most cases, and the record and replay system [38] involved 480% overhead on average.…”

Section: Answer To Rqmentioning

confidence: 99%

Identifying Core Objects for Trace Summarization by Analyzing Reference Relations and Dynamic Properties

Noda

Kobayashi

Atsumi

2018

IEICE Trans. Inf. & Syst.

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Kunihiro NODA†a) , Nonmember, Takashi KOBAYASHI †b) , and Noritoshi ATSUMI † †c) , Members SUMMARY Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often afflicted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactified reverseengineered sequence diagram that contains system's key behaviors. To identify core objects, first, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more effective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-theart trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a significant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.

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“…Sen et al present Jalangi, an instrumentation framework for dynamic program analysis of JavaScript applications [Sen et al 2013]. Jalangi has been used as the basis for various tools, such as DLint, a łlintž tool based on dynamic program analysis [Gong et al 2015], MemInsight, a platform-independent memory debugging tool [Jensen et al 2015], and Crowdie, a feedback-directed debugging tool [Madsen et al 2016]. Considerable research has been devoted to tools for detecting and repairing event races in JavaScript applications [Adamsen et al 2017b,a;Petrov et al 2012;Raychev et al 2013;Zhang and Wang 2017;Zheng et al 2011].…”

Section: Related Workmentioning

confidence: 99%

Finding broken promises in asynchronous JavaScript programs

Alimadadi

Zhong

Madsen

et al. 2018

Proc. ACM Program. Lang.

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Recently, promises were added to ECMAScript 6, the JavaScript standard, in order to provide better support for the asynchrony that arises in user interfaces, network communication, and non-blocking I/O. Using promises, programmers can avoid common pitfalls of event-driven programming such as event races and the deeply nested counterintuitive control flow referred to as łcallback hellž. Unfortunately, promises have complex semantics and the intricate controlś and data-flow present in promise-based code hinders program comprehension and can easily lead to bugs. The promise graph was proposed as a graphical aid for understanding and debugging promise-based code. However, it did not cover all promise-related features in ECMAScript 6, and did not present or evaluate any technique for constructing the promise graphs. In this paper, we extend the notion of promise graphs to include all promise-related features in ECMAScript 6, including default reactions, exceptions, and the synchronization operations race and all. Furthermore, we report on the construction and evaluation of PromiseKeeper, which performs a dynamic analysis to create promise graphs and infer common promise anti-patterns. We evaluate PromiseKeeper by applying it to 12 open source promise-based Node.js applications. Our results suggest that the promise graphs constructed by PromiseKeeper can provide developers with valuable information about occurrences of common anti-patterns in their promise-based code, and that promise graphs can be constructed with acceptable run-time overhead. CCS Concepts: • Software and its engineering → Software testing and debugging; Concurrent programming structures;

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Feedback-directed instrumentation for deployed JavaScript applications

Cited by 19 publications

References 33 publications

BugsJS: a Benchmark of JavaScript Bugs

BugsJS: a Benchmark of JavaScript Bugs

Identifying Core Objects for Trace Summarization by Analyzing Reference Relations and Dynamic Properties

Finding broken promises in asynchronous JavaScript programs

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