Practically Tunable Static Analysis Framework for Large-Scale JavaScript Applications (T)

Ko, Yoonseok; Lee, Hongki; Dolby, Julian; Ryu, Sukyoung

doi:10.1109/ase.2015.28

Cited by 26 publications

(12 citation statements)

References 18 publications

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“…In general, while there have been several proposed static analysis tools and techniques for JavaScript in previous research, most suffer from being sound but impractical, or the opposite, practical but unsound [119,142]. Practical limitations include lack of scalability and inabilities to analyze library usage.…”

Section: Static and Dynamic Analysis For Javascriptmentioning

confidence: 99%

Why and how JavaScript developers use linters

Tomasdottir

Aniche

Deursen

2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)

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A linter is a type of static analysis tool that warns software developers about possible errors in code or violations to coding standards. By using such a tool, errors can be surfaced early in the development process when they are cheaper to fix, and code can be kept more readable and maintainable. For such a tool to be successful, it is important for its creators to understand the needs and challenges of developers when using a linter. Furthermore, it needs to be made clear to developers why using such a tool can be beneficial, along with how linters can be configured to identify appropriate and relevant issues for their projects.In this thesis, we examine developers' perceptions of linters to increase our knowledge on these tools for JavaScript, the most widely used programming language in the world today. More specifically, we study why and how developers use ESLint, the most popular JavaScript linter, along with the challenges that they face while using the tool. We collect data with three different methods where we first interview 15 experts on using linters, then analyze over 9,500 ESLint configuration files and finally survey more than 300 developers from the JavaScript community. The combined results from these analyses provide developers, tool makers and researchers with valuable knowledge and advice on using and developing a linter for JavaScript.

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Section: Static and Dynamic Analysis For Javascriptmentioning

confidence: 99%

Why and how JavaScript developers use linters

Tomasdottir

Aniche

Deursen

2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)

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“…WALA uses this machinery for multiple languages, JavaScript from Rhino and Java source from Eclipse. It has been public for several years now, used by researchers for a range of work, such as hybrid app analysis [19], correlation tracking [36], a special form of context sensitivity, tunable analysis techniques [17], and approximate analysis to handle real-world JavaScript code [10]. It has been robust and scalable enough to support products, and the Python analysis for machine learning leverages that technology.…”

Section: Front Endmentioning

confidence: 99%

Ariadne: analysis for machine learning programs

Dolby¹,

Shinnar²,

Allain³

et al. 2018

Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages

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Machine learning has transformed domains like vision and translation, and is now increasingly used in science, where the correctness of such code is vital. Python is popular for machine learning, in part because of its wealth of machine learning libraries, and is felt to make development faster; however, this dynamic language has less support for error detection at code creation time than tools like Eclipse. This is especially problematic for machine learning: given its statistical nature, code with subtle errors may run and produce results that look plausible but are meaningless. This can vitiate scientific results. We report on Ariadne: applying a static framework, WALA, to machine learning code that uses TensorFlow. We have created static analysis for Python, a type system for tracking tensors-Tensorflow's core data structures-and a data flow analysis to track their usage. We report on how it was built and present some early results.

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“…• Because web APIs are commonly used in code written in dynamically typed languages such as JavaScript, we targeted our analysis on JavaScript code. Typically, interprocedural static whole-program analysis on JavaScript code is more challenging (i.e., less scalable, due difficulties from dynamical typing) than the same analysis on Java code [22]- [25]. For the initial implementation, we chose to handle requests written using the jQuery framework due to its popularity -reportedly, 70% of websites use the jQuery framework [26].…”

Section: B Our Approach and Relevance To Web Api Researchersmentioning

confidence: 99%

Opportunities in Software Engineering Research for Web API Consumption

Wittern

Ying

Zheng

et al. 2017

2017 IEEE/ACM 1st International Workshop on API Usage and Evolution (WAPI)

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Nowadays, invoking third party code increasingly involves calling web services via their web APIs, as opposed to the more traditional scenario of downloading a library and invoking the library's API. However, there are also new challenges for developers calling these web APIs. In this paper, we highlight a broad set of these challenges and argue for resulting opportunities for software engineering research to support developers in consuming web APIs. We outline two specific research threads in this context: (1) web API specification curation, which enables us to know the signatures of web APIs, and (2) static analysis that is capable of extracting URLs, HTTP methods etc. of web API calls. Furthermore, we present new work on how we combine (1) and (2) to provide IDE support for application developers consuming web APIs. As web APIs are used broadly, research in supporting the consumption of web APIs offers exciting opportunities.

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Practically Tunable Static Analysis Framework for Large-Scale JavaScript Applications (T)

Cited by 26 publications

References 18 publications

Why and how JavaScript developers use linters

Why and how JavaScript developers use linters

Ariadne: analysis for machine learning programs

Opportunities in Software Engineering Research for Web API Consumption

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