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JSAI

swMATH ID: 30521
Software Authors: Vineeth Kashyap, Kyle Dewey, Ethan A. Kuefner, John Wagner, Kevin Gibbons, John Sarracino, Ben Wiedermann, Ben Hardekopf
Description: JSAI: Designing a Sound, Configurable, and Efficient Static Analyzer for JavaScript. We describe JSAI, an abstract interpreter for JavaScript. JSAI uses novel abstract domains to compute a reduced product of type inference, pointer analysis, string analysis, integer and boolean constant propagation, and control-flow analysis. In addition, JSAI allows for analysis control-flow sensitivity (i.e., context-, path-, and heap-sensitivity) to be modularly configured without requiring any changes to the analysis implementation. JSAI is designed to be provably sound with respect to a specific concrete semantics for JavaScript, which has been extensively tested against existing production-quality JavaScript implementations. We provide a comprehensive evaluation of JSAI’s performance and precision using an extensive benchmark suite. This benchmark suite includes real-world JavaScript applications, machine-generated JavaScript code via Emscripten, and browser addons. We use JSAI’s configurability to evaluate a large number of analysis sensitivities (some well-known, some novel) and observe some surprising results. We believe that JSAI’s configurability and its formal specifications position it as a useful research platform to experiment on novel sensitivities, abstract domains, and client analyses for JavaScript.
Homepage: https://arxiv.org/abs/1403.3996
Source Code:  https://github.com/nystrom/jsai
Dependencies: JavaScript
Keywords: Programming Languages; arXiv_cs.PL; JavaScript; Static Analyzer; JSAI
Related Software: JavaScript; Phantm; Norn; Node.js; PPL; ast; GATEKEEPER; Synode; Judge; WALA; Soot; pycallgraph; SAFEWAPI; Paramiko; PyYAML; npm; Depends; Callgrind; code2graph; Pyan3
Cited in: 5 Publications

Standard Articles

1 Publication describing the Software Year
JSAI: Designing a Sound, Configurable, and Efficient Static Analyzer for JavaScript
Vineeth Kashyap, Kyle Dewey, Ethan A. Kuefner, John Wagner, Kevin Gibbons, John Sarracino, Ben Wiedermann, Ben Hardekopf
2014

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