Paper Info
Title
Practically Tunable Static Analysis Framework for Large-Scale JavaScript Applications (T)
Abstract
We present a novel approach to analyze large-scale JavaScript applications statically by tuning the analysis scalability possibly giving up its soundness. For a given sound static baseline analysis of JavaScript programs, our framework allows users to define a sound approximation of selected executions that they are interested in analyzing, and it derives a tuned static analysis that can analyze the selected executions practically. The selected executions serve as parameters of the framework by taking trade-off between the scalability and the soundness of derived analyses. We formally describe our framework in abstract interpretation, and implement two instances of the framework. We evaluate them by analyzing large-scale real-world JavaScript applications, and the evaluation results show that the framework indeed empowers users to experiment with different levels of scalability and soundness. Our implementation provides an extra level of scalability by deriving sparse versions of derived analyses, and the implementation is publicly available.
Year
DOI
Venue
2015
10.1109/ASE.2015.28
Automated Software Engineering
Keywords
Field
DocType
practically tunable static analysis framework,large-scale JavaScript applications,JavaScript programs,tuned static analysis,selected executions approximation,abstract interpretation,sparse versions
Programming language,Computer science,Abstract interpretation,Static analysis,AC power,Theoretical computer science,Soundness,Scalability,JavaScript
Conference
ISSN
Citations 
PageRank 
1527-1366
9
0.51
References 
Authors
14
4
Name
Order
Citations
PageRank
Yoonseok Ko191.18
Hongki Lee2132.38
Julian Dolby3138974.44
Sukyoung Ryu418525.77