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value partitioning a lightweight approach to relational static analysis for javascript

Bibliographic Details
Title: value partitioning a lightweight approach to relational static analysis for javascript
Authors: Nielsen, Benjamin Barslev, Møller, Anders
Contributors: Benjamin Barslev Nielsen and Anders Møller, Hirschfeld, Robert, Pape, Tobias
Source: Nielsen, B B & Møller, A 2020, Value Partitioning : A Lightweight Approach to Relational Static Analysis for JavaScript. in 34th European Conference on Object-Oriented Programming, ECOOP 2020., 16, Dagstuhl Publishing, Leibniz International Proceedings in Informatics, 34th European Conference on Object-Oriented Programming (ECOOP 2020), Online, 15/11/2020. https://doi.org/10.4230/LIPIcs.ECOOP.2020.16
34th European Conference on Object-Oriented Programming
Publisher Information: Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.
Publication Year: 2020
Subject Terms: JavaScript, Dataflow analysis, Theory of computation → Program analysis, Abstract interpretation, abstract interpretation, ddc:004, dataflow analysis
Description: In static analysis of modern JavaScript libraries, relational analysis at key locations is critical to provide sound and useful results. Prior work addresses this challenge by the use of various forms of trace partitioning and syntactic patterns, which is fragile and does not scale well, or by incorporating complex backwards analysis. In this paper, we propose a new lightweight variant of trace partitioning named value partitioning that refines individual abstract values instead of entire abstract states. We describe how this approach can effectively capture important relational properties involving dynamic property accesses, functions with free variables, and predicate functions. Furthermore, we extend an existing JavaScript analyzer with value partitioning and demonstrate experimentally that it is a simple, precise, and efficient alternative to the existing approaches for analyzing widely used JavaScript libraries.
Document Type: Article
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DOI: 10.4230/lipics.ecoop.2020.16
Access URL: https://drops.dagstuhl.de/opus/volltexte/2020/13173/
https://drops.dagstuhl.de/opus/volltexte/2020/13173/pdf/LIPIcs-ECOOP-2020-16.pdf/
https://dblp.uni-trier.de/db/conf/ecoop/ecoop2020.html#NielsenM19
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2020.16
https://pure.au.dk/portal/en/publications/d9bc64e8-6c48-4bbc-9b42-e5bcc9047452
https://pure.au.dk/portal/en/publications/d9bc64e8-6c48-4bbc-9b42-e5bcc9047452
https://doi.org/10.4230/LIPIcs.ECOOP.2020.16
https://pure.au.dk/ws/files/202400613/LIPIcs_ECOOP_2020_16.pdf
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Rights: CC BY
Accession Number: edsair.dedup.wf.002..f5a1f4c52f0cd8b63f8f010830d7838e
Database: OpenAIRE
Description
DOI:10.4230/lipics.ecoop.2020.16