Value Partitioning: A Lightweight Approach to Relational Static Analysis for JavaScript
Sun 15 Nov 2020 23:40 - 00:00 at SPLASH-I - S-3 Chair(s): Elisa Gonzalez Boix, Atsushi Igarashi
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.
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.
Sun 15 NovDisplayed time zone: Central Time (US & Canada) change
11:00 - 12:20 | |||
11:00 20mTalk | Flow-Sensitive Type-Based Heap Cloning Research Papers Mohamad Barbar University of Technology, Sydney, Yulei Sui University of Technology Sydney, Shiping Chen Data61 at CSIRO, Australia / UNSW, Australia Link to publication DOI Media Attached | ||
11:20 20mTalk | Sound regular corecursion in coFJ Research Papers Davide Ancona DIBRIS, University of Genova, Italy, Pietro Barbieri Università di Genova, Francesco Dagnino DIBRIS, University of Genova, Italy, Elena Zucca University of Genova Link to publication DOI Media Attached | ||
11:40 20mTalk | Value Partitioning: A Lightweight Approach to Relational Static Analysis for JavaScript Research Papers Link to publication DOI Pre-print Media Attached | ||
12:00 20mTalk | Row and Bounded Polymorphism via Disjoint Polymorphism Research Papers Ningning Xie University of Toronto, Bruno C. d. S. Oliveira University of Hong Kong, Xuan Bi The University of Hong Kong, Tom Schrijvers KU Leuven Link to publication DOI Media Attached |