On-the-Fly Static Analysis via Dynamic Bidirected Dyck Reachability

• Published in: Proceedings of ACM on programming languages Vol. 8; no. POPL; pp. 1239 - 1268
• Main Authors: Krishna, Shankaranarayanan, Lal, Aniket, Pavlogiannis, Andreas, Tuppe, Omkar
• Format: Journal Article
• Language: English
• Published: New York, NY, USA ACM 02.01.2024
• Subjects: Program analysis; Software and its engineering; Software verification and validation; Theory and algorithms for application domains; Theory of computation; static analysis; dynamic algorithms; CFL reachability
• DOI: 10.1145/3632884
• ISSN: 2475-1421

Dyck reachability is a principled, graph-based formulation of a plethora of static analyses. Bidirected graphs are used for capturing dataflow through mutable heap data, and are usual formalisms of demand-driven points-to and alias analyses. The best (offline) algorithm runs in O(m+n · α(n)) time, where n is the number of nodes and m is the number of edges in the flow graph, which becomes O(n2) in the worst case. In the everyday practice of program analysis, the analyzed code is subject to continuous change, with source code being added and removed. On-the-fly static analysis under such continuous updates gives rise to dynamic Dyck reachability, where reachability queries run on a dynamically changing graph, following program updates. Naturally, executing the offline algorithm in this online setting is inadequate, as the time required to process a single update is prohibitively large. In this work we develop a novel dynamic algorithm for bidirected Dyck reachability that has O(n · α(n)) worst-case performance per update, thus beating the O(n2) bound, and is also optimal in certain settings. We also implement our algorithm and evaluate its performance on on-the-fly data-dependence and alias analyses, and compare it with two best known alternatives, namely (i) the optimal offline algorithm, and (ii) a fully dynamic Datalog solver. Our experiments show that our dynamic algorithm is consistently, and by far, the top performing algorithm, exhibiting speedups in the order of 1000X. The running time of each update is almost always unnoticeable to the human eye, making it ideal for the on-the-fly analysis setting.