A Theoretical Foundation of Sensitivity in an Abstract Interpretation Framework

• Published in: ACM transactions on programming languages and systems Vol. 40; no. 3; pp. 1 - 44
• Main Authors: Kim, Se-Won, Rival, Xavier, Ryu, Sukyoung
• Format: Journal Article
• Language: English
• Published: New York, NY, USA ACM 01.08.2018
• Subjects: Automated static analysis; Computer Science; Formal methods; Programming Languages; Software and its engineering; Software functional properties; Software organization and properties; Abstract interpretation; analysis sensitivity; analysis framework; program analysis
• ISSN: 0164-0925; 1558-4593
• DOI: 10.1145/3230624

Program analyses often utilize various forms of sensitivity such as context sensitivity, call-site sensitivity, and object sensitivity. These techniques all allow for more precise program analyses, that are able to compute more precise program invariants, and to verify stronger properties. Despite the fact that sensitivity techniques are now part of the standard toolkit of static analyses designers and implementers, no comprehensive frameworks allow the description of all common forms of sensitivity. As a consequence, the soundness proofs of static analysis tools involving sensitivity often rely on ad hoc formalization, which are not always carried out in an abstract interpretation framework. Moreover, this also means that opportunities to identify similarities between analysis techniques to better improve abstractions or to tune static analysis tools can easily be missed. In this article, we present and formalize a framework for the description of sensitivity in static analysis. Our framework is based on a powerful abstract domain construction, and utilizes reduced cardinal power to tie basic abstract predicates to the properties analyses are sensitive to. We formalize this abstraction, and the main abstract operations that are needed to turn it into a generic abstract domain construction. We demonstrate that our approach can allow for a more precise description of program states, and that it can also describe a large set of sensitivity techniques, both when sensitivity criteria are static (known before the analysis) or dynamic (inferred as part of the analysis), and sensitive analysis tuning parameters. Last, we show that sensitivity techniques used in state-of-the-art static analysis tools can be described in our framework.