Multivariant Assertion-based Guidance in Abstract Interpretation

TL;DR

This paper introduces assertion-based guidance techniques within abstract interpretation to improve analysis precision and efficiency, supporting multivariance and runtime semantics, with formal analysis of their effects.

Contribution

It presents a novel framework for integrating rich assertions into abstract interpretation, enhancing precision and performance while handling multivariance and runtime semantics.

Findings

Supports multivariance and context-sensitivity in assertions

Improves analysis precision and speed

Provides formal analysis of assertion effects

Abstract

Approximations during program analysis are a necessary evil, as they ensure essential properties, such as soundness and termination of the analysis, but they also imply not always producing useful results. Automatic techniques have been studied to prevent precision loss, typically at the expense of larger resource consumption. In both cases (i.e., when analysis produces inaccurate results and when resource consumption is too high), it is necessary to have some means for users to provide information to guide analysis and thus improve precision and/or performance. We present techniques for supporting within an abstract interpretation framework a rich set of assertions that can deal with multivariance/context-sensitivity, and can handle different run-time semantics for those assertions that cannot be discharged at compile time. We show how the proposed approach can be applied to both improving precision and accelerating analysis. We also provide some formal results on the effects of such assertions on the analysis results.