Modular Collaborative Program Analysis in OPAL

TL;DR

This paper introduces a modular approach to static program analysis that combines declarative and imperative techniques within the OPAL framework, enabling flexible, exchangeable analyses with improved soundness, precision, and scalability.

Contribution

It presents a novel modular analysis method that integrates declarative and imperative techniques, allowing for analysis exchangeability and improved performance in OPAL.

Findings

Implemented various dissimilar analyses in OPAL

Achieved a points-to analysis outperforming Doop

Enhanced analysis soundness, precision, and scalability

Abstract

Current approaches combining multiple static analyses deriving different, independent properties focus either on modularity or performance. Whereas declarative approaches facilitate modularity and automated, analysis-independent optimizations, imperative approaches foster manual, analysis-specific optimizations. In this paper, we present a novel approach to static analyses that leverages the modularity of blackboard systems and combines declarative and imperative techniques. Our approach allows exchangeability, and pluggable extension of analyses in order to improve sound(i)ness, precision, and scalability and explicitly enables the combination of otherwise incompatible analyses. With our approach integrated in the OPAL framework, we were able to implement various dissimilar analyses, including a points-to analysis that outperforms an equivalent analysis from Doop, the state-of-the-art points-to analysis framework.