Artifact for ESOP '23 Paper: Clustered Relational Thread-Modular Abstract Interpretation with Local Traces We construct novel thread-modular analyses that track relational information for potentially overlapping clusters of global variables – given that they are protected by common mutexes. We provide a framework to systematically increase the precision of clustered relational analyses by splitting control locations based on abstractions of local traces. As one instance, we obtain an analysis of dynamic thread creation and joining. Interestingly, tracking less relational information for globals may result in higher precision. We consider the class of 2-decomposable domains that encompasses many weakly relational domains (e.g., Octagons). For these domains, we prove that maximal precision is attained already for clusters of globals of sizes at most 2. Please refer to esop23.md for the artifact descritption. For the most up-to-date version of the Goblint Static Analyzer, please refer to https://goblint.in.tum.de.

This work was supported in part by Deutsche Forschungsgemeinschaft (DFG) – 378803395/2428 ConVeY, the Estonian Research Council grant PSG61, and the Estonian Centre of Excellence in IT (EXCITE), funded by the European Regional Development Fund.