Memory efficient context-sensitive program analysis

Abstract Static program analysis is in general more precise if it is sensitive to execution contexts (execution paths). But then it is also more expensive in terms of memory consumption. For languages with conditions and iterations, the number of contexts grows exponentially with the program size. This problem is not just a theoretical issue. Several papers evaluating inter-procedural context-sensitive data-flow analysis report severe memory problems, and the path-explosion problem is a major issue in program verification and model checking. In this paper we propose χ -terms as a means to capture and manipulate context-sensitive program information in a data-flow analysis. χ -terms are implemented as directed acyclic graphs without any redundant subgraphs. To show the efficiency of our approach we run experiments comparing the memory usage of χ -terms with four alternative data structures. Our experiments show that χ -terms clearly outperform all the alternatives in terms of memory efficiency.