Context-Sensitive Data-Dependence Analysis Via Linear Conjunctive Language Reachability

Many program analysis problems can be formulated as graph reachability problems. In the literature, context-free language (CFL) reachability has been the most popular formulation and can be computed in subcubic time. The context-sensitive data-dependence analysis is a fundamental abstraction that can express a broad range of program analysis problems. It essentially describes an interleaved matched-parenthesis language reachability problem. The language is not context-free, and the problem is well-known to be undecidable. In practice, many program analyses adopt CFL-reachability to exactly model the matched parentheses for either context-sensitivity or structure-transmitted data-dependence, but not both. Thus, the CFL-reachability formulation for context-sensitive data-dependence analysis is inherently an approximation.To support more precise and scalable analyses, this paper introduces linear conjunctive language (LCL) reachability, a new, expressive class of graph reachability. LCL not only contains the interleaved matched-parenthesis language, but is also closed under all set-theoretic operations. Given a graph with n nodes and m edges, we propose an O (mn) time approximation algorithm for solving all-pairs LCL-reachability, which is asymptotically better than known CFL-reachability algorithms. Our formulation and algorithm offer a new perspective on attacking the aforementioned undecidable problem - the LCL-reachability formulation is exact, while the LCL-reachability algorithm yields a sound approximation. We have applied the LCL-reachability framework to two existing client analyses. The experimental results show that the LCL-reachability framework is both more precise and scalable than the traditional CFL-reachability framework. This paper opens up the opportunity to exploit LCL-reachability in program analysis.