Inclusion-based alias analysis for C can be formulated as a context-free language (CFL) reachability problem. It is well known that the traditional cubic CFL-reachability algorithm does not scale well in practice. We present a highly scalable and efficient CFL-reachability-based alias analysis for C. The key novelty of our algorithm is to propagate reachability information along only original graph edges and bypass a large portion of summary edges, while the traditional CFL-reachability algorithm propagates along all summary edges. We also utilize the Four Russians' Trick - a key enabling technique in the subcubic CFL-reachability algorithm - in our alias analysis. We have implemented our subcubic alias analysis and conducted extensive experiments on widely-used C programs from the pointer analysis literature. The results demonstrate that our alias analysis scales extremely well in practice. In particular, it can analyze the recent Linux kernel (which consists of 10M SLOC) in about 30 seconds.

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.

A calling context is an important piece of information used widely to help developers understand program executions (e.g., for debugging). While calling contexts o er useful control information, information regarding data involved in a bug (e.g., what data structure holds a leaking object), in many cases, can bring developers closer to the bug's root cause. Such data information, often exhibited as heap reference paths, has already been needed by many tools. The only way for a dynamic analysis to record complete reference paths is to perform heap dumping, which incurs huge runtime overhead and renders the analysis impractical. This paper presents a novel static analysis that can precisely infer, from a calling context of a method that contains a use (e.g., read or write) of an object, the heap reference paths leading to the object at the time the use occurs. Since calling context recording is much less expensive, our technique provides bene ts for all dynamic techniques that need heap information, signi cantly reducing their overhead. CCS Concepts • Software and its engineering → Automated static analysis; Dynamic analysis;

Pointer information, indispensable for static analysis tools, is expensive to compute and query. We provide a query-efficient persistence technique, Pestrie, to mitigate the costly computation and slow querying of precise pointer information. Leveraging equivalence and hub properties, Pestrie can compress pointer information and answers pointer related queries very efficiently. The experiment shows that Pestrie produces 10.5X and 17.5X smaller persistent files than the traditional bitmap and BDD encodings. Meanwhile, Pestrie is 2.9X to 123.6X faster than traditional demand-driven approaches for serving points-to related queries.