When dealing with millions of lines of code, we still cannot have the cake and eat it: sparse value-flow analysis is powerful in checking source-sink problems, but existing work cannot escape from the “pointer trap” – a precise points-to analysis limits its scalability and an imprecise one seriously undermines its precision. We present Pinpoint, a holistic approach that decomposes the cost of high-precision points-to analysis by precisely discovering local data dependence and delaying the expensive inter-procedural analysis through memorization. Such memorization enables the on-demand slicing of only the necessary inter-procedural data dependence and path feasibility queries, which are then solved by a costly SMT solver. Experiments show that Pinpoint can check programs such as MySQL (around 2 million lines of code) within 1.5 hours. The overall false positive rate is also very low (14.3% - 23.6%). Pinpoint has discovered over forty real bugs in mature and extensively checked open source systems. And the implementation of Pinpoint and all experimental results are freely available.
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.
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.
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