Eagle

Object sensitivity is widely used as a context abstraction for computing the points-to information context-sensitively for object-oriented programming languages such as Java. Due to the combinatorial explosion of contexts in large object-oriented programs, k -object-sensitive pointer analysis (under k -limiting), denoted k -obj , is often inefficient even when it is scalable for small values of k , where k ⩽ 2 holds typically. A recent popular approach for accelerating k -obj trades precision for efficiency by instructing k -obj to analyze only some methods in a program context-sensitively, determined heuristically by a pre-analysis. In this article, we investigate how to develop a fundamentally different approach, Eagle , for designing a pre-analysis that can make k -obj run significantly faster while maintaining its precision. The novelty of Eagle is to enable k -obj to analyze a method with partial context sensitivity (i.e., context-sensitively for only some of its selected variables/allocation sites) by solving a context-free-language (CFL) reachability problem based on a new CFL-reachability formulation of k -obj . By regularizing one CFL for specifying field accesses and using another CFL for specifying method calls, we have formulated Eagle as a fully context-sensitive taint analysis (without k -limiting) that is both effective (by selecting the variables/allocation sites to be analyzed by k -obj context-insensitively so as to reduce the number of context-sensitive facts inferred by k -obj in the program) and efficient (by running linearly in terms of the number of pointer assignment edges in the program). As Eagle represents the first precision-preserving pre-analysis, our evaluation focuses on demonstrating its significant performance benefits in accelerating k -obj for a set of popular Java benchmarks and applications, with call graph construction, may-fail-casting, and polymorphic call detection as three important client analyses.