Automatic Generation and Reuse of Precise Library Summaries for Object-Sensitive Pointer Analysis

The extensive use of libraries in modern software impedes the scalability of pointer analysis. To address this issue, library summarization can be beneficial, but only if the resulting summary-based pointer analysis is faster without sacrificing much precision in the application code. However, currently, no library summarization approaches exist that meet this design objective. This paper presents a novel approach that solves this problem by using k-object-sensitive pointer analysis, k-obj, for Java. The approach involves applying k-obj, along with a set of summary-based inference rules, to generate a k-object-sensitive library summary. By replacing the program's library with this summary and applying k-obj, the efficiency of the program can be significantly improved while maintaining nearly the same or better precision in the application code. We validate our approach with an implementation in Soot and an evaluation using representative Java programs.