Active Learning of Points-To Specifications
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When analyzing programs, large libraries pose significant challenges to static points-to analysis. A popular solution is to have a human analyst provide points-to specifications that summarize relevant behaviors of library code, which can substantially improve precision and furthermore handle missing code such as native code. We propose Atlas, a tool that automatically infers points-to specifications. Atlas synthesizes unit tests that exercise the library code, and then infers points-to specifications based on observations from these executions. Atlas automatically infers specifications for the Java standard library, and produces better results for a client static information flow analysis on a benchmark of 46 Android apps compared to using existing handwritten specifications.
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