When analyzing programs, large libraries pose significant
\nchallenges to static points-to analysis. A popular solution
\nis to have a human analyst provide points-to specifications
\nthat summarize relevant behaviors of library code, which
\ncan substantially improve precision and handle missing code
\nsuch as native code. We propose Atlas, a tool that automatically infers points-to specifications. Atlas synthesizes unit
\ntests that exercise the library code, and then infers points-to
\nspecifications based on observations from these executions.
\nAtlas automatically infers specifications for the Java standard library, and produces better results for a client static
\ninformation flow analysis on a benchmark of 46 Android
\napps compared to using existing handwritten specifications.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 handle missing code such as native code. We propose Atlas, a tool that automatically infers points-to specifications. Atlas 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