Active Learning of Points-To Specifications
نویسندگان
چکیده
Large libraries pose signicant challenges to static points-to analysis. A popular solution is to have a human analyst provide points-to specications that summarize relevant behaviors of library code, which can substantially improve precision and scalability, and furthermore handle missing code such as native code. We propose Atlas, a tool that automatically infers points-to specications. Atlas synthesizes test cases that exercise the library code, and then generates points-to specications based on observations from these executions. Atlas automatically infers 97% of specications for the Java Collections API, and discovers 20% more points-to edges than existing, handwrien specications.
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عنوان ژورنال:
- CoRR
دوره abs/1711.03239 شماره
صفحات -
تاریخ انتشار 2017