Learning stateful preconditions modulo a test generator
Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation(2019)
摘要
In this paper, we present a novel learning framework for inferring stateful preconditions (i.e., preconditions constraining not only primitive-type inputs but also non-primitive-type object states) modulo a test generator, where the quality of the preconditions is based on their safety and maximality with respect to the test generator. We instantiate the learning framework with a specific learner and test generator to realize a precondition synthesis tool for C#. We use an extensive evaluation to show that the tool is highly effective in synthesizing preconditions for avoiding exceptions as well as synthesizing conditions under which methods commute.
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关键词
Data-Driven Inference, Specification Mining, Synthesis
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