Learning stateful preconditions modulo a test generator

Learning stateful preconditions modulo a test generator
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学习以测试生成器为模的状态前提条件

DOI:
10.1145/3314221.3314641
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发表时间:
2019
期刊:
PLDI 2019: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation
影响因子:
--
通讯作者:
Xie, Tao
Xie, Tao
中科院分区:
--
文献类型:
--
作者:
Astorga, Angello;Madhusudan, P.;Saha, Shambwaditya;Wang, Shiyu;Xie, Tao

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在本文中,我们提出了一个新的学习框架,用于推断有状态的前提条件(即,不仅约束连续类型输入而且约束非原始类型对象状态的前提条件)对测试生成器取模,其中前提条件的质量基于它们相对于测试生成器的安全性和最大性。我们实例化的学习框架与特定的学习器和测试生成器实现C#的先决条件合成工具。我们使用了广泛的评估表明,该工具是非常有效的合成先决条件,避免异常,以及合成条件下,方法交换。
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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