@inproceedings{12ce00f46dfd43e682cdd79c27569f92,
title = "Learning stateful preconditions modulo a test generator",
abstract = "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.",
keywords = "Data-Driven Inference, Specification Mining, Synthesis",
author = "Angello Astorga and P. Madhusudan and Shambwaditya Saha and Shiyu Wang and Tao Xie",
note = "This work was supported in part by National Science Foundation under grant no. CCF-1527395, CNS-1513939, CNS-1564274, CCF-1816615 and the GEM fellowship.; 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2019 ; Conference date: 22-06-2019 Through 26-06-2019",
year = "2019",
month = jun,
day = "8",
doi = "10.1145/3314221.3314641",
language = "English (US)",
series = "Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)",
publisher = "Association for Computing Machinery",
pages = "775--787",
editor = "McKinley, {Kathryn S.} and Kathleen Fisher",
booktitle = "PLDI 2019 - Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation",
address = "United States",
}