Perracotta: mining temporal API rules from imperfect traces (2006)
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| Venue: | Ohio University |
| Citations: | 85 - 2 self |
BibTeX
@INPROCEEDINGS{Yang06perracotta:mining,
author = {Jinlin Yang and David Evans and Deepali Bhardwaj and Thirumalesh Bhat and Manuvir Das},
title = {Perracotta: mining temporal API rules from imperfect traces},
booktitle = {Ohio University},
year = {2006}
}
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Abstract
Dynamic inference techniques have been demonstrated to provide useful support for various software engineering tasks including bug finding, test suite evaluation and improvement, and specification generation. To date, however, dynamic inference has only been used effectively on small programs under controlled conditions. In this paper, we identify reasons why scaling dynamic inference techniques has proven difficult, and introduce solutions that enable a dynamic inference technique to scale to large programs and work effectively with the imperfect traces typically available in industrial scenarios. We describe our approximate inference algorithm, present and evaluate heuristics for winnowing the large number of inferred properties to a manageable set of interesting properties, and report on experiments using inferred properties. We evaluate our techniques on JBoss and the Windows kernel. Our tool is able to infer many of the properties checked by the Static Driver Verifier and leads us to discover a previously unknown bug in Windows.







