Perracotta: Mining Temporal API Rules from Imperfect Traces
28th
International Conference on Software Engineering
Shanghai, China
20-28 May 2006
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.
Note: An earlier version of this paper used "Terracotta" in the
title. The name was changed to "Perracotta" due to a trademark
issue.
Keywords:
Dynamic analysis, temporal properties, specification inference.
Complete Paper (10 pages)
[PDF]
Inexpensive Program Analysis
Group Page
Perracotta
