Analyzing program behavior through active automata learning

The objective of the RERS Challenge 2013 was to analyze program behavior with respect to given sets of LTL and reachability properties for a set of reactive programs. The programs in various sizes and complexities could be divided into three different categories, depending on the available informati...

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Bibliographic Details
Published in:International journal on software tools for technology transfer 2014-10, Vol.16 (5), p.531-542
Main Authors: Bauer, Oliver, Geske, Maren, Isberner, Malte
Format: Article
Language:English
Subjects:
Algorithms
Categories
Complexity
Computer programs
Computer Science
Learning
Obstacles
Optimization
Rers
Software
Software Engineering
Software Engineering/Programming and Operating Systems
Theory of Computation
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Summary:The objective of the RERS Challenge 2013 was to analyze program behavior with respect to given sets of LTL and reachability properties for a set of reactive programs. The programs in various sizes and complexities could be divided into three different categories, depending on the available information: from black-box (binary-only) to white-box (full source code) over a mixed form thereof (grey-box). In this paper we present our approach to tackling the challenge problems, which is based on active automata learning . This required extending automata learning algorithms to exploit the given information, and adapting them in order to overcome problem-specific obstacles. We describe general optimizations and discuss the achieved results.
ISSN:1433-2779
1433-2787
DOI:10.1007/s10009-014-0333-2