NDLI: Model-Based Testing Using Symbolic Animation and Machine Learning

Content Provider	IEEE Xplore Digital Library
Author	Bue, P.-C. Dadeau, F. Heam, P.-C.
Copyright Year	2010
Abstract	We present in this paper a technique based on symbolic animation of models that aims at producing model-based tests. In order to guide the animation of the model, we rely on the use of a deterministic finite automaton (DFA) of the model that is built using a well-known machine learning algorithm, that considers a complex model as a black-box component, whose behavior is inferred. Since the DFA obtained in this way may be an over-approximation and, thus, admit traces that were not admitted on the original model, this abstraction is refined using counter-examples made of unfeasible traces. The computation of counter-examples is performed using a systematic coverage of the DFA states and transitions, producing test sequences that are replayed on the model, providing either test cases for offline testing, or counter-examples that aim at refining the abstraction.
Starting Page	355
Ending Page	360
File Size	425546
Page Count	6
File Format	PDF
ISBN	9781424467730
e-ISBN	9781424467747
DOI	10.1109/ICSTW.2010.43
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-06
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software testing System testing Machine learning algorithms constraint solving Learning automata Doped fiber amplifiers State-space methods abstraction refinement machine learning symbolic animation Refining Machine learning Animation Context modeling
Content Type	Text
Resource Type	Article

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