DocumentCode :
2516157
Title :
Integration Testing of Components Guided by Incremental State Machine Learning
Author :
Li, Keqin ; Groz, Roland ; Shahbaz, Muzammil
Author_Institution :
CNRS LSR-IMAG
fYear :
2006
fDate :
29-31 Aug. 2006
Firstpage :
59
Lastpage :
70
Abstract :
The design of complex systems, e.g., telecom services, is nowadays usually based on the integration of components (COTS), loosely coupled in distributed architectures. When components come from third party sources, their internal structure is usually unknown and the documentation is insufficient. Therefore, the system integrator faces the problem of providing a required system assembling COTS whose behaviour is barely specified and for which no model is usually available. In this paper, we address the problem of integration testing of COTS. It combines test generation techniques with machine learning algorithms. State-based models of components are built from observed behaviours. The models are alternatively used to generate tests and extended to take into account observed behaviour. This process is iterated until a satisfactory level of confidence in testing is achieved
Keywords :
finite state machines; formal specification; integrated software; learning (artificial intelligence); object-oriented programming; program testing; software packages; systems analysis; COTS; complex system design; component integration testing; distributed architecture; formal specification; incremental state machine learning algorithm; state-based component model; test generation technique; Assembly systems; Documentation; Inference algorithms; Machine learning; Machine learning algorithms; Software design; Software systems; System testing; Telecommunication services; Web and internet services;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Testing: Academic and Industrial Conference - Practice And Research Techniques, 2006. TAIC PART 2006. Proceedings
Conference_Location :
Windsor
Print_ISBN :
0-7695-2672-1
Type :
conf
DOI :
10.1109/TAIC-PART.2006.15
Filename :
1691670
Link To Document :
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