DocumentCode :
632650
Title :
Automated generation of state abstraction functions using data invariant inference
Author :
Tonella, Paolo ; Cu Duy Nguyen ; Marchetto, A. ; Lakhotia, Kiran ; Harman, Mark
Author_Institution :
Fondazione Bruno Kessler, Trento, Italy
fYear :
2013
fDate :
18-19 May 2013
Firstpage :
75
Lastpage :
81
Abstract :
Model based testing relies on the availability of models that can be defined manually or by means of model inference techniques. To generate models that include meaningful state abstractions, model inference requires a set of abstraction functions as input. However, their specification is difficult and involves substantial manual effort. In this paper, we investigate a technique to automatically infer both the abstraction functions necessary to perform state abstraction and the finite state models based on such abstractions. The proposed approach uses a combination of clustering, invariant inference and genetic algorithms to optimize the abstraction functions along three quality attributes that characterize the resulting models: size, determinism and infeasibility of the admitted behaviors. Preliminary results on a small e-commerce application are extremely encouraging because the automatically produced models include the set of manually defined gold standard models.
Keywords :
finite state machines; genetic algorithms; inference mechanisms; pattern clustering; automated generation; clustering; data invariant inference; e-commerce application; finite state models; genetic algorithms; model based testing; model inference techniques; quality attributes; state abstraction functions; Abstracts; Concrete; Genetic algorithms; Measurement; Object oriented modeling; Optimization; Testing; Abstraction functions; Model inference; Model-based testing; Search-based software engineering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation of Software Test (AST), 2013 8th International Workshop on
Conference_Location :
San Francisco, CA
Type :
conf
DOI :
10.1109/IWAST.2013.6595795
Filename :
6595795
Link To Document :
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