Reference : Automated Generation of State Abstraction Functions using Data Invariant Inference
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
Automated Generation of State Abstraction Functions using Data Invariant Inference
Nguyen, Duy Cu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Tonella, Paolo mailto [Fondazione Bruno Kessler- FBK > SE Unit]
Marchetto, Alessandro mailto [Fondazione Bruno Kessler - FBK > SE Unit]
Lakhotia, Kiran mailto [University College London - UCL]
Harman, Mark mailto [University College London - UCL]
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 combi- nation 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.
8th International Workshop on Automation of Software Test (AST’13)
May 18-19
FP7 ; 257574 - FITTEST - Future Internet Testing

File(s) associated to this reference

Fulltext file(s):

Limited access
ast2013-absfun.pdfPublisher postprint355.87 kBRequest a copy

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.