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An Efficient Model Inference Algorithm for Learning-based Testing of Reactive Systems
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-9706-5008
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
2012 (English)Report (Other academic)
Abstract [en]

Learning-based testing (LBT) is an emerging methodology to automate iterative black-box requirements testing of software systems. The methodology involves combining model inference with model checking techniques. However, a variety of optimisations on model inference are necessary in order to achieve scalable testing for large systems.

In this paper we describe the IKL learning algorithm which is an active incremental learning algorithm for deterministic Kripke structures. We formally prove the correctness of IKL. We discuss the optimisations it incorporates to achieve scalability of testing. We also evaluate a black box heuristic for test termination based on convergence of IKL learning.

Place, publisher, year, edition, pages
2012. , 29 p.
Keyword [en]
automata learning, black-box testing, learning-based testing, reactive systems
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-119266OAI: oai:DiVA.org:kth-119266DiVA: diva2:610364
Note

QC 20130312

Available from: 2013-03-11 Created: 2013-03-11 Last updated: 2013-03-12Bibliographically approved
In thesis
1. Algorithms and Tools for Learning-based Testing of Reactive Systems
Open this publication in new window or tab >>Algorithms and Tools for Learning-based Testing of Reactive Systems
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis we investigate the feasibility of learning-based testing (LBT) as a viable testing methodology for reactive systems. In LBT, a large number of test cases are automatically generated from black-box requirements for the system under test (SUT) by combining an incremental learning algorithm with a model checking algorithm. The integration of the SUT with these algorithms in a feedback loop optimizes test generation using the results from previous outcomes. The verdict for each test case is also created automatically in LBT.

To realize LBT practically, existing algorithms in the literature both for complete and incremental learning of finite automata were studied. However, limitations in these algorithms led us to design, verify and implement new incremental learning algorithms for DFA and Kripke structures. On the basis of these algorithms we implemented an LBT architecture in a practical tool called LBTest which was evaluated on pedagogical and industrial case studies.

The results obtained from both types of case studies show that LBT is an effective methodology which discovers errors in reactive SUTs quickly and can be scaled to test industrial applications. We believe that this technology is easily transferrable to industrial users because of its high degree of automation.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. xii, 79 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2013:03
Keyword
specification-based testing, learning-based testing, reactive systems, LBTest, case studies
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-119267 (URN)978-91-7501-674-0 (ISBN)
Public defence
2013-04-16, F3, Lindstedtsvägen 26, Kungliga Tekniska Högskolan, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20130312

Available from: 2013-03-12 Created: 2013-03-11 Last updated: 2013-03-12Bibliographically approved

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Meinke, Karl

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