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Case Studies in Learning-based Testing
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
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.
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
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2013 (English)Report (Other academic)
Abstract [en]

We present case studies which show how the paradigm of learning-based testing (LBT) can be successfully applied to black-box requirements testing of reactive systems. For this we apply a new testing tool LBTest, which combines algorithms for incremental black-box learning of Kripke structures with model checking technology. We show how test requirements can be modeled in propositional linear temporal logic extended by finite abstract data types. We provide benchmark performance results for LBTest applied to two industrial case studies. Finally we present a first coverage study for the tool.

Place, publisher, year, edition, pages
2013. , 22 p.
Keyword [en]
Learning-based Testing, Specification-based Testing, case studies
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-119265OAI: oai:DiVA.org:kth-119265DiVA: diva2:610361
Note

QC 20130312

Available from: 2013-03-11 Created: 2013-03-11 Last updated: 2015-03-03Bibliographically 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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