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LBTest: A Learning-based Testing Tool for 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.
2013 (English)In: Proceedings - IEEE 6th International Conference on Software Testing, Verification and Validation, ICST 2013, IEEE Computer Society, 2013, 447-454 p.Conference paper, Published paper (Refereed)
Abstract [en]

We give an introduction to the LBTest tool which implements learning-based testing for reactive systems. It makes use of incremental learning and model checking algorithms to automate: i) test case generation, ii) test execution and iii) test verdict construction. The paper illustrates the tool by means of a pedagogical case study, to enable the user to setup and learn the tool quickly. We provide a usability exercise to support tool evaluation.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013. 447-454 p.
Keyword [en]
requirements testing, learning-based testing, black-box testing, LBTest
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-119088DOI: 10.1109/ICST.2013.62ISI: 000332473300050Scopus ID: 2-s2.0-84883443673ISBN: 978-0-7695-4968-2 (print)OAI: oai:DiVA.org:kth-119088DiVA: diva2:609696
Conference
IEEE 6th International Conference on Software Testing, Verification and Validation, ICST 2013; Luxembourg; Luxembourg; 18 May 2013 through 20 May 2013
Note

QC 20130312

Available from: 2013-03-06 Created: 2013-03-06 Last updated: 2014-04-10Bibliographically 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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