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Incremental Learning and Testing of Reactive Systems
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-9706-5008
2011 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis concerns the design, implementation and evaluation of a specification based testing architecture for reactive systems using the paradigm of learning-based testing. As part of this work we have designed, verified and implemented new incremental learning algorithms for DFA and Kripke structures.These have been integrated with the NuSMV model checker to give a new learning-based testing architecture. We have evaluated our architecture on case studies and shown that the method is effective.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology , 2011. , x, 45 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2011:14
Keyword [en]
Incremental learning, software testing, specification based testing, reactive systems, model checking
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-37763ISBN: 978-91-7501-062-5 (print)OAI: oai:DiVA.org:kth-37763DiVA: diva2:435171
Presentation
2011-09-30, K2, Teknikringen 28, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note
QC 20110822Available from: 2011-08-22 Created: 2011-08-17 Last updated: 2011-08-22Bibliographically approved
List of papers
1. Correctness and Performance of an Incremental Learning Algorithm for Finite Automata
Open this publication in new window or tab >>Correctness and Performance of an Incremental Learning Algorithm for Finite Automata
2010 (English)Report (Other academic)
Abstract [en]

We present a new algorithm IDSfor incremental learning of deterministic finite automata (DFA). This algorithm is based on the concept of distinguishing sequences introduced in [Angluin 1981]. We give a rigorous proof that two versions of this learning algorithm correctly learn in the limit. Finally we present an empirical performance analysis that compares these two algorithms, focussing on learning times and different types of learning queries. We conclude that IDSis an efficient algorithm for software engineering applications of automata learning, such as testing and model inference.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2010. 19 p.
National Category
Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-37678 (URN)
Note
QC 20110816Available from: 2011-08-16 Created: 2011-08-16 Last updated: 2011-08-22Bibliographically approved
2. Incremental Learning based Testing for Reactive Systems
Open this publication in new window or tab >>Incremental Learning based Testing for Reactive Systems
2011 (English)In: / [ed] Martin Gogolla and Burkhart Wolff, 2011, 134-151 p.Conference paper, Published paper (Refereed)
Abstract [en]

We show how the paradigm of learning-based testing (LBT)can be applied to automate specification-based black-box testing of reactivesystems. Since reactive systems can be modeled as Kripke structures,we introduce an efficient incremental learning algorithm IKL forsuch structures. We show how an implementation of this algorithm combinedwith an efficient model checker such as NuSMV yields an effectivelearning-based testing architecture for automated test case generation(ATCG), execution and evaluation, starting from temporal logic requirements.

Series
Lecture Notes In Computer Science, 6706
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-37339 (URN)10.1007/978-3-642-21768-5_11 (DOI)2-s2.0-79960247126 (Scopus ID)978-3-642-21767-8 (ISBN)
Conference
5th International Conference on Tests and Proofs (TAP)
Note
QC 20110822Available from: 2011-08-09 Created: 2011-08-09 Last updated: 2013-03-12Bibliographically approved

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CiteExportLink to record
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Citation style
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Output format
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