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An Efficient Model Inference Algorithm for Learning-based Testing of Reactive Systems
KTH, Skolan för datavetenskap och kommunikation (CSC), Teoretisk datalogi, TCS.ORCID-id: 0000-0002-9706-5008
KTH, Skolan för datavetenskap och kommunikation (CSC), Teoretisk datalogi, TCS.
2012 (Engelska)Rapport (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
2012. , s. 29
Nyckelord [en]
automata learning, black-box testing, learning-based testing, reactive systems
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:kth:diva-119266OAI: oai:DiVA.org:kth-119266DiVA, id: diva2:610364
Anmärkning

QC 20130312

Tillgänglig från: 2013-03-11 Skapad: 2013-03-11 Senast uppdaterad: 2022-06-24Bibliografiskt granskad
Ingår i avhandling
1. Algorithms and Tools for Learning-based Testing of Reactive Systems
Öppna denna publikation i ny flik eller fönster >>Algorithms and Tools for Learning-based Testing of Reactive Systems
2013 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Stockholm: KTH Royal Institute of Technology, 2013. s. xii, 79
Serie
Trita-CSC-A, ISSN 1653-5723 ; 2013:03
Nyckelord
specification-based testing, learning-based testing, reactive systems, LBTest, case studies
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:kth:diva-119267 (URN)978-91-7501-674-0 (ISBN)
Disputation
2013-04-16, F3, Lindstedtsvägen 26, Kungliga Tekniska Högskolan, Stockholm, 10:00 (Engelska)
Opponent
Handledare
Anmärkning

QC 20130312

Tillgänglig från: 2013-03-12 Skapad: 2013-03-11 Senast uppdaterad: 2022-06-24Bibliografiskt granskad

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Meinke, Karl

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Meinke, KarlSindhu, Muddassar
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