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Automated software testing: Evaluation of Angluin's L* algorithm and applications in practice
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Learning-based testing can ensure software quality without a formal documentation or maintained specification of the system under test. Therefore, an automaton learning algorithm is the key component to automatically generate efficient test cases for black-box systems. In the present report Angluin’s automaton learning algorithm L* and the extension called L* Mealy are examined and evaluated in the application area of learning-based software testing. The purpose of this work is to estimate the applicability of the L* algorithm for learning real world software and to describe constraints of this approach. To achieve this, a framework to test the L* implementation on various deterministic finite automata (DFA) was written and an adaptation called L* Mealy was integrated into the learning-based testing platform LBTest. To follow the learning process, the queries that the learner needs to perform on the system to learn are tracked and measured. Both algorithms show a polynomial growth on these queries in case studies from real world business software or on randomly generated DFAs. The test data indicate a better learning performance in practice than the theoretical predictions imply. In contrast to other existing learning algorithms, the L* adaptation L* Mealy performs slowly in LBTest due to a polynomially growing interval between the types of queries that the learner needs to derive a hypothesis.

Place, publisher, year, edition, pages
2014. , 50 p.
National Category
Computer Science
URN: urn:nbn:se:kth:diva-146018OAI: diva2:721652
Subject / course
Computer Science
Educational program
Bachelor of Science in Engineering - Computer Engineering
Available from: 2014-11-25 Created: 2014-06-04 Last updated: 2014-11-25Bibliographically approved

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Czerny, Maximilian
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