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School of
Computer Science
and Communication

^ Up to Research, Theory group at Nada, KTH.

Learning-based Software Testing

Learning-based testing (LBT) is a new paradigm for black-box specification-based testing of software systems. This approach combines model-based testing methods (using model checkers) with machine learning algorithms to produce efficient search heuristics for testing. We have designed, implemented and benchmarked a number of LBT algorithms. This research is an ongoing project involving PhD and Masters level students, as well as international collaborators and projects.

Researcher Leader

Karl Meinke

Post Docs

Graduate students

Mateus de Oliveira Oliveira,

Niu Fei,

Mudassar Sindhu

Publications

K. Meinke and M. Sindhu: An n log n algorithm for Deterministic Kripke Structure Minimization, submitted for journal publication, 2012.

K. Meinke and F. Niu: An Incremental Learning Algorithm for Extended Mealy Automata, to appear in B. Steffen and T. Margharia (eds) Proc. 2012 Int. ISoLA workshop on Machine Learning for Software Construction, LNCS, Springer Verlag, 2012.

K. Meinke, F. Niu and M. Sindhu: Learning-Based Software Testing: a Tutorial, invited paper to appear in B. Steffen and R. Haehnle (eds) Proc. Int. ISoLA workshop on Machine Learning for Software Construction, CCIS, Springer Verlag, 2011.

F. Niu: Learning-based Software Testing using Symbolic Constraint Solving Methods, Licentiate Thesis, School of Computer Science and Communication, Royal Institute of Technology, Stockholm, Sweden, 2011, ISBN 978-91-7501-117-2.

K. Meinke and M. Sindhu: Correctness and Performance of an Incremental Learning Algorithm for Finite Automata, accepted for poster presentation at Third Asian Conference on Machine Learning (ACML 2011), 13-15 November 2011, Taiwan.

K. Meinke and F. Niu, Learning-Based Testing for Reactive Systems using Term Rewriting Technology, pp 97-114 in B. Wolff and F Zaidi (eds) Proc. 23rd IFIP Int. Conf. on Testing Software and Systems (ICTSS 2011), LNCS 7019, Springer Verlag, 2011.

M. Sindhu, Incremental Learning and Testing of Reactive Systems Licentiate Thesis, School of Computer Science and Communication, Royal Institute of Technology, Stockholm, Sweden, 2011, ISBN 978-91-7501-062-5.

K. Meinke and M. Sindhu, Incremental Learning-Based testing for Reactive Systems, pp 134-151 in: Proc. Int. Conf. on Tests and Proofs TAP 2011, LNCS 6706, Springer Verlag, 2011.

K. Meinke and F. Niu, A Learning-Based Approach to Unit Testing of Numerical Software, pp 221-235 in A. Petrenko et al. (eds) Proc. 22nd IFIP Int. Conf. on Testing Software and Systems (ICTSS 2010), LNCS 6435, Springer Verlag, 2010.

K. Meinke, CGE: a Sequential Learning Algorithm for Mealy Automata, pp 148-162 in J.M. Sempere and P. Garcia (eds), Proc. 10th Int. Colloquium on Grammatical Inference, (ICGI 2010), LNCS 6339, Springer Verlag, 2010.

Here is my original paper on learning-based testing. This paper appeared as: K. Meinke, Automated Black-Box Testing of Functional Correctness using Function Approximation, pp 143-153 in: G. Rothermel (ed) Proc. ACM SIGSOFT Int. Symp. on Software Testing and Analysis, ISSTA 2004, Software Engineering Notes 29 (4), ACM Press, 2004.

Published by: Karl Meinke <karlm@nada.kth.se>
Updated 2012-05-28