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An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9051-7609
Mälardalen University, Västerås, Sweden.
Mälardalen University, Västerås, Sweden.
University Malaysia Pahang, Pekan, Malaysia.
2020 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479Article in journal (Refereed) Epub ahead of print
Abstract [en]

Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. This work focuses on the selection type of hyper-heuristic, called the exponential Monte Carlo with counter (EMCQ). Current implementations rely on the memory-less selection that can be counterproductive as the selected search operator may not (historically) be the best performing operator for the current search instance. Addressing this issue, we propose to integrate the memory into EMCQ for combinatorial t-wise test suite generation using reinforcement learning based on the Q-learning mechanism, called Q-EMCQ. The limited application of combinatorial test generation on industrial programs can impact the use of such techniques as Q-EMCQ. Thus, there is a need to evaluate this kind of approach against relevant industrial software, with a purpose to show the degree of interaction required to cover the code as well as finding faults. We applied Q-EMCQ on 37 real-world industrial programs written in Function Block Diagram (FBD) language, which is used for developing a train control management system at Bombardier Transportation Sweden AB. The results show that Q-EMCQ is an efficient technique for test case generation. Addition- ally, unlike the t-wise test suite generation, which deals with the minimization problem, we have also subjected Q-EMCQ to a maximization problem involving the general module clustering to demonstrate the effectiveness of our approach. The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including modified choice function, Tabu high-level hyper-heuristic, teaching learning-based optimization, sine cosine algorithm, and symbiotic optimization search in clustering quality within comparable execution time.

Place, publisher, year, edition, pages
Springer, 2020.
Keywords [en]
Search-based software engineering (SBSE), Fault finding, System reliability, Software testing, Hyper-heuristics
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-77050DOI: 10.1007/s00500-020-04769-zISI: 000516083100001OAI: oai:DiVA.org:kau-77050DiVA, id: diva2:1396175
Available from: 2020-02-25 Created: 2020-02-25 Last updated: 2020-04-02Bibliographically approved

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Ahmed, Bestoun S.

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