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Handling constraints in combinatorial interaction testing in the presence of multi objective particle swarm and multithreading

Ahmed, Bestoun S., 1982- (författare)
Ist Dalle Molle Studi sullIntelligenza Artificial, CH-6928 Manno Lugano, Switzerland.;Czech Tech Univ, Fac Elect Engn, Dept Comp Sci, Karlovo Nam 13, Prague 12135 2, Czech Republic. CZE,Istituto Dalle Molle di Studi sullIntelligenza Artificiale (IDSIA), Switzerland
Gambardella, Luca M. (författare)
Ist Dalle Molle Studi sullIntelligenza Artificial, CH-6928 Manno Lugano, Switzerland. CHE,Istituto Dalle Molle di Studi sullIntelligenza Artificiale (IDSIA), Switzerland
Afzal, Wasif (författare)
Mälardalens högskola,Inbyggda system,Mlardalen Univ, Sch Innovat Design & Engn, Vasteras, Sweden.
visa fler...
Zamli, Kamal Z. (författare)
Univ Malaysia Pahang, Fac Comp Syst & Software Engn, Gambang, Malaysia. MYS,University Malaysia Pahang, Gambang, Malaysia
visa färre...
Ist Dalle Molle Studi sullIntelligenza Artificial, CH-6928 Manno Lugano, Switzerland;Czech Tech Univ, Fac Elect Engn, Dept Comp Sci, Karlovo Nam 13, Prague 12135 2, Czech Republic. CZE Istituto Dalle Molle di Studi sullIntelligenza Artificiale (IDSIA), Switzerland (creator_code:org_t)
Elsevier, 2017
2017
Engelska.
Ingår i: Information and Software Technology. - : Elsevier. - 0950-5849 .- 1873-6025. ; 86, s. 20-36
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Context: Combinatorial testing strategies have lately received a lot of attention as a result of their diverse applications. In its simple form, a combinatorial strategy can reduce several input parameters (configurations) of a system into a small set based on their interaction (or combination). In practice, the input configurations of software systems are subjected to constraints, especially in case of highly configurable systems. To implement this feature within a strategy, many difficulties arise for construction. While there are many combinatorial interaction testing strategies nowadays, few of them support constraints. Objective: This paper presents a new strategy, to construct combinatorial interaction test suites in the presence of constraints. Method: The design and algorithms are provided in detail. To overcome the multi-judgement criteria for an optimal solution, the multi-objective particle swarm optimisation and multithreading are used. The strategy and its associated algorithms are evaluated extensively using different benchmarks and comparisons. Results: Our results are promising as the evaluation results showed the efficiency and performance of each algorithm in the strategy. The benchmarking results also showed that the strategy can generate constrained test suites efficiently as compared to state-of-the-art strategies. Conclusion: The proposed strategy can form a new way for constructing of constrained combinatorial interaction test suites. The strategy can form a new and effective base for future implementations. (C) 2017 Elsevier B.V. All rights reserved.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

Constrained combinatorial interaction
Multi-objective particle swarm optimisation
Test generation tools
Search-based software engineering
Test case design techniques
Computer Science
Datavetenskap

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