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Can Pairwise Testing Perform Comparably to Manually Handcrafted Testing Carried Out by Industrial Engineers?
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-2416-4205
2017 (English)In: Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 92-99Conference paper, Published paper (Refereed)
Abstract [en]

Testing is an important activity in engineering of industrial software. For such software, testing is usually performed manually by handcrafting test suites based on specific design techniques and domain-specific experience. To support developers in testing, different approaches for producing good test suites have been proposed. In the last couple of years combinatorial testing has been explored with the goal of automatically combining the input values of the software based on a certain strategy. Pairwise testing is a combinatorial technique used to generate test suites by varying the values of each pair of input parameters to a system until all possible combinations of those parameters are created. There is some evidence suggesting that these kinds of techniques are efficient and relatively good at detecting software faults. Unfortunately, there is little experimental evidence on the comparison of these combinatorial testing techniques with, what is perceived as, rigorous manually handcrafted testing. In this study we compare pairwise test suites with test suites created manually by engineers for 45 industrial programs. The test suites were evaluated in terms of fault detection, code coverage and number of tests. The results of this study show that pairwise testing, while useful for achieving high code coverage and fault detection for the majority of the programs, is almost as effective in terms of fault detection as manual testing. The results also suggest that pairwise testing is just as good as manual testing at fault detection for 64% of the programs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. p. 92-99
Keyword [en]
code coverage, combinatorial testing, fault detection, industrial control software, manual testing, PLC, C (programming language), Codes (symbols), Computer software selection and evaluation, Programmable logic controllers, Software reliability, Testing, Combinatorial techniques, Experimental evidence, Industrial controls, Industrial programs, Industrial software, Software testing
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-37415DOI: 10.1109/QRS-C.2017.23ISI: 000426819400013Scopus ID: 2-s2.0-85034440318ISBN: 9781538620724 (print)OAI: oai:DiVA.org:mdh-37415DiVA, id: diva2:1163695
Conference
2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017, 25 July 2017 through 29 July 2017
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-03-22Bibliographically approved

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Enoiu, Eduard Paul

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