Combinatorial Testing for Identifying Defect Patterns in Manufacturing

Maslita Abd Aziz, Kamal Z. Zamli and Zuriani Mustaffa
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Maslita Abd Aziz: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, 76100, Malaysia
Kamal Z. Zamli: Fakulti Komputeran, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan, 26600, Pahang, Malaysia
Zuriani Mustaffa: Fakulti Komputeran, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan, 26600, Pahang, Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 9, 8174-8182

Abstract: In the manufacturing industry, improving product quality and reducing defects are crucial objectives. This study investigates the use of combinatorial testing to analyse defect patterns in a manufacturing setting. We utilised a dataset containing various defects attributes on available open-source Kaggle datasets. Pairwise test cases were generated using hybrid metaheuristics to systematically explore interactions between these attributes. The proposed method significantly reduced the number of test cases while ensuring comprehensive coverage of pairwise interactions, compared to exhaustive testing. Results indicate that the combinatorial testing approach effectively identifies defect patterns, reducing the time span for defect identification. The integration of reward and penalty mechanisms with the Roulette Wheel algorithm in our hybrid metaheuristic optimisation process further enhanced the efficiency of candidate solutions for combinatorial testing. This study provides a practical framework for improving defect detection and quality control in manufacturing settings, highlighting the benefits of advanced combinatorial testing techniques.

Date: 2025
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