Vol.67, No.1, 2021, pp.1051-1068, doi:10.32604/cmc.2021.014686
An Ontology Based Test Case Prioritization Approach in Regression Testing
  • Muhammad Hasnain1, Seung Ryul Jeong2,*, Muhammad Fermi Pasha1, Imran Ghani3
1 Monash University, Petaling Jaya, 46150, Malaysia
2 Kookmin University, Seoul, 136, Korea
3 Indiana University of Pennsylvania, PA, 15705, USA
* Corresponding Author: Seung Ryul Jeong. Email:
Received 08 October 2020; Accepted 26 November 2020; Issue published 12 January 2021
Regression testing is a widely studied research area, with the aim of meeting the quality challenges of software systems. To achieve a software system of good quality, we face high consumption of resources during testing. To overcome this challenge, test case prioritization (TCP) as a sub-type of regression testing is continuously investigated to achieve the testing objectives. This study provides an insight into proposing the ontology-based TCP (OTCP) approach, aimed at reducing the consumption of resources for the quality improvement and maintenance of software systems. The proposed approach uses software metrics to examine the behavior of classes of software systems. It uses Binary Logistic Regression (BLR) and AdaBoostM1 classifiers to verify correct predictions of the faulty and non-faulty classes of software systems. Reference ontology is used to match the code metrics and class attributes. We investigated five Java programs for the evaluation of the proposed approach, which was used to achieve code metrics. This study has resulted in an average percentage of fault detected (APFD) value of 94.80%, which is higher when compared to other TCP approaches. In future works, large sized programs in different languages can be used to evaluate the scalability of the proposed OTCP approach.
Software code metric; machine learning; faults detection; testing
Cite This Article
M. Hasnain, S. R. Jeong, M. F. Pasha and I. Ghani, "An ontology based test case prioritization approach in regression testing," Computers, Materials & Continua, vol. 67, no.1, pp. 1051–1068, 2021.
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