International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Sarika Chaudhary*, Aman Jatain

Department of Computer Science and Engineering, Amity University, Gurugram, India


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Consistent regression testing (RT) is an abstract class, that considered indispensable for assuring the quality of software systems but it is too expensive. To minimize the computational cost of RT, test case prioritization (TCP) is the most adopted methodology in literature. The implementation of TCP process, performed using various hard clustering techniques but fuzzy clustering, one of the most sought clustering technique for selecting appropriate test cases had not been discover at a wider platform. Therefore, the proposed work discusses a novel density based fuzzy c- mean (NDB-FCM) algorithm with newly derived initialize membership function for prioritizing the test cases. It first, generates optimal number of cluster (Copt) using a density based algorithm, which in turn minimizes the search criteria to find the ‘Copt’, especially in cases where a given data set does not follow the empirical rule. Then, creates an initial fuzzy partition matrix based upon newly suggested initial membership method. In addition, a novel multi-objective prioritization model (NDS-FCMPM) proposed to achieve the performance goal of enhanced fault recognition. Initially, feature extraction carried out by exploiting the dependencies between test cases, and then test cases are clustered using proposed fuzzy clustering approach, which finally, prioritized using a newly developed prioritization algorithm. To validate the performance of suggested fuzzy clustering algorithm two-performance measure namely “Fuzzy Rand Index” and “Run Time” exercised and for prioritization algorithm “APFD” metrics is analysed. The proposed model is assessed using eclipse data extracted from Github Repository. Inferences generated depict that NDB-FCM clustering provide more stable results in terms of classification accuracy, run time and quick convergence when compared with other state-of-the-art techniques. Also, it is verified that NDS-FCMPM observes an improved rate of fault identification at early stage.

Keywords: APFD, Customer requirements, Fuzzy clustering, Feature extraction, Prioritization, Regression testing.

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Received: 2021-03-19

Accepted: 2021-07-01
Available Online: 2021-09-01

Cite this article:

Chaudhary, S., Jatain, A. 2021. Analysing a novel multi-objective prioritization model using improved fuzzy c mean clustering. International Journal of Applied Science and Engineering, 18, 2021092.

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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