Feature Weight Based Fuzzy C-Means Clustering with Optimal Initialization for Software Fault Prediction
Feature Weight Based Fuzzy C-Means Clustering with Optimal Initialization for Software Fault Prediction |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : Yuvaraj K, Balaji N V | ||
| DOI : 10.14445/22315381/IJETT-V73I7P122 | ||
How to Cite?
Yuvaraj K, Balaji N V, "Feature Weight Based Fuzzy C-Means Clustering with Optimal Initialization for Software Fault Prediction," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.280-292, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P122
Abstract
In this digital era, software is ruling the world by making the life of humans easier and more convenient in many ways. Not only in business, but software is also required for each specific field. Software development has become a predominant and common field that provides services to every other field of science and engineering. However, the primary challenge in developing software is to identify and fix the faults that occur in various circumstances as early as possible to minimize the time, effort, and associated inconvenience. This paper proposes an effective software fault prediction framework to identify the fault modules in software projects. The model applies accelerated k-means clustering for identifying the count of clusters by evaluating gap statistics. Then, fuzzy clustering is applied over the training set, which makes use of a probability distribution for initializing cluster centroids and feature weights to compute the similarity between the samples and the cluster centroids. As a result, samples inside the cluster are strengthened and samples outside the cluster are weakened. Moreover, it also helps to increase the quality of the clusters and accelerates the convergence of the clustering process by reducing the iterations. Using the classification model, the modules are categorized as non-defective or defective based on their high population in the relevant cluster. The effectiveness of the proposed model has been tested experimentally, and the findings show that the framework can successfully identify defective software modules in less time and with higher accuracy.
Keywords
Accelerated k-means, Feature weight, Fuzzy c-means clustering, Probability distribution, Software fault prediction.
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