Predicting the Quality of Software by Using Cat Swarm Optimization (CSO) Algorithm
DOI:
https://doi.org/10.29304/jqcsm.2025.17.11981Keywords:
Software Defect, Machine Learning, Prediction, OptimizationAbstract
In the early stages of software engineering, efforts were made to enhance software quality by using quality indicators that are considered vital in software development. Software testing was improved and software issues were identified via the use of predictive quality metrics. An evaluation, testing, and analysis system based on a dataset extracted from the NASA quality metrics database utilising a cat swarm optimization approach is the goal of this project, this is doing by applying two steps: first step is preprocessing of the data to process the missing value and after that apply normalization, second step using CSO algorithm to find the goal of this study is to predicting the quality of software. This research demonstrates how effective machine learning approaches are at extracting knowledge from big databases and offers useful insights into software quality prediction, when compared to all of the classification techniques used in the research, the results demonstrated that the approach considerably improved the accuracy rate to 96% in predicting quality performance.
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