AI-DRIVEN APPROACH FOR SOFTWARE QUALITY PREDICTION TO IMPROVE ESTIMATION ACCURACY
Keywords:
Keywords: Software quality prediction, Quality categories, Principal Component Analysis (PCA), Gradient Boosting Classifier, Automated prediction model.Abstract
Software quality prediction is crucial in enhancing estimation accuracy and reducing post-releasedefects in software engineering. Studies reveal that over 60% of software projects exceed budget ortime estimates, while 45% of software defects remain undetected until after deployment, and nearly30% of testing efforts are spent on low-risk modules. Despite these concerns, manual software qualityestimation remains error-prone due to subjectivity, lack of real-time analytical support, and inefficienthandling of high-dimensional data
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