Authors - Nathula Dayarathne, Guhanathan Poravi Abstract - This paper presents a novel methodology for predicting bug severity and priority in software development using machine learning models. The approach involves leveraging a manually curated dataset labelled with the support of industry experts, enabling the incorporation of domainspecific knowledge into feature selection and classification. A K-Means clustering method is initially employed to label the collected data, ensuring accurate grouping and feature extraction. The study identifies and utilizes 16 key features for classification and develops separate models for severity and priority prediction. These models, trained on the expertly labelled dataset, achieve high performance with accuracy metrics above 90%. This study uniquely combines K-Means pre-labelling with expert validation to reduce manual annotation while maintaining model accuracy. The proposed method demonstrates the effectiveness of combining clustering techniques with expert-driven labelling for improving bug management processes. By automating severity and priority classification, this research contributes to enhancing the efficiency and reliability of software development workflows.
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