Leveraging Machine Learning for Predictive Bug Analysis

Software quality and reliability are of the utmost concern in modern systems. Nevertheless, the growth in the scale and complexity of software development has made the traditional bug detection analysis techniques inefficient and resource-intensive. This is where machine learning-driven predictive bug analysis comes in handy: allowing one to make predictions by classifying and prioritizing software defects based on previous bug history. This paper explores the application of ML algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, for predictive bug analysis. It discusses data collection, preprocessing techniques, and evaluation metrics such as accuracy, precision, and recall. The results have shown that ML-based models outperform traditional approaches concerning bug prediction accuracy. The paper also points out issues with the quality of datasets and computational overhead but goes ahead to proffer potential improvements that could be achieved using transfer learning and explainable AI techniques. This study highlights the potential transformative impact of ML on the quality of software and strongly encourages its use in mainstream software development.