Software Bug Prediction Using Machine Learning

Software bug prediction is a crucial aspect of software development and maintenance as it directly impacts the overall success of the software. By identifying potential bugs early on, software quality, reliability, and efficiency can be improved while also helping to reduce costs. Creating a reliable bug prediction model is a complex task, with various techniques proposed in the literature. In this paper, a bug prediction model utilizing machine learning algorithms is introduced. Three supervised machine learning algorithms have been incorporated to forecast future software faults using historical data. The evaluation process revealed that Machine Learning algorithms, specifically Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Networks (ANNs), can be effectively utilized with a high level of accuracy. Additionally, a comparison measure was implemented to assess the proposed prediction model against other methods. The findings indicated that the Machine Learning approach outperformed other techniques in terms of performance.