Machine learning has emerged as an effective approach for predicting software defects in the early stages of developing complex systems. In this study, historical project statistics and code metrics were collected and preprocessed, while initial software class labels were generated using K-means clustering. Several classification algorithms, including Support Vector Machine (SVM), Random Forest, Naive Bayes, […]

Software bug prediction aims to automatically identify code modules likely to contain defects, improving quality and reducing testing costs. Traditional methods use static code metrics (complexity, churn) with classical machine learning, but these often ignore inter-module relationships. Recently, deep learning (DL) techniques have shown promise by learning features from code text and structure. In particular, […]

Software systems today face increasing demands for high performance and efficient memory usage. Traditional optimization methods, often hard-coded and inflexible, struggle to adapt to complex and dynamic workloads. Machine Learning (ML) algorithms offer a promising approach by automatically learning patterns and making intelligent decisions to optimize execution time (runtime) and memory consumption. This paper provides […]