Machine Learning-Based Prediction Of Software Behavior

Abstract

The increasing complexity of modern software systems has made it challenging to anticipate system behavior under varying operational conditions. Predictive modeling using machine learning offers a powerful approach to analyze historical execution data and forecast software behavior, enabling proactive decision-making and improved system reliability. This study presents a comprehensive framework for predicting software behavior by leveraging supervised and unsupervised machine learning techniques on software metrics such as execution logs, resource utilization, and user interaction patterns. The proposed approach involves data preprocessing, feature extraction, and the application of models including regression algorithms, decision trees, and ensemble methods to identify patterns and predict outcomes such as performance degradation, fault occurrence, and system anomalies. The model is trained and validated using real-world datasets to ensure accuracy and generalizability. Additionally, techniques for handling imbalanced data and improving model interpretability are incorporated to enhance practical applicability. Experimental results demonstrate that machine learning-based predictive models can significantly improve the accuracy of software behavior prediction compared to traditional rule-based approaches. The findings highlight the potential of predictive analytics in optimizing software maintenance, reducing downtime, and enhancing overall system performance. This research contributes to the development of intelligent, self-adaptive software systems capable of anticipating and responding to dynamic environments.