AI-Driven Software Testing: A Machine Learning Approach for Automated Bug Detection

The abstract presents a strong case for an AI-driven defect detection model, effectively covering all essential research components within a concise paragraph. It establishes the necessity for the research by highlighting the limitations of traditional testing (time-consuming, poor at complex defects). The core solution is introduced as a hybrid model integrating two cutting-edge techniques: CodeBERT for semantic code understanding and Random Forest for supervised machine learning classification. The use of CodeBERT is crucial as it allows the model to analyze the meaning and context of the code, not just its syntax. Validation is anchored by the use of the recognized Defects4J dataset. Finally, the conclusion asserts a significant improvement in prediction accuracy and reduction in false alarms, demonstrating the practical value of integrating AI to achieve faster bug detection and enhanced software quality. The paragraph functions as a complete, persuasive summary, limited only by the absence of specific numerical results, which is typical for an abstract.