Enhancing Software Quality with AI: Predicting and Preventing Bugs

Abstract

Defect prevention incorporating techniques from Artificial Intelligence (AI) has become imperative in today’s rapid software evolution. With the increase of AI in day-to-day life, such as self-driving cars, pattern recognition, face identification, etc., it provides machine learning and natural language processing algorithms, and predicting and preventing defects can be easily automated. Using historical defect data, impending failures can be identified by patterns or anomalies well ahead of time. Implementation of AI in the software development lifecycle increases development accuracy, reduces overall debugging cost, and enhances the reliability of software produced. This study further forms a framework for defect predictive and preventive AI systems, discusses the prospects, merits, and restraints of this model, and examines the overall impact. Moreover, issues with the availability of data, the interpretability of the model, and the incorporation into existing practices of software engineering also need to be solved. Future researches are needed to improve the efficiency of AI tools incorporated into controlling and assuring the quality of software.