GENETIC PROGRAMMING FOR AUTOMATED ERROR HANDLING AND RECOVERY IN DEVOPS ENVIRONMENTS

Abstract

Efficient error handling and recovery mechanisms are critical in contemporary DevOps environments to maintain system reliability and operational efficiency. Traditional methodologies often prove inadequate in addressing the complexities of modern distributed systems, sparking interest in advanced techniques such as Genetic Programming (GP) algorithms for automation. This research investigates the potential of GP algorithms in automating error handling and recovery within DevOps, aiming to develop self-healing systems capable of autonomously detecting, diagnosing, and mitigating errors. Focused on delineating, executing, and assessing GP algorithms tailored for error management, this inquiry aims to elucidate challenges and prospects in this domain. Through a comprehensive approach encompassing literature review, theoretical analysis, and empirical case studies, this study delves into the fundamental tenets of GP algorithms and their relevance to DevOps. While GP algorithms show promise for enhancing system reliability and efficiency, addressing challenges such as scalability and interpretability remains crucial for their effective integration into DevOps practices.