Automated AI Tool for Log File Analysis

Log file analysis has a critical role in monitoring and maintaining software systems, yet the manual inspection of logs becomes increasingly impractical with the growing volume of data. This survey paper explores recent advancements in automated log file analysis, with a particular focus on the integration of AI techniques., which includes ML models and NLP. The study identifies key challenges in traditional methods, such as the need for human interpretation., difficulty in detecting new errors, and issues in backtracking within continuous data streams. Moreover, we examine state-of-the- art AI approaches., like LLaMA 2, to streamline log analysis by automating error detection., summarization, and anomaly identification. Research deficiencies are identified, notably the necessity for advanced methodologies to manage variety of log formats, automated model optimization, and ongoing learning processes. This comprehensive review endeavors to provide a thorough examination of the current landscape, encompassing perspectives on potential outcomes and prospective trajectories in the domain of AI -enhanced log file analysis. In addition to providing insights into prospective solutions and future directions in AI-driven log file analysis, this study attempts to give an in-depth analysis of the current situation.