Automation of Trace Analysis

Software Automation approach involves the use of various tools and frameworks that can automatically perform tasks such as code generation, testing, deployment, and monitoring to avoid the repetitive and time-consuming tasks. By automating routine tasks, software developers can focus on more complex and creative aspects of their work, leading to faster delivery of software products. The analysis of trace log is a crucial step in various fields such as software engineering, cyber-security, and network analysis. However, manual analysis of logs can be time-consuming, error-prone, and difficult to scale up for large log file. Therefore, there is a need for automation in the analysis of trace log to improve efficiency, accuracy, and scalability. In recent years, various AI-based and statistical techniques and tools have been developed for automation of trace analysis. These techniques can be used to identify patterns, anomalies, and correlations in trace data, as well as to generate insights and predictions that can be useful for decision-making. The proposed work aims to develop a system that can be used for easy interpretation of analysis reports that identify failed series of the log file and potential causes of system failure. It intends to streamline log analysis and improve software development efficiency.