AML: A Novel Accuracy Metric Model for Critical Evaluation of Log Parsing Techniques ( rebuttal_package)

This repository contains a collection of scripts for preparing data and calculating the Accuracy Metrics for Log Parsing (AML). AML is a comprehensive evaluation metric designed to assess the performance of log parsing techniques accurately.

The provided scripts offer a streamlined workflow for preparing log data, including preprocessing, cleaning, and formatting. They ensure that the data is in a suitable format for subsequent AML calculations. Additionally, the scripts facilitate the extraction of relevant log features and templates, allowing for effective log parsing analysis.

Once the data is prepared, the repository includes scripts for calculating the AML metric. AML provides a holistic assessment of log parsing accuracy, considering both template-level and file-level evaluations. The metric considers the correctness of template identification, log event association, and overall coherence of log file organization.

The scripts are implemented in a user-friendly manner, with clear instructions and examples provided. They are written in python and can be easily adapted to different log parsing scenarios and datasets. Researchers and practitioners in the field of log analysis and parsing can utilize these scripts to evaluate and compare the performance of different log parsing techniques.

By leveraging these scripts, users can gain valuable insights into the strengths and weaknesses of log parsing methods, identify potential errors and misclassifications, and make informed decisions on improving and selecting appropriate log parsers for specific systems or applications.

These scripts are intended to contribute to the advancement of log parsing research and support the development of more accurate and effective log analysis techniques.