MoLFI (Multi-objective Log message Format Identification) is a tool implementing a search-based approach to solve the problem of log message format identification. MoLFI uses an evolutionary approach based on NSGA-II to solve this problem.

MoLFI applies the following steps:

1. Pre-processing the log file (detect trivial variable parts using domain knowledge).
2. Run NSGA-II algorithm.
3. Post-processing: apply corrections to the resulting solutions.

Read more information about MoLFI from the following paper:

• Salma Messaoudi, Annibale Panichella, Domenico Bianculli, Lionel Briand, and Raimondas Sasnauskas. A Search-based Approach for Accurate Identification of Log Message Formats, Proceedings of the 26th IEEE/ACM International Conference on Program Comprehension (ICPC), 2018.

The code has been tested in the following enviornment:

• python 3.7.6
• regex 2022.3.2
• pandas 1.0.1
• numpy 1.18.1
• scipy 1.4.1
• deap 1.4.1

Run the following script to start the demo:

python demo.py

Run the following script to execute the benchmark:

python benchmark.py

Running the benchmark script on Loghub_2k datasets, you could obtain the following results.

🔭 If you use our logparser tools or benchmarking results in your publication, please kindly cite the following papers.

• [ICSE'19] Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He, Qi Xie, Zibin Zheng, Michael R. Lyu. Tools and Benchmarks for Automated Log Parsing. International Conference on Software Engineering (ICSE), 2019.
• [DSN'16] Pinjia He, Jieming Zhu, Shilin He, Jian Li, Michael R. Lyu. An Evaluation Study on Log Parsing and Its Use in Log Mining. IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2016.