Análise de desempenho de Log Parsers da coleção Logpai em dados brutos de dispositivos Android

• João Alfredo Bessa UFAM
• Ricardo Filho UFAM
• Girlana Souza UFAM
• Larissa Pessoa UFAM
• Raimundo Barreto UFAM
• Rosiane de Freitas UFAM

DOI: https://doi.org/10.5753/wperformance.2024.2423

Resumo

O processo de estruturar arquivos de logs para melhor análise, ou “Log Parsing”, é crucial para extrair informações valiosas de registros gerados por sistemas de software, contribuindo na compreensão das operações e detecção de anomalias no sistema. Neste trabalho é realizada uma análise comparativa de oito ferramentas e modelos de parsing presentes na coleção Logpai, sendo eles, AEL, Brain, Drain, LFA, LogCluster, Logram, NuLog e SHISO, envolvendo dados brutos de Dispositivos Android. Os resultados apresentados demonstram uma perda de precisão nos modelos com instâncias sem pré-processamento, onde as ferramentas existentes apresentam grande dificuldade de manipular logs não tratados, com consequente baixa performance na análise de informações dos Logs Android brutos.

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