A Multi-Parser Benchmark over Android Bug-Report Logs
Resumo
A análise sintática de logs converte linhas em templates de eventos e é a primeira etapa da maioria dos pipelines de mineração, mas o comportamento dos parsers em logs móveis é pouco caracterizado. Apresentamos o ABRLogParser, um benchmark multi-parser que expande o conjunto ABRLog de bug reports Android aplicando treze parsers clássicos aos seus oito blocos extraídos por expressões regulares: 342 arquivos, 14 dispositivos e 32,0 milhões de linhas. Cada par (arquivo, parser) gera saída estruturada e resumo de templates publicados, e cada parser é caracterizado por conteúdo e por execução. A abstração varia por um fator de 906 entre parsers e a vazão cerca de cinquenta vezes; os padrões do LogPAI, ajustados para servidores, degeneram em transformações quase identidade no Android. Sem templates de referência, o artefato é um substrato para comparação de parsers, não um benchmark de acurácia.
Referências
Bessa, J. A., Miranda Filho, R., Barreto, R., and de Freitas, R. (2025a). A multi-device Android mobile raw log dataset for intelligent analysis. In XV Symposium on Computing Systems Engineering (SBESC), pages 1–6. IEEE.
Bessa, J. A., Miranda Filho, R., Barreto, R., and de Freitas, R. (2026). ABRLogParser: A multi-parser expansion of the ABRLog Android bug-report log dataset. DOI: 10.5281/zenodo.21033598.
Bessa, J. A., Miranda Filho, R., Souza, G., Barreto, R., and de Freitas, R. (2025b). Log parsers performance on raw logs from Android devices. Journal of Internet Services and Applications (JISA), 16(1):105–116.
Dai, H., Li, H., Chen, C.-S., Shang, W., and Chen, T.-H. (2022). Logram: Efficient log parsing using n-gram dictionaries. IEEE Trans. Software Engineering, 48(3):879–892.
Du, M. and Li, F. (2016). Spell: Streaming parsing of system event logs. In Proc. IEEE 16th Int. Conf. Data Mining (ICDM), pages 859–864.
Hamooni, H., Debnath, B., Xu, J., Zhang, H., Jiang, G., and Mueen, A. (2016). LogMine: Fast pattern recognition for log analytics. In Proc. 25th ACM Int. Conf. Information and Knowledge Management (CIKM), pages 1573–1582.
He, P., Zhu, J., He, S., Li, J., and Lyu, M. R. (2016). An evaluation study on log parsing and its use in log mining. In Proc. 46th Annual IEEE/IFIP Int. Conf. Dependable Systems and Networks (DSN), pages 654–661.
He, P., Zhu, J., Zheng, Z., and Lyu, M. R. (2017). Drain: An online log parsing approach with fixed depth tree. In Proc. IEEE Int. Conf. Web Services (ICWS), pages 33–40.
Jiang, Z. M., Hassan, A. E., Hamann, G., and Flora, P. (2008). Abstracting execution logs to execution events for enterprise applications. In Proc. 8th Int. Conf. Quality Software (QSIC), pages 181–186.
Karagiannis, S., Ntantogian, C., Magkos, E., et al. (2023). ChiDroid: A mobile Android application for log collection and security analysis in healthcare and IoMT. Applied Sciences, 13(5):3061.
Makanju, A. A. O., Zincir-Heywood, A. N., and Milios, E. E. (2009). Clustering event logs using iterative partitioning. In Proc. 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), pages 1255–1264.
Messaoudi, S., Panichella, A., Bianculli, D., Briand, L., and Sasnauskas, R. (2018). A search-based approach for accurate identification of log message formats. In Proc. 26th IEEE/ACM Int. Conf. Program Comprehension (ICPC), pages 167–177.
Miranda Filho, R., Bessa, J. A., Barreto, R., and de Freitas, R. (2025). ABRLog: A multi-device Android bug-report log dataset. DOI: 10.5281/zenodo.19401760.
Mizutani, M. (2013). Incremental mining of system log format. In Proc. IEEE Int. Conf. Services Computing (SCC), pages 595–602.
Shima, K. (2016). Length matters: Clustering system log messages using length of words. arXiv preprint arXiv:1611.03213.
Tang, L., Li, T., and Perng, C.-S. (2011). LogSig: Generating system events from raw textual logs. In Proc. 20th ACM Int. Conf. Information and Knowledge Management (CIKM), pages 785–794.
Vaarandi, R. and Pihelgas, M. (2015). LogCluster – a data clustering and pattern mining algorithm for event logs. In Proc. 11th Int. Conf. Network and Service Management (CNSM), pages 1–7.
Yu, S., He, P., Chen, N., and Wu, Y. (2023). Brain: Log parsing with bidirectional parallel tree. IEEE Trans. Services Computing, 16(5):3224–3237.
Zhu, J., He, S., He, P., Liu, J., and Lyu, M. R. (2023). Loghub: A large collection of system log datasets for AI-driven log analytics. In Proc. IEEE/ACM Int. Symp. Empirical Software Engineering and Measurement (ESEM).
Zhu, J., He, S., Liu, J., He, P., Xie, Q., Zheng, Z., and Lyu, M. R. (2019). Tools and benchmarks for automated log parsing. In Proc. IEEE/ACM 41st Int. Conf. Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 121–130.
Bessa, J. A., Miranda Filho, R., Barreto, R., and de Freitas, R. (2026). ABRLogParser: A multi-parser expansion of the ABRLog Android bug-report log dataset. DOI: 10.5281/zenodo.21033598.
Bessa, J. A., Miranda Filho, R., Souza, G., Barreto, R., and de Freitas, R. (2025b). Log parsers performance on raw logs from Android devices. Journal of Internet Services and Applications (JISA), 16(1):105–116.
Dai, H., Li, H., Chen, C.-S., Shang, W., and Chen, T.-H. (2022). Logram: Efficient log parsing using n-gram dictionaries. IEEE Trans. Software Engineering, 48(3):879–892.
Du, M. and Li, F. (2016). Spell: Streaming parsing of system event logs. In Proc. IEEE 16th Int. Conf. Data Mining (ICDM), pages 859–864.
Hamooni, H., Debnath, B., Xu, J., Zhang, H., Jiang, G., and Mueen, A. (2016). LogMine: Fast pattern recognition for log analytics. In Proc. 25th ACM Int. Conf. Information and Knowledge Management (CIKM), pages 1573–1582.
He, P., Zhu, J., He, S., Li, J., and Lyu, M. R. (2016). An evaluation study on log parsing and its use in log mining. In Proc. 46th Annual IEEE/IFIP Int. Conf. Dependable Systems and Networks (DSN), pages 654–661.
He, P., Zhu, J., Zheng, Z., and Lyu, M. R. (2017). Drain: An online log parsing approach with fixed depth tree. In Proc. IEEE Int. Conf. Web Services (ICWS), pages 33–40.
Jiang, Z. M., Hassan, A. E., Hamann, G., and Flora, P. (2008). Abstracting execution logs to execution events for enterprise applications. In Proc. 8th Int. Conf. Quality Software (QSIC), pages 181–186.
Karagiannis, S., Ntantogian, C., Magkos, E., et al. (2023). ChiDroid: A mobile Android application for log collection and security analysis in healthcare and IoMT. Applied Sciences, 13(5):3061.
Makanju, A. A. O., Zincir-Heywood, A. N., and Milios, E. E. (2009). Clustering event logs using iterative partitioning. In Proc. 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), pages 1255–1264.
Messaoudi, S., Panichella, A., Bianculli, D., Briand, L., and Sasnauskas, R. (2018). A search-based approach for accurate identification of log message formats. In Proc. 26th IEEE/ACM Int. Conf. Program Comprehension (ICPC), pages 167–177.
Miranda Filho, R., Bessa, J. A., Barreto, R., and de Freitas, R. (2025). ABRLog: A multi-device Android bug-report log dataset. DOI: 10.5281/zenodo.19401760.
Mizutani, M. (2013). Incremental mining of system log format. In Proc. IEEE Int. Conf. Services Computing (SCC), pages 595–602.
Shima, K. (2016). Length matters: Clustering system log messages using length of words. arXiv preprint arXiv:1611.03213.
Tang, L., Li, T., and Perng, C.-S. (2011). LogSig: Generating system events from raw textual logs. In Proc. 20th ACM Int. Conf. Information and Knowledge Management (CIKM), pages 785–794.
Vaarandi, R. and Pihelgas, M. (2015). LogCluster – a data clustering and pattern mining algorithm for event logs. In Proc. 11th Int. Conf. Network and Service Management (CNSM), pages 1–7.
Yu, S., He, P., Chen, N., and Wu, Y. (2023). Brain: Log parsing with bidirectional parallel tree. IEEE Trans. Services Computing, 16(5):3224–3237.
Zhu, J., He, S., He, P., Liu, J., and Lyu, M. R. (2023). Loghub: A large collection of system log datasets for AI-driven log analytics. In Proc. IEEE/ACM Int. Symp. Empirical Software Engineering and Measurement (ESEM).
Zhu, J., He, S., Liu, J., He, P., Xie, Q., Zheng, Z., and Lyu, M. R. (2019). Tools and benchmarks for automated log parsing. In Proc. IEEE/ACM 41st Int. Conf. Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 121–130.
Publicado
19/10/2026
Como Citar
BESSA, João Alfredo; MIRANDA FILHO, Ricardo; FREITAS, Rosiane de.
A Multi-Parser Benchmark over Android Bug-Report Logs. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 14. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 33-40.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2026.32039.
