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A systematic literature review on automated log abstraction techniques

El-Masri Diana, Petrillo Fabio, Guéhéneuc Yann-Gaël, Hamou-Lhadj Abdelwahab et Bouziane Anas. (2020). A systematic literature review on automated log abstraction techniques. Information and Software Technology, 122, p. 106276.

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URL officielle: http://dx.doi.org/doi:10.1016/j.infsof.2020.106276

Résumé

Context: Logs are often the first and only information available to software engineers to understand and debug their systems. Automated log-analysis techniques help software engineers gain insights into large log data. These techniques have several steps, among which log abstraction is the most important because it transforms raw log-data into high-level information. Thus, log abstraction allows software engineers to perform further analyses. Existing log-abstraction techniques vary significantly in their designs and performances. To the best of our knowledge, there is no study that examines the performances of these techniques with respect to the following seven quality aspects concurrently: mode, coverage, delimiter independence, efficiency,scalability, system knowledge independence, and parameter tuning effort.

Objectives: We want (1) to build a quality model for evaluating automated log-abstraction techniques and (2) to evaluate and recommend existing automated log-abstraction techniques using this quality model.

Method: We perform a systematic literature review (SLR) of automated log-abstraction techniques. We review 89 research papers out of 2,864 initial papers.

Results: Through this SLR, we (1) identify 17 automated log-abstraction techniques, (2) build a quality model composed of seven desirable aspects: mode, coverage, delimiter independence, efficiency, scalability, system knowledge independence, and parameter tuning effort, and (3) make recommendations for researchers on future research directions.

Conclusion: Our quality model and recommendations help researchers learn about the state-of-the-art automated log-abstraction techniques, identify research gaps to enhance existing techniques, and develop new ones. We also support software engineers in understanding the advantages and limitations of existing techniques and in choosing the suitable technique to their unique use cases.

Type de document:Article publié dans une revue avec comité d'évaluation
ISSN:09505849
Volume:122
Pages:p. 106276
Version évaluée par les pairs:Non
Date:1 Juin 2020
Identifiant unique:10.1016/j.infsof.2020.106276
Sujets:Sciences naturelles et génie > Sciences mathématiques > Informatique
Département, module, service et unité de recherche:Départements et modules > Département d'informatique et de mathématique
Mots-clés:log abstraction techniques, log analysis, log mining, log parsing, software analysis, software log, systematic literature review, systematic survey, AIOps, data mining, log management, quality model
Déposé le:11 févr. 2021 19:31
Dernière modification:11 févr. 2021 19:31
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Creative Commons LicenseSauf indication contraire, les documents archivés dans Constellation sont rendus disponibles selon les termes de la licence Creative Commons "Paternité, pas d'utilisation commerciale, pas de modification" 2.5 Canada.

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