Affiliation:
1. Nokia, Rodziny Hiszpanskich 8, 02-685 Warszawa, Poland
2. Politechnika Wroclawska, Wybrzeze Stanisława Wyspianskiego 27, 50-370 Wrocław, Poland
Abstract
The use of automated methods for log analysis is unavoidable in any large company; therefore, it has attracted attention from engineers and researchers. As a result, the number of articles in the field grows yearly and new approaches are frequently proposed. Unfortunately, published research works only sometimes meet the needs of engineers wishing to apply the methods in real-life systems. A common issue is that the method’s benefits often do not compensate for the effort required for its implementation and maintenance. Therefore, engineers must understand the pros and cons of full-scale applications, including the implementation details and the required effort. This work provides a comprehensive review of automated log analysis methods and aims to offer a guide for software engineers who fix integration and production failures. The article categorizes and provides an overview of existing methods and assesses their implementation and maintenance costs, as well as the feasibility of the methods. The article also identifies and describes the shortcomings of existing methods, including concept drift, which is not addressed with sufficient attention, as well as the lack of online benchmarks and the interpretation of the log sequence as a language, without an in-depth analysis of its properties. Despite growing efforts to provide feasible and widely adopted solutions, many reference implementations are unavailable. Consequently, the time and computation complexities differ between various implementations of the same approaches, making the results of research work difficult to replicate in real-life scenarios.
Funder
Polish Ministry of Education and Science
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Cited by
2 articles.
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1. Fault Diagnosis for Test Alarms in Microservices through Multi-source Data;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10
2. Unsupervised Log Sequence Segmentation;IEEE Access;2024