Software Log Classification in Telecommunication Industry

Abstract

Software system admins depend on log data for understanding system behavior, monitoring anomalies, tracking software bugs, and malfunctioning detection. Log analysis based on machine learning techniques enables to transform of raw logs into meaningful information that helps the DevOps team and administrators to solve problems. Al ensures to group similar logs together and keeps periodic logs more organized and sorted, allowing us to get to where we need to look faster. In this paper, we present a log classification system on log data generated by VoIP (Voice over Internet Protocol) soft-switch product. In this way, we targeted to detect the problem, direct it to the relevant department, allocate resources, and solve software bugs faster and more efficiently. Machine learning algorithms such as Linear Classifiers, Support Vector Machines, Decision Tree, Random Forest, Boosting, K-Nearest Neighbors, and Multilayer Perceptron are used for log classification.