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Title: | Predicting software anomalies using machine learning techniques |
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Author: | Alonso, Javier; Belanche Muñoz, Luis Antonio; Avresky, Dimiter |
Other authors: | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
Abstract: | In this paper, we present a detailed evaluation of a set of well-known Machine Learning classifiers in front of dynamic and non-deterministic software anomalies. The system state prediction is based on monitoring system metrics. This allows software proactive rejuvenation to be triggered automatically. Random Forest approach achieves validation errors less than 1% in comparison to the well-known ML algorithms under avaluation. In order to reduce automatically the number of monitored parameters, needed to predict software anomalies, we analyze Lasso Regularization technique jointly with the Machine Learning classifiers to evaluate how the prediction accuracy could be guaranteed within an acceptable threshold. This allows to reduce drastically (around 60% in the best case) the number of monitoring parameters. The framework, based on ML and Lasso regularization techniques, has been validated using an e-commerce environment with Apache Tomcat server, and MySql database server. |
Subject(s): | -Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic -Machine learning -Software failures -- Prevention -Computer crashes -Instruction sets -Machine learning algorithms -Monitoring -Prediction algorithms -Predictive models -Aprenentatge automàtic -Programari -- Control de qualitat |
Rights: | Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Document type: | Article - Submitted version Conference Object |
Published by: | IEEE Computer Society Publications |
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