Automatic Log Analysis using Machine Learning : Awesome Automatic Log Analysis version 2.0

Sammanfattning: Many problems exist in the testing of a large scale system. The automated testing results are not reliable enough and manual log analysis is indispensable when automated testing cannot figure out the problems. However, it requires much expert knowledge, costs too much and is time consuming to do manual log analysis for alarge scale system. In this project, we propose to apply machine learning techniques to do automated log analysis as they are effective and efficient to big data problems. Features from the contents of the logs are extracted and clustering algorithms are leveraged to detect abnormal logs. This research investigates multiple kinds offeatures in natural language processing and information retrieval. Several variants of basic clustering and artificial neural network algorithms are developed. Data preprocessing is experimented before feature extraction as well. In order to select a suitable model for our problem, cross validation and F-score are used to evaluate different learning models compared to automated test system verdicts. Finally, the influences of factors that may affect the prediction results such as single or mixed test cases, single or mixed track types and single or mixed configuration types are verified based on different learning models.