Memory Analysis and Log Enhancements

Abstract

Numerous issues exist in the testing of a large scale framework. The mechanized testing results are not solid enough and manual log examination is vital when robotized testing can’t sort out the issues. Be that as it may, it requires a lot of expertise and is excessively expensive and is tedious to do manual log investigation for a large scale framework. In this undertaking, we propose to upgrade the logging utility, both for capturing and analyzing the logs from dev boxes and boxes available remotely (boxes in the field) and apply machine learning techniques to do automated log analysis as they are efficient to big data problems. Highlights from the substance of the logs are extricated and grouping calculations are utilized to recognize strange logs. This project aims to investigate various features in natural language processing and information retrieval. Some variants of basic clustering and artificial neural network algorithms are developed.