Machine Learning Approach for Reliability Assessment of Open Source Software

Abstract

Some of the quality parameters for any successful open source software may be attributed to affordability, availability of source code, re-distributability, and modifiability etc. Quality of software can be further improvised subsequently by either users or associated developers by constantly monitoring some of the reliability aspects. Since multiple users are allowed to modify the code there is a potential threat for security, which might degrade the reliability of software. Bug tracking systems are often considered to monitor various software faults, detected mostly in open source software projects. Various authors have made research in this direction by applying different techniques in order to improve the reliability of open source software projects. In this work, an various machine learning models have been implemented to examine the reliability of the software. An extensive numerical illustration has also been presented for bug data recorded on bug tracking system. The effectiveness of machine learning models for estimating the level of faults associated with the systems has been verified by comparing it with similar approaches as available in the literature.