Software Reliability Assessment Using Deep Learning Technique

Some of the quality parameters for any successful open-source software(OSS) may be attributed to affordability, availability of source code, redistributability, 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 can modify the software, there is a possible threat that it may be exposed to various security problems, which might degrade the reliability of software. Bug tracking systems are often considered to monitor various software faults, detected mostly in OSS projects. Various authors have made study in this direction by applying different techniques, so that reliability of OSS projects can be improved. In this paper, an efficient approach based on deep learning technique has been proposed to improve the reliability of open-source software. An extensive numerical illustration has also been presented for bug data recorded on bug tracking system. The effectiveness of proposed deep learning-based technique for estimating the level of faults associated with the systems has been verified by comparing it with similar approaches as available in the literature.