ML-Based Software Defect Prediction in Embedded Software for Telecommunication Systems (Focusing on the Case of SAMSUNG ELECTRONICS)

Abstract

Software stands out as one of the most rapidly evolving technologies in the present era, characterized by its swift expansion in both scale and complexity, which leads to challenges in quality assurance. Software defect prediction (SDP) has emerged as a methodology crafted to anticipate undiscovered defects, leveraging known defect data from existing codes. This methodology serves to facilitate software quality management, thereby ensuring overall product quality. The methodologies of machine learning (ML) and one of its branches, deep learning (DL), exhibit superior accuracy and adaptability compared to traditional statistical approaches, catalyzing active research in this domain. However, it makes it hard to generalize, not only because of the disparity between open-source projects and commercial projects but also due to the differences in each industrial sector. Consequently, further research utilizing datasets sourced from diverse real-world sectors has become imperative to bolster the applicability of these findings. For this study, we utilized embedded software for use with the telecommunication systems of Samsung Electronics, supplemented by the introduction of nine novel features to train the model, and a subsequent analysis of the results ensued. The experimental outcomes revealed that the F-measurement metric has been enhanced from 0.58 to 0.63 upon integration of the new features, thereby signifying a performance augmentation of 8.62%. This case study is anticipated to contribute to bolstering the application of SDP methodologies within analogous industrial sectors.