| dc.description.abstract | Software error detection is a critical aspect of software development. However, due to the lack of time, budget, and workforce, testing applications can be challenging, and in some cases, bug reports may not make it to the final stage. Additionally, a lack of product domain knowledge can lead to misinterpretation of calculations, resulting in errors. To address these challenges, early bug prediction is necessary to develop error-free and efficient applications. In this study, the author proposed a system that uses machine learning to analyze system error logs and detect errors in real time. The proposed system leverages imbalanced data sets from live servers running applications developed using PHP and Codeigniter. The system uses classification algorithms to identify errors and suggests steps to overcome them, thus improving the software’s quality, reliability, and efficiency. Our approach addresses the challenges associated with large and complex software where it can be difficult to identify bugs in the early stages. By analyzing system logs, we demonstrate how machine learning classification algorithms can be used to detect errors and improve system performance. Our work contributes to a better understanding of how machine learning can be used in real-world applications and highlights the practical benefits of early bug prediction in software development. | en_US |
