ENHANCING FAULT DETECTION ACCURACY AND RELIABILITY IN SOFTWARE ENGINEERING THROUGH SUPERVISED MACHINE LEARNING ALGORITHM

A crucial component of software engineering is fault detection, which seeks to locate and fix flaws in software systems to guarantee their dependability and resilience. The accuracy and reliability of traditional defect detection techniques are frequently limited, which has led to the investigation of new methods. In contemporary software engineering, ensuring software systems' performance and dependability is crucial. Support vector machines (SVM), decision trees, and random forests are a few examples of supervised machine learning techniques that have become useful tools for automating defect detection processes and improving accuracy and reliability. This paper examines how each algorithm is used in software defect detection, including a thorough analysis of the techniques and guiding concepts of each algorithm. The usefulness of SVMs, Decision Trees, and Random Forests in fault detection tasks is illustrated across a range of software engineering disciplines through a synthesis of case studies and empirical evidence. The study also examines feature selection, data preparation methods, and ensemble approaches as ways to increase fault detection accuracy and reliability. In addition, it addresses research goals and obstacles in the future, highlighting the importance of customized algorithms and looking at applications in cutting-edge fields like continuous integration and deployment. Overall, this work emphasizes how important supervised machine learning methods are to improving software system robustness and defect detection capabilities.