Issues like bugs in software engineering are a primary cause of systems failure, regarding information security system, it is a threat. Traditional approach of bug detection system is ineffective both in its efficiency and speed. The automated bug detection system through machine learning (ML) is the scope of this study using supervised learning approaches. The study covers main parts like Data collection, preprocessing, model training, and model evaluation which are the most important problems of this study. The ML models help software developers to accomplish their work in an easier and more reliable way with high code quality because the models are able to detect issues in a smart, effective, and scalable way with less effort to the developer

Bugs in Software, Software Defects, Machine Learning, Deep Learning, Software Code Analysis, Software Assurance: Reliability

Integrating machine learning (ML) algorithms to bug tracking and detection improves the quality and dependability of software. Includes manual analysis, code inspection, and static processes are common approaches to bug detection, but are time consuming and prone to human error. The use of machine learning models increases bug detection accuracy and decrease false positive rates through patterns identification, anomalies recognition, and advanced deep learning capture of complex software bugs. Self-Supervised models, as well as advanced deep learning and optimization tools, show greater potential in more accurately detecting software bugs. These models help predict software threats through gauging class instances of bugs in historical data along with offering plausible solutions. This approach gives rise to software feature search automation post bug fixing, significantly cutting down time spent on debugging. Additionally, machine learning benefits from feature selection and dimensionality reduction during the automation development phase of systems to improve model performance. There are many untapped areas with the model training needing manual working intervention such as the so called the quality unbalance ratio and constant model tuning to fit the new dynamics of software frameworks

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