Enhancing Software Debugging Processes Through Machine Learning-Driven Automated Bug Detection

In software development, debugging is an essential but laborious process that frequently requires the manual discovery and correction of errors. The project aims to develop an automated bug detection system powered by machine learning to address the challenge of enhancing this process. The process involves evaluating code samples to discover probable problems based on extracted features such Term Frequency-Inverse Document Frequency (TF-IDF) scores, code complexity metrics, and syntactic patterns. It leverages Natural Language Processing (NLP) and a Random Forest Classifier. Using a dataset of labeled code snippets, the suggested approach was assessed and found to significantly enhance mistake detection efficiency as well as accuracy. The outcomes show an approximate 35-40% decrease in the amount of manual debugging work and an improvement in problem identification accuracy. The findings demonstrate how well machine learning approaches can be incorporated into the debugging process, providing a viable way forward for more productive software development procedures.