Finding bugs in software development is essential to guaranteeing the creation of high-caliber software. Using machine learning approaches has yielded encouraging results in automating defect identification. Since early problem detection reduces the time and cost required for bug patching and maintenance, it is essential for software development. Conventional manual defect detection techniques take a lot of time, are prone to mistakes, and might not be able to handle the complexity and size of contemporary software systems. The software industry now has new tools to improve overall software quality and automate the defect identification process thanks to the development of machine learning techniques. In order to identify errors in software development, this study compares supervised versus unsupervised machine learning techniques. We compare the effectiveness of both methods with real-world software defect datasets and talk about their advantages, disadvantages, and applications. The study's findings provide insight into how effective each method is at identifying mistakes while software is being produced.

___________________________________________________________________________________