Published Paper Details:

Software defect prediction is a critical task that can make or break a software project. The frustration and disappointment that come with discovering a defect after a release can be overwhelming, especially when it could have been prevented. That's why we, as machine learning researchers, are passionate about finding ways to predict defects and improve software quality. In this study, we dive deep into the Jm1 dataset, carefully selecting features and pre-processing the data to ensure the best possible results. We train and evaluate a variety of machine learning algorithms, all with the goal of finding the best method to predict defects. It's a thrilling journey, filled with both successes and setbacks, but we are determined to find the optimal solution. Ultimately, our hope is that this research will have a real-world impact, helping software teams detect and fix defects before they cause serious problems. The idea of contributing to industrial success is a powerful motivator, and we are excited to see what the future holds for software defect prediction and machine learning.