  Software quality is one of the essential aspects of a software. With
increasing demand, software designs are becoming more complex, increasing the
probability of software defects. Testers improve the quality of software by
fixing defects. Hence the analysis of defects significantly improves software
quality. The complexity of software also results in a higher number of defects,
and thus manual detection can become a very time-consuming process. This gave
researchers incentives to develop techniques for automatic software defects
detection. In this paper, we try to analyze the state of the art machine
learning algorithms' performance for software defect classification. We used
seven datasets from the NASA promise dataset repository for this research work.
The performance of Neural Networks and Gradient Boosting classifier dominated
other algorithms.
