Software Defect Prediction and Multi-Classification: A Review of Domestic and International Research

Software defect prediction and multi-classification are research topics that have attracted considerable attention in both domestic and international academic circles. In recent years, researchers have proposed various methods and techniques for software defect prediction, including machine learning, data mining, and statistical analysis. These approaches have been applied to different software projects and have achieved promising results in identifying potential defects and improving software quality.

Similarly, multi-classification has also been a popular area of research in recent years, with the goal of classifying data into multiple categories. This has been applied to various fields, including natural language processing, image recognition, and medical diagnosis. Researchers have proposed different algorithms and models for multi-classification, such as decision trees, neural networks, and support vector machines.

In terms of the domestic research status, Chinese scholars have also made significant contributions to these areas, proposing various methods and techniques for software defect prediction and multi-classification. They have also applied these approaches to different fields, including software engineering, finance, and healthcare.

Overall, software defect prediction and multi-classification are important research topics that have attracted a lot of attention from both domestic and international academic circles. With the development of new techniques and methods, it is expected that these areas will continue to advance and contribute to the improvement of software quality and data classification.