论文标题
关于软件缺陷预测方法的审查
A Review On Software Defects Prediction Methods
论文作者
论文摘要
软件质量是软件的重要方面之一。随着需求的增加，软件设计变得越来越复杂，增加了软件缺陷的可能性。测试人员通过解决缺陷来提高软件的质量。因此，对缺陷的分析可显着提高软件质量。软件的复杂性还会导致更多的缺陷，因此手动检测可能会成为非常耗时的过程。这使研究人员激励了开发自动软件缺陷检测技术的技术。在本文中，我们试图分析用于软件缺陷分类的最先进的机器学习算法的性能。我们在NASA Promise数据集存储库中使用了七个数据集进行此研究工作。神经网络的性能和梯度增强分类器主导了其他算法。
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.
