基于半监督学习的多源软件缺陷预测模型

Multi-Source Software Defect Prediction Model Based on Semi-Supervised Learning

本文研究在不同的软件项目之间,建立通用软件缺陷预测模型的方法。通过分析多源软件的项目信息,本文设计了25维软件特征用于机器学习。为了克服不同软件项目之间的代码区别,实现模型的通用性,使用基于半监督学习Self-training自训练算法生成分类器。最后利用本文设计的25维数据特征建立训练数据,通过Self-training算法生成通用的多源软件缺陷预测模型。

This paper studies the method of establishing a general software defect prediction model between different software projects. By analyzing the project information of multi-source software, this paper designs 25-dimensional software features for machine learning. In order to overcome the differences between different software projects and achieve the generality of the model, a Self-training algorithm based on semi-supervised learning is used to generate a classifier. Finally, the 25-dimensional data features are used to build training data, and a general multi-source software defect prediction model is generated by the Self-training algorithm.