A novel method for early software quality prediction based on support vector machine
A novel method for early software quality prediction based on support vector machine
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DOI：
10.1109/issre.2005.6
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发表时间：
2005-11
期刊：
影响因子：
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通讯作者：
Fei Xing;Ping Guo;Michael R. Lyu
中科院分区：
文献类型：
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作者：
Fei Xing;Ping Guo;Michael R. Lyu
软件开发过程在每个开发阶段对软件质量都有重要影响，因此，如何提高软件质量是每个软件开发阶段的共同目标。因此，软件质量预测的目的是定期评估软件质量水平，并及早发现软件质量问题。在本文中，我们提出了一种新的技术来预测软件质量，采用支持向量机（SVM）的软件模块的复杂性度量的基础上进行分类。由于在软件生命周期的早期阶段，软件复杂性度量的信息非常有限，普通的软件质量模型通常不能做出很好的预测。众所周知，支持向量机在小训练样本条件下，即使在高维空间也具有很好的推广性。因此，我们提出了一个基于SVM的软件分类模型，其特点是适合早期的软件质量预测时，只有少量的样本数据。利用医学影像系统软件度量数据进行的实验结果表明，与常用的软件质量预测模型相比，本文提出的SVM预测模型能够实现更好的软件质量预测
The software development process imposes major impacts on the quality of software at every development stage; therefore, a common goal of each software development phase concerns how to improve software quality. Software quality prediction thus aims to evaluate software quality level periodically and to indicate software quality problems early. In this paper, we propose a novel technique to predict software quality by adopting support vector machine (SVM) in the classification of software modules based on complexity metrics. Because only limited information of software complexity metrics is available in early software life cycle, ordinary software quality models cannot make good predictions generally. It is well known that SVM generalizes well even in high dimensional spaces under small training sample conditions. We consequently propose a SVM-based software classification model, whose characteristic is appropriate for early software quality predictions when only a small number of sample data are available. Experimental results with a medical imaging system software metrics data show that our SVM prediction model achieves better software quality prediction than some commonly used software quality prediction models