A Bayesian approach for software quality prediction

N. Bouguila, J. Wang, A. Ben Hamza
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引用次数: 19

Abstract

Many statistical algorithms have been proposed for software quality prediction of fault-prone and non fault-prone program modules. The main goal of these algorithms is the improvement of software development processes. In this paper, we introduce a new software prediction algorithm. Our approach is purely Bayesian and is based on finite Dirichlet mixture models. The implementation of the Bayesian approach is done through the use of the Gibbs sampler. Experimental results are presented using simulated data, and a real application for software modules classification is also included.
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软件质量预测的贝叶斯方法
针对易故障和非易故障程序模块的软件质量预测,提出了许多统计算法。这些算法的主要目标是改进软件开发过程。本文介绍了一种新的软件预测算法。我们的方法是纯粹的贝叶斯和基于有限的狄利克雷混合模型。贝叶斯方法的实现是通过使用吉布斯采样器来完成的。给出了模拟数据的实验结果,并给出了软件模块分类的实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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