A Bayesian Approach for Software Quality Prediction

被引：0

作者：

Bouguila, Nizar ^{[1
]}

Wang, Jian Han ^{[1
]}

Ben Hamza, A. ^{[1
]}

机构：

[1] Concordia Univ, Inst Informat Syst Engn CIISE, Montreal, PQ H3G 1T7, Canada

来源：

2008 4TH INTERNATIONAL IEEE CONFERENCE INTELLIGENT SYSTEMS, VOLS 1 AND 2 | 2008年

关键词：

Software modules; finite mixture models; Dirichlet; prediction; Bayesian inference;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：512 / 517

页数：6

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