A novel defect prediction method for web pages using k-means++
A novel defect prediction method for web pages using k-means++
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DOI：
10.1016/j.eswa.2015.03.013
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
M. Öztürk;Ü. Çavuşoğlu;A. Zengin
中科院分区：
文献类型：
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作者：
M. Öztürk;Ü. Çavuşoğlu;A. Zengin
随着web软件复杂性的增加，缺陷检测和预防已成为软件行业的关键环节。在过去的几十年里，缺陷预测研究报告了减少软件产品成本的令人鼓舞的结果。尽管这些研究结果很有希望，但很少应用于使用聚类算法的基于web的系统。在缺陷预测中适当地实现聚类可以方便地估计网页源代码中的缺陷。广泛使用的聚类算法之一是k-means，其衍生版本如k-means++在大数据集上表现出良好的性能。本文提出了一种基于k-means++的网页源代码缺陷聚类方法。根据实验结果，几乎一半的缺陷是在网页中间被检测出来的。k-means++在4个数据集的3个标准上明显优于其他4种聚类算法。我们还在四种分类器上测试了我们的方法，结果表明，聚类后，线性判别分析总体上优于其他三种分类器。(C) 2015 Elsevier Ltd.版权所有。
With the increase of the web software complexity, defect detection and prevention have become crucial processes in the software industry. Over the past decades, defect prediction research has reported encouraging results for reducing software product costs. Despite promising results, these researches have hardly been applied to web based systems using clustering algorithms. An appropriate implementation of the clustering in defect prediction may facilitate to estimate defects in a web page source code. One of the widely used clustering algorithms is k-means whose derived versions such as k-means++ show good performance on large-data sets. Here, we present a new defect clustering method using k-means++ for web page source codes. According to the experimental results, almost half of the defects are detected in the middle of web pages. k-means++ is significantly better than the other four clustering algorithms in three criteria on four data set. We also tested our method on four classifiers and the results have shown that after the clustering, Linear Discriminant Analysis is, in general, better than the other three classifiers. (C) 2015 Elsevier Ltd. All rights reserved.