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Assessment of defect prediction models using machine learning techniques for object-oriented systems

机译：使用机器学习技术对面向对象系统的缺陷预测模型进行评估

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摘要

Software development is an essential field today. The advancement in software systems leads to risk of them being exposed to defects. It is important to predict the defects well in advance in order to help the researchers and developers to build cost effective and reliable software. Defect prediction models extract information about the software from its past releases and predict the occurrence of defects in future releases. A number of Machine Learning (ML) algorithms proposed and used in the literature to efficiently develop defect prediction models. What is required is the comparison of these ML techniques to quantify the advantage in performance of using a particular technique over another. This study scrutinizes and compares the performances of 17 ML techniques on the selected datasets to find the ML technique which gives the best performance for determining defect prone classes in an Object-Oriented(OO) software. Also, the superiority of the best ML technique is statistically evaluated. The result of this study demonstrates the predictive capability of ML techniques and advocates the use of Bagging as the best ML technique for defect prediction.
机译：今天，软件开发是必不可少的领域。软件系统的进步导致存在暴露于缺陷中的风险。为了帮助研究人员和开发人员构建具有成本效益的可靠软件，提前做好缺陷预测非常重要。缺陷预测模型从其先前版本中提取有关该软件的信息，并预测将来版本中缺陷的发生。提出并在文献中使用了许多机器学习（ML）算法来有效地开发缺陷预测模型。需要对这些ML技术进行比较，以量化使用特定技术相对于另一种技术的性能优势。这项研究仔细检查并比较了17种机器学习技术在所选数据集上的性能，以发现机器学习技术在确定面向对象（OO）软件中的易错类时表现出最佳性能。同样，最好的ML技术的优越性在统计学上也得到了评估。这项研究的结果证明了机器学习技术的预测能力，并提倡将装袋作为最好的机器学习技术进行缺陷预测。

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