软件缺陷预测中类不平衡问题的几种方法综述

Abdullah Dar, Sheikh Umar Farooq
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引用次数: 1

摘要

软件数据集的不平衡性导致预测模型对大多数类(非缺陷类)的观测结果有偏见学习。该预测模型对少数类观测结果的预测结果较差。这样的盗用可能被证明是代价高昂的,特别是在软件开发中,从学习的角度来看,少数派(有缺陷的)是最感兴趣的。针对软件缺陷预测中的类不平衡问题,人们已经采用了多种方法,但没有一种方法占主导地位,因此,针对不平衡数据集开发一种通用的软件缺陷预测模型仍然是一个问题。本文综述了现有的软件缺陷数据集类不平衡问题的处理方法。在这个调查中,大多数相关的软件缺陷预测研究并确定了用于处理软件缺陷数据集不平衡问题的两种主要方法。此外,我们还提供了一些最新文献的研究结果的比较和开展未来研究的指导方针。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey of Different Approaches for the Class Imbalance Problem in Software Defect Prediction
The imbalanced nature of the software datasets leads to the biased learning of prediction model toward the observations of the majority class (non-defective class). The prediction model can produce poor results for the minority class observations. Such misappropriations can prove costly especially in software development where minority class (defective) is the one that has the highest interest from the learning point of view. Various approaches have been used for dealing with class imbalance problem of software defect prediction but no one dominates and hence developing a generalized software defect prediction model for imbalanced datasets remains problematic. This paper surveys existing approaches for handling class imbalance problem of software defect datasets. In this survey, most relevant software defect prediction studies and identified the two main approaches that have been used for handling imbalance issue of software defect datasets. Furthermore, we also provide some comparison of findings in state-of-the-art literature and the guidelines for carrying out future research.
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