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Code Cleaning for Software Defect Prediction: A Cautionary Tale

机译:用于软件缺陷预测的代码清洗:警告性的故事

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In this paper, we describe our experience of developing a new technique to improve defect prediction (code cleaning) which performed very encouragingly on the first two systems on which we evaluated it (both systems had their origins in one company). Code cleaning also worked well on an additional open source system (Eclipse). But our code cleaning technique then performed disappointingly on all 69 subsequent open source systems on which we evaluated it. Without our round two evaluations on these 69 open source systems we would have published misleading prediction results. We discuss the need for performance evaluations to be performed on carefully selected samples of systems if reliable conclusions are to be drawn.
机译:在本文中,我们描述了我们开发改进缺陷预测(代码清除)新技术的经验,该技术在我们对其进行评估的前两个系统(两个系统均起源于一家公司)中表现非常令人鼓舞。代码清理在附加的开源系统(Eclipse)上也能很好地工作。但是我们的代码清理技术随后在评估它的所有69个后续开源系统上都表现令人失望。如果没有对这69个开源系统进行第二轮评估,我们将发布误导性的预测结果。如果要得出可靠的结论,我们将讨论对仔细选择的系统样本进行性能评估的必要性。

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