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A comparison and integration of capture-recapture models and the detection profile method

机译:捕获-捕获模型与检测配置文件方法的比较和集成

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In order to control inspections, the number of remaining defects in software artifacts after their inspection should be estimated. This would allow, for example, deciding whether a reinspection of supposedly faulty artifacts is necessary. Several studies in software engineering have considered capture-recapture models for performing such estimations. These models were initially developed for estimating animal abundance in wildlife research. In addition to these models, researchers in software engineering have recently proposed an alternative approach, namely the detection profile method (DPM), that makes less restrictive assumptions than some capture-recapture models and that show promise in terms of estimation accuracy. The authors investigate how to select between these two approaches for defect content estimation. As a result of this investigation they present a selection procedure taking into account the strength and weaknesses of the two methods. A weakness known for capture-recapture models is that they tend to provide extreme under/over estimation. The existence of such extreme outliers can discourage their use because their consequences in terms of wasted effort or defect slippage can be substantial, and therefore it is not clear,whether a particular estimate can be trusted. The evaluation of the selection procedure with actual inspection data indicates that this selection procedure provides the same accuracy as capture-recapture models alone and DPM alone, and most importantly does not exhibit extreme over/under estimation.
机译:为了控制检查,应该估计检查后软件工件中剩余的缺陷数量。例如,这将允许确定是否有必要对所谓的有缺陷的伪影进行重新检查。软件工程中的一些研究已经考虑了用于执行这种估计的捕获-捕获模型。最初开发这些模型是为了估计野生动植物研究中的动物数量。除了这些模型之外,软件工程领域的研究人员最近还提出了另一种方法,即检测配置文件方法(DPM),该方法比某些捕获-捕获模型具有更少的限制假设,并且在估计精度方面显示出希望。作者研究了如何在这两种方法中选择缺陷含量的方法。作为这项调查的结果,他们提出了一种选择程序,其中考虑了这两种方法的优缺点。捕获-捕获模型的一个已知缺点是,它们倾向于提供极端的过低/过高估计。这些极端离群值的存在可能会阻止其使用,因为它们在浪费精力或缺陷打滑方面的后果可能是巨大的,因此尚不清楚是否可以信任特定的估计值。使用实际检查数据对选择过程进行评估表明,该选择过程提供的准确性与单独的捕获模型和单独的DPM相同,并且最重要的是不会表现出过高或过低的估计值。

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