Comparing Classifiers Correctly

寻找一种有效的方法来预测软件系统中容易出现缺陷的部分一直是软件工程研究人员面临的一个长期挑战。这将有助于稀缺的软件测试资源的分配，并指导决策有关何时发布软件系统。因此，缺陷预测已经吸引了大量的研究关注，发表了数百项研究，这并不奇怪。为了将这些研究联合收割机结合起来，研究人员需要一种比较预测性能的合理方法，而目前这是令人惊讶的问题。目前，流行的方法是有偏见的（因此不可靠），也很难解释。通过解决这一需求，这项拟议的研究将有助于释放大量研究的潜在价值，主要是使用机器学习方法，对缺陷预测。这将产生更值得信赖的，因此可操作的结果，这意味着从业者可以更好地指导如何发现并修复缺陷。这笔旅行补助金将使Shepperd教授和MacDonell教授之间的密切合作能够深入研究这个问题。

Finding an effective way to predict defect-prone parts of software systems has been a longstanding challenge for software engineering researchers. This would assist both the allocation of scarce software testing resources and also guide decision-making concerning when to release software systems. Thus it's no surprise that defect prediction has attracted a great deal of research attention with many hundreds of published studies.In order to combine these studies, researchers need a sound means of comparing predictive performance and currently this is surprisingly problematic. Currently, the popular methods are biased (and therefore unreliable) and also difficult to interpret.By addressing this need, this proposed research will help unlock the potential value of the large number of studies, primarily using machine learning methods, on defect prediction. This will yield more trustworthy and therefore actionable results meaning that practitioners can be better guided as how to find and therefore fix defects.This travel grant will enable close collaboration between Profs Shepperd and MacDonell to work intensively on this problem.