Defect Prediction Models aim at identifying error-prone modules of a software system to guide quality assurance activities such as tests or code reviews. Such models have been actively researched for more than a decade, with more than 100 published research papers. However, most of the models proposed so far have assumed that the cost of applying quality assurance activities is the same for each module. In a recent paper, we have shown that this fact can be exploited by a trivial classifier ordering files just by their size: such a classifier performs surprisingly good, at least when effort is ignored during the evaluation. When effort is considered, many classifiers perform not significantly better than a random selection of modules. In this paper, we compare two different strategies to include treatment effort into the prediction process, and evaluate the predictive power of such models. Both models perform significantly better when the evaluation measure takes the effort into account.
