Replication of defect prediction studies: problems, pitfalls and recommendations
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Searching for rules to detect defective modules: A subgroup discovery approach
Information Sciences: an International Journal
Evaluating defect prediction approaches: a benchmark and an extensive comparison
Empirical Software Engineering
Bug prediction based on fine-grained module histories
Proceedings of the 34th International Conference on Software Engineering
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
A cost-effectiveness criterion for applying software defect prediction models
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
A study of subgroup discovery approaches for defect prediction
Information and Software Technology
Comparative study on effectiveness of standard bug prediction approaches
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
DConfusion: a technique to allow cross study performance evaluation of fault prediction studies
Automated Software Engineering
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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.