In this paper, we introduce a novel approach to predictive modeling for software engineering, named Learning From Mistakes (LFM). The core idea underlying our proposal is to automatically learn from past estimation errors made by human experts, in order to predict the characteristics of their future misestimates, therefore resulting in improved future estimates. We show the feasibility of LFM by investigating whether it is possible to predict the type, severity and magnitude of errors made by human experts when estimating the development effort of software projects, and whether it is possible to use these predictions to enhance future estimations. To this end we conduct a thorough empirical study investigating 402 maintenance and new development industrial software projects. The results of our study reveal that the type, severity and magnitude of errors are all, indeed, predictable. Moreover, we find that by exploiting these predictions, we can obtain significantly better estimates than those provided by random guessing, human experts and traditional machine learners in 31 out of the 36 cases considered (86%), with large and very large effect sizes in the majority of these cases (81%). This empirical evidence opens the door to the development of techniques that use the power of machine learning, coupled with the observation that human errors are predictable, to support engineers in estimation tasks rather than replacing them with machine-provided estimates.

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Software defect prediction research has adopted various evaluation measures to assess the performance of prediction models. In this paper, we further stress on the importance of the choice of appropriate measures in order to correctly assess strengths and weaknesses of a given defect prediction model, especially given that most of the defect prediction tasks suffer from data imbalance.Investigating 111 previous studies published between 2010 and 2020, we found out that over a half either use only one evaluation measure, which alone cannot express all the characteristics of model performance in presence of imbalanced data, or a set of binary measures which are prone to be biased when used to assess models especially when trained with imbalanced data.We also unveil the magnitude of the impact of assessing popular defect prediction models with several evaluation measures based, for the first time, on both statistical significance test and effect size analyses. Our results reveal that the evaluation measures produce a different ranking of the classification models in 82% and 85% of the cases studied according to the Wilcoxon statistical significance test and Â12 effect size, respectively. Further, we observe a very high rank disruption (between 64% to 92% on average) for each of the measures investigated. This signifies that, in the majority of the cases, a prediction technique that would be believed to be better than others when using a given evaluation measure becomes worse when using a different one.We conclude by providing some recommendations for the selection of appropriate evaluation measures based on factors which are specific to the problem at hand such as the class distribution of the training data, the way in which the model has been built and will be used. Moreover, we recommend to include in the set of evaluation measures, at least one able to capture the full picture of the confusion matrix, such as MCC. This will enable researchers to assess whether proposals made in previous work can be applied for purposes different than the ones they were originally intended for. Besides, we recommend to report, whenever possible, the raw confusion matrix to allow other researchers to compute any measure of interest thereby making it feasible to draw meaningful observations across different studies.

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