Automated Parameter Optimization of Classification Techniques for Defect Prediction Models

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Easy over hard: a case study on deep learning (2017, background): “• Learning control seings for learners can take days to weeks to years of CPU time [22, 64, 69].”
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “There are a plethora of classification techniques that have been studied in defect prediction domain [19, 20, 41, 55, 72, 83].”
The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “We first start with a grid search technique that is previously used in our recent work [138].”
Sapienz: multi-objective automated testing for Android applications (2016, background): “This is because there are a number of settings and parameter choices that could affect the results and, as been widely noted in other areas of empirical software engineering [58,60], the choice of parameter tuning options can have a dramatic effect on results.”
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, background): “On the other hand, out-of-sample bootstrap validation yields the best balance between the bias and variance of estimates in the context of our study.”
Heterogeneous Defect Prediction (2015, methodology): “Since we used the default options for machine learners in our experiments, the experimental results could be improved further when we use optimized options [16], [86].”