Defect prediction as a multiobjective optimization problem

In this paper, we formalize the defect‐prediction problem as a multiobjective optimization problem. Specifically, we propose an approach, coined as multiobjective defect predictor (MODEP), based on multiobjective forms of machine learning techniques—logistic regression and decision trees specifically—trained using a genetic algorithm. The multiobjective approach allows software engineers to choose predictors achieving a specific compromise between the number of likely defect‐prone classes or the number of defects that the analysis would likely discover (effectiveness), and lines of code to be analysed/tested (which can be considered as a proxy of the cost of code inspection). Results of an empirical evaluation on 10 datasets from the PROMISE repository indicate the quantitative superiority of MODEP with respect to single‐objective predictors, and with respect to trivial baseline ranking classes by size in ascending or descending order. Also, MODEP outperforms an alternative approach for cross‐project prediction, based on local prediction upon clusters of similar classes. Copyright © 2015 John Wiley & Sons, Ltd.