Predicting Bugs by Monitoring Developers During Task Execution

Knowing which parts of the source code will be defective can allow practitioners to better allocate testing resources. For this reason, many approaches have been proposed to achieve this goal. Most state-of-the-art predictive models rely on product and process metrics, i.e., they predict the defectiveness of a component by considering what developers did. However, there is still limited evidence of the benefits that can be achieved in this context by monitoring how developers complete a development task. In this paper, we present an empirical study in which we aim at understanding whether measuring human aspects on developers while they write code can help predict the introduction of defects. First, we introduce a new developer-based model which relies on behavioral, psychophysical, and control factors that can be measured during the execution of development tasks. Then, we run a controlled experiment involving 20 software developers to understand if our developer-based model is able to predict the introduction of bugs. Our results show that a developed-based model is able to achieve a similar accuracy compared to a state-of-the-art code-based model, i.e., a model that uses only features measured from the source code. We also observed that by combining the models it is possible to obtain the best results (∼84% accuracy).