Towards a cognitive model of feature model comprehension

Feature models are pivotal components of Software Product Lines. Therefore, their correct comprehension is crucial for performing adequately all the tasks where they are involved. Despite their importance, to the best of our knowledge, no research has been done on feature model comprehension using eye-trackers. As a first step to address this lack, our work contributes an empirical study of feature model comprehension in simple configuration validation tasks. We propose a first cognitive model for this type of tasks that we analyze by measuring eye gaze fixations on the different visual elements involved in the tasks. Our results identified three main components of the cognitive model and their distribution in terms of the cognitive effort for performing these tasks. We argue that further research on feature model comprehension can inform language design and tool development to provide more suitable language structures, user interfaces and support for this kind of models.