Predicting Code Comprehension: A Novel Approach to Align Human Gaze with Code using Deep Neural Networks

Abstract

The better the code quality and the less complex the code, the easier it is for software developers to comprehend and evolve it. Yet, how do we best detect quality concerns in the code? Existing measures to assess code quality, such as McCabe’s cyclomatic complexity, are decades old and neglect the human aspect. Research has shown that considering how a developer reads and experiences the code can be an indicator of its quality. In our research, we built on these insights and designed, trained, and evaluated the first deep neural network that aligns a developer’s eye gaze with the code tokens the developer looks at to predict code comprehension and perceived difficulty. To train and analyze our approach, we performed an experiment in which 27 participants worked on a range of 16 short code comprehension tasks while we collected fine-grained gaze data using an eye tracker. The results of our evaluation show that our deep neural sequence model that integrates both the human gaze and the stimulus code, can predict (a) code comprehension and (b) the perceived code difficulty significantly better than current state-of-the-art reference methods. We also show that aligning human gaze with code leads to better performance than models that rely solely on either code or human gaze. We discuss potential applications and propose future work to build better human-inclusive code evaluation systems.