Applying Machine Learning to Gaze Data in Software Development: a Mapping Study

Eye tracking has been used as part of software engineering and computer science research for a long time, and during this time new techniques for machine learning (ML) have emerged. Some of those techniques are applicable to the analysis of eye-tracking data, and to some extent have been applied. However, there is no structured summary available on which ML techniques are used for analysis in different types of eye-tracking research studies. In this paper, our objective is to summarize the research literature with respect to the application of ML techniques to gaze data in the field of software engineering. To this end, we have conducted a systematic mapping study, where research articles are identified through a search in academic databases and analyzed qualitatively. After identifying 10 relevant articles, we found that the most common software development activity studied so far with eye-tracking and ML is program comprehension, and Support Vector Machines and Decision Trees are the most commonly used ML techniques. We further report on limitations and challenges reported in the literature and opportunities for future work.