Towards Automatic Capturing of Traceability Links by Combining Eye Tracking and Interaction Data

Abstract

Despite its numerous scientifically cited benefits, traceability is still rarely established in industrial settings. Years of research in the area have brought several different approaches to create traceability links including Information Retrieval (IR) and Machine Learning approaches. However, their accuracy and overall traceability support is not sufficient yet to be properly applied in practice. In my research, I want to investigate the usage of eye tracking and interaction data in the field of traceability. By tracking how software engineers interact with documents, where they focus on and recording gaze links between those documents, an algorithm is designed to obtain trace links between artifacts from these data. Eye tracking and interaction data have the advantage that they can be recorded in an automatic, non-intrusive way without requiring manual effort. They give detailed insight about where people focus on when working on tasks. However, software support of eye tracking is still limited, especially in the context of dynamic content such as switching between, scrolling or editing documents. Therefore, one essential step of my research is to provide software support for recording eye tracking data in dynamic document environments. By combining eye tracking data with additionally recorded metadata such as interactions, this eye tracking framework shall enable the automatic capturing of gaze links and gaze durations during software engineering tasks. The approach of using eye tracking in the context of traceability will be evaluated in several usage scenarios such as requirements coverage assessment.