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To investigate the role of heuristics in the domain of software engineering, an eye tracking study was conducted in which experts and novices were compared. The study focused on one of the most challenging parts in this domain: the generation of an object model for a software product based on a requirements specification. During their training, software engineers are taught different techniques to solve this task. One of these techniques is the noun/verb analysis.
However, it is still unclear to what extent novice and expert programmers are making use of it. Ideally, the noun/verb analysis works as a heuristic and helps programmers to make fast and accurate decisions. Participants in the study were 40 software programmers at four levels of expertise (novices, intermediates, experienced rogrammers, experts). They were presented with ten decision tasks. In each task, participants read a requirement specification and then had to choose one out of three presented class diagrams that they considered the best solution. During the task, their eye movements were recorded. Results show that all participants used the noun/verb analysis as a heuristic. Programmers with higher levels of expertise, however, outperformed programmers with lower levels of expertise. Interestingly, the more experienced programmers were not following the noun/verb analysis in a blindfolded way. They realised that the noun/verb analysis would produce diagrams, but a skilled software architect would not model them in this way. Instead they created their models in a way that they perceived as more logical and realistic
Eye tracking is a powerful technique that helps reveal how people process visual information. This paper discusses a novel metric for indicating expertise in visual information processing. Named the Gaze Relational Index (GRI), this metric is defined as the ratio of mean fixation duration to fixation count. Data from two eye-tracking studies of professional vision and visual expertise in using 3D dynamic medical visualizations are presented as cases to illustrate the suitability and additional benefits of the GRI. Calculated values of the GRI were higher for novices than for experts, and higher in non-representative, semi-familiar / unfamiliar task conditions than in domain-representative familiar tasks. These differences in GRI suggest that, compared to novices, experts engaged in more knowledge-driven, top-down processing that was characterized by quick, exploratory visual search. We discuss future research aiming to replicate the GRI in professional domains with complex visual stimuli and to identify the moderating role of cognitive ability on GRI estimates.