  This paper launches a new effort at modeling programmer attention by
predicting eye movement scanpaths. Programmer attention refers to what
information people intake when performing programming tasks. Models of
programmer attention refer to machine prediction of what information is
important to people. Models of programmer attention are important because they
help researchers build better interfaces, assistive technologies, and more
human-like AI. For many years, researchers in SE have built these models based
on features such as mouse clicks, key logging, and IDE interactions. Yet the
holy grail in this area is scanpath prediction -- the prediction of the
sequence of eye fixations a person would take over a visual stimulus. A
person's eye movements are considered the most concrete evidence that a person
is taking in a piece of information. Scanpath prediction is a notoriously
difficult problem, but we believe that the emergence of lower-cost,
higher-accuracy eye tracking equipment and better large language models of
source code brings a solution within grasp. We present an eye tracking
experiment with 27 programmers and a prototype scanpath predictor to present
preliminary results and obtain early community feedback.
