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After decades of research, there is still no comprehensive, validated model of program comprehension. Recently, researchers have been applying psycho-physiological measures to expand our understanding of program comprehension. In this position paper, we argue that measuring program comprehension simultaneously with functional magnetic resonance imaging (fMRI) and eye tracking is promising. However, due to the different nature of both measures in terms of response delay and temporal resolution, we need to develop suitable tools. We describe the challenges of conjoint analysis of fMRI and eye-tracking data, and we also outline possible solution strategies. 

Researchers have been employing psycho-physiological measures to better understand program comprehension, for example simultaneous fMRI and eye tracking to validate top-down comprehension models. In this paper, we argue that there is additional value in eye-tracking data beyond eye gaze: Pupil dilation and blink rates may offer insights into programmers' cognitive load. However, the fMRI environment may influence pupil dilation and blink rates, which would diminish their informative value. We conducted a preliminary analysis of pupil dilation and blink rates of an fMRI experiment with 22 student participants. We conclude from our preliminary analysis that the correction for our fMRI environment is challenging, but possible, such that we can use pupil dilation and blink rates to more reliably observe program comprehension. 

Background: Researchers have recently started to validate decades-old program-comprehension models using functional magnetic resonance imaging (fMRI). While fMRI helps us to understand neural correlates of cognitive processes during program comprehension, its comparatively low temporal resolution (i.e., seconds) cannot capture fast cognitive subprocesses (i.e., milliseconds). Aims: To increase the explanatory power of fMRI measurement of programmers, we are exploring in this methodological paper the feasibility of adding simultaneous eye tracking to fMRI measurement. By developing a method to observe programmers with two complementary measures, we aim at obtaining a more comprehensive understanding of program comprehension. Method: We conducted a controlled fMRI experiment of 22 student participants with simultaneous eye tracking. Results: We have been able to successfully capture fMRI and eye-tracking data, although with limitations regarding partial data loss and spatial imprecision. The biggest issue that we experienced is the partial loss of data: for only 10 participants, we could collect a complete set of high-precision eye-tracking data. Since some participants of fMRI studies show excessive head motion, the proportion of full and high-quality data on fMRI and eye tracking is rather low. Still, the remaining data allowed us to confirm our prior hypothesis of semantic recall during program comprehension, which was not possible with fMRI alone. Conclusions: Simultaneous measurement of program comprehension with fMRI and eye tracking is promising, but with limitations. By adding simultaneous eye tracking to our fMRI study framework, we can conduct more fine-grained fMRI analyses, which in turn helps us to understand programmer behavior better. 

Program comprehension is an important, but hard to measure cognitive process. This makes it difficult to provide suitable programming languages, tools, or coding conventions to support developers in their everyday work. Here, we explore whether functional magnetic resonance imaging (fMRI) is feasible for soundly measuring program comprehension. To this end, we observed 17 participants inside an fMRI scanner while they were comprehending source code. The results show a clear, distinct activation of five brain regions, which are related to working memory, attention, and language processing, which all fit well to our understanding of program comprehension. Furthermore, we found reduced activity in the default mode network, indicating the cognitive effort necessary for program comprehension. We also observed that familiarity with Java as underlying programming language reduced cognitive effort during program comprehension. To gain confidence in the results and the method, we replicated the study with 11 new participants and largely confirmed our findings. Our results encourage us and, hopefully, others to use fMRI to observe programmers and, in the long run, answer questions, such as: How should we train programmers? Can we train someone to become an excellent programmer? How effective are new languages and tools for program comprehension?