Code comprehension has been recently investigated from physiological and
cognitive perspectives through the use of medical imaging. Floyd et al (i.e.,
the original study) used fMRI to classify the type of comprehension tasks
performed by developers and relate such results to their expertise. We
replicate the original study using lightweight biometrics sensors which
participants (28 undergrads in computer science) wore when performing
comprehension tasks on source code and natural language prose. We developed
machine learning models to automatically identify what kind of tasks developers
are working on leveraging their brain-, heart-, and skin-related signals. The
best improvement over the original study performance is achieved using solely
the heart signal obtained through a single device (BAC 87% vs. 79.1%).
Differently from the original study, we were not able to observe a correlation
between the participants' expertise and the classifier performance (tau = 0.16,
p = 0.31). Our findings show that lightweight biometric sensors can be used to
accurately recognize comprehension tasks opening interesting scenarios for
research and practice.Comment: Author version submitted to ICPC2019 (Replication track
