Code comprehension has been recently investigated from physiological and cognitive perspectives using medical imaging devices. Floyd et al. (i.e., the original study) used fMRI to classify the type of comprehension tasks performed by developers and relate their results to their expertise. We replicate the original study using lightweight biometrics sensors. Our study participants—28 undergrads in computer science—performed 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 did not observe a correlation between the participants’ expertise and the classifier performance (τ = 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.

A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors

Daniela Girardi;Nicole Novielli;Filippo Lanubile
2019

Abstract

Code comprehension has been recently investigated from physiological and cognitive perspectives using medical imaging devices. Floyd et al. (i.e., the original study) used fMRI to classify the type of comprehension tasks performed by developers and relate their results to their expertise. We replicate the original study using lightweight biometrics sensors. Our study participants—28 undergrads in computer science—performed 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 did not observe a correlation between the participants’ expertise and the classifier performance (τ = 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.
978-1-7281-1519-1
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/230190
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