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Article

Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers’ Cognitive Load?

1
Department of Informatics Engineering, CISUC-Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, P-3030-790 Coimbra, Portugal
2
Coimbra Polytechnic—ISEC, R. Pedro Nunes, P-3030-199 Coimbra, Portugal
3
ICNAS-Institute of Nuclear Sciences Applied to Health, University of Coimbra, P-3000-548 Coimbra, Portugal
4
CIBIT-Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, P-3000-548 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Iosa
Sensors 2021, 21(7), 2338; https://doi.org/10.3390/s21072338
Received: 25 February 2021 / Revised: 20 March 2021 / Accepted: 25 March 2021 / Published: 27 March 2021
(This article belongs to the Section Intelligent Sensors)
An emergent research area in software engineering and software reliability is the use of wearable biosensors to monitor the cognitive state of software developers during software development tasks. The goal is to gather physiologic manifestations that can be linked to error-prone scenarios related to programmers’ cognitive states. In this paper we investigate whether electroencephalography (EEG) can be applied to accurately identify programmers’ cognitive load associated with the comprehension of code with different complexity levels. Therefore, a controlled experiment involving 26 programmers was carried. We found that features related to Theta, Alpha, and Beta brain waves have the highest discriminative power, allowing the identification of code lines and demanding higher mental effort. The EEG results reveal evidence of mental effort saturation as code complexity increases. Conversely, the classic software complexity metrics do not accurately represent the mental effort involved in code comprehension. Finally, EEG is proposed as a reference, in particular, the combination of EEG with eye tracking information allows for an accurate identification of code lines that correspond to peaks of cognitive load, providing a reference to help in the future evaluation of the space and time accuracy of programmers’ cognitive state monitored using wearable devices compatible with software development activities. View Full-Text
Keywords: software engineering; bio-signal processing; electroencephalogram; biofeedback; human error software engineering; bio-signal processing; electroencephalogram; biofeedback; human error
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Figure 1

  • Externally hosted supplementary file 1
    Link: https://ai4eu.dei.uc.pt/base-mental-effort-monitoring-dataset/
    Description: The dataset, experiment protocol and the sample programs (Code 1, Code 2 and Code3) are publicly available in the repository of the H2020 project AI4EI (A European AI On Demand Platform and Ecosystem). Any other data can be requested directly from the corresponding author.
MDPI and ACS Style

Medeiros, J.; Couceiro, R.; Duarte, G.; Durães, J.; Castelhano, J.; Duarte, C.; Castelo-Branco, M.; Madeira, H.; de Carvalho, P.; Teixeira, C. Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers’ Cognitive Load? Sensors 2021, 21, 2338. https://doi.org/10.3390/s21072338

AMA Style

Medeiros J, Couceiro R, Duarte G, Durães J, Castelhano J, Duarte C, Castelo-Branco M, Madeira H, de Carvalho P, Teixeira C. Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers’ Cognitive Load? Sensors. 2021; 21(7):2338. https://doi.org/10.3390/s21072338

Chicago/Turabian Style

Medeiros, Júlio, Ricardo Couceiro, Gonçalo Duarte, João Durães, João Castelhano, Catarina Duarte, Miguel Castelo-Branco, Henrique Madeira, Paulo de Carvalho, and César Teixeira. 2021. "Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers’ Cognitive Load?" Sensors 21, no. 7: 2338. https://doi.org/10.3390/s21072338

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