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Related Experiment Video

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

Júlio Medeiros1, Ricardo Couceiro1, Gonçalo Duarte1

  • 1Department of Informatics Engineering, CISUC-Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, P-3030-790 Coimbra, Portugal.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

Electroencephalography (EEG) can identify cognitive load in software developers by analyzing brain waves. This method accurately measures mental effort during code comprehension, outperforming traditional complexity metrics.

Keywords:
bio-signal processingbiofeedbackelectroencephalogramhuman errorsoftware engineering

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Area of Science:

  • Software Engineering
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Wearable biosensors are emerging for monitoring software developers' cognitive states.
  • Physiologic data can be linked to error-prone scenarios in software development.
  • Understanding cognitive load is crucial for improving software reliability.

Purpose of the Study:

  • To investigate Electroencephalography (EEG) for accurately identifying cognitive load in programmers.
  • To correlate brain wave activity with code comprehension difficulty.
  • To compare EEG-based mental effort with traditional software complexity metrics.

Main Methods:

  • A controlled experiment was conducted with 26 programmers.
  • Electroencephalography (EEG) was used to record brain wave activity.
  • Code complexity levels were varied to assess cognitive load.

Main Results:

  • Theta, Alpha, and Beta brain waves showed high discriminative power for identifying high-effort code.
  • EEG revealed mental effort saturation as code complexity increased.
  • Traditional software complexity metrics did not accurately reflect mental effort.

Conclusions:

  • EEG effectively measures programmers' cognitive load during code comprehension.
  • EEG offers a more accurate assessment of mental effort than conventional metrics.
  • Combining EEG with eye tracking can precisely identify cognitive load peaks for evaluating wearable monitoring systems.