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

• Autor(a) principal: Medeiros, Júlio
• Data de Publicação: 2021
• Outros Autores: Couceiro, Ricardo, Duarte, Gonçalo, Durães, João, Castelhano, João, Duarte, Catarina, Castelo-Branco, Miguel, Madeira, Henrique, Carvalho, Paulo de, Teixeira, César
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/105462; https://doi.org/10.3390/s21072338
• topic: software engineering; bio-signal processing; electroencephalogram; biofeedback; human error; Cognition; Reproducibility of Results; Software; Brain; Electroencephalography

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.