Department of Informatics Engineering, CISUC-Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal.
ICNAS-Institute of Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal.
PLoS One. 2024 Mar 7;19(3):e0299108. doi: 10.1371/journal.pone.0299108. eCollection 2024.
Cognitive human error and recent cognitive taxonomy on human error causes of software defects support the intuitive idea that, for instance, mental overload, attention slips, and working memory overload are important human causes for software bugs. In this paper, we approach the EEG as a reliable surrogate to MRI-based reference of the programmer's cognitive state to be used in situations where heavy imaging techniques are infeasible. The idea is to use EEG biomarkers to validate other less intrusive physiological measures, that can be easily recorded by wearable devices and useful in the assessment of the developer's cognitive state during software development tasks. Herein, our EEG study, with the support of fMRI, presents an extensive and systematic analysis by inspecting metrics and extracting relevant information about the most robust features, best EEG channels and the best hemodynamic time delay in the context of software development tasks. From the EEG-fMRI similarity analysis performed, we found significant correlations between a subset of EEG features and the Insula region of the brain, which has been reported as a region highly related to high cognitive tasks, such as software development tasks. We concluded that despite a clear inter-subject variability of the best EEG features and hemodynamic time delay used, the most robust and predominant EEG features, across all the subjects, are related to the Hjorth parameter Activity and Total Power features, from the EEG channels F4, FC4 and C4, and considering in most of the cases a hemodynamic time delay of 4 seconds used on the hemodynamic response function. These findings should be taken into account in future EEG-fMRI studies in the context of software debugging.
认知人为错误和最近的软件缺陷人为错误原因认知分类学支持这样一种直观的观点,即例如,心理过载、注意力不集中和工作记忆过载是软件错误的重要人为原因。在本文中,我们将 EEG 作为一种可靠的替代方法,用于基于 MRI 的程序员认知状态的参考,以应用于无法进行繁重成像技术的情况。其想法是使用 EEG 生物标志物来验证其他侵入性较小的生理测量方法,这些方法可以通过可穿戴设备轻松记录,并在软件开发任务中评估开发人员的认知状态时非常有用。在此,我们的 EEG 研究在 fMRI 的支持下,通过检查指标并提取关于软件开发任务中最稳健特征、最佳 EEG 通道和最佳血液动力学时滞的相关信息,进行了广泛而系统的分析。从执行的 EEG-fMRI 相似性分析中,我们发现 EEG 特征的子集与大脑岛叶之间存在显著相关性,据报道,该区域与高认知任务高度相关,例如软件开发任务。我们得出结论,尽管最佳 EEG 特征和血液动力学时滞的个体间变异性很明显,但在所有受试者中,最稳健和主要的 EEG 特征都与 Hjorth 参数活动和总功率特征有关,这些特征来自 EEG 通道 F4、FC4 和 C4,并考虑到在大多数情况下,血液动力学响应函数使用的血液动力学时滞为 4 秒。这些发现应该在未来与软件调试相关的 EEG-fMRI 研究中加以考虑。