Software code complexity assessment using EEG features

Medeiros, Julio; Couceiro, Ricardo; Castelhano, João; Duarte, Goncalo; Duarte, Catarina; Duraes, Joao; Madeira, Henrique; Carvalho, Paulo; Teixeira, César

doi:10.1109/embc.2019.8856283

Cited by 9 publications

(15 citation statements)

References 13 publications

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“…In Duraisingam et al (2017) and Ishida and Uwano (2019) , EEG features taken from several brain regions are applied to perform a thorough analysis of task difficulty level for program comprehension. Furthermore, there is clear evidence that the complexity of the code induces mental effort that can be assessed using EEG ( Medeiros et al, 2019 ). In Lee et al (2016) , Crk and Kluthe (2014) , and Crk et al (2016) , EEG-based feature analysis was applied to classify expertise level of software programmers.…”

Section: Related Workmentioning

confidence: 99%

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On the accuracy of code complexity metrics: A neuroscience-based guideline for improvement

et al. 2023

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Complexity is the key element of software quality. This article investigates the problem of measuring code complexity and discusses the results of a controlled experiment to compare different views and methods to measure code complexity. Participants (27 programmers) were asked to read and (try to) understand a set of programs, while the complexity of such programs is assessed through different methods and perspectives: (a) classic code complexity metrics such as McCabe and Halstead metrics, (b) cognitive complexity metrics based on scored code constructs, (c) cognitive complexity metrics from state-of-the-art tools such as SonarQube, (d) human-centered metrics relying on the direct assessment of programmers’ behavioral features (e.g., reading time, and revisits) using eye tracking, and (e) cognitive load/mental effort assessed using electroencephalography (EEG). The human-centered perspective was complemented by the subjective evaluation of participants on the mental effort required to understand the programs using the NASA Task Load Index (TLX). Additionally, the evaluation of the code complexity is measured at both the program level and, whenever possible, at the very low level of code constructs/code regions, to identify the actual code elements and the code context that may trigger a complexity surge in the programmers’ perception of code comprehension difficulty. The programmers’ cognitive load measured using EEG was used as a reference to evaluate how the different metrics can express the (human) difficulty in comprehending the code. Extensive experimental results show that popular metrics such as V(g) and the complexity metric from SonarSource tools deviate considerably from the programmers’ perception of code complexity and often do not show the expected monotonic behavior. The article summarizes the findings in a set of guidelines to improve existing code complexity metrics, particularly state-of-the-art metrics such as cognitive complexity from SonarSource tools.

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Section: Related Workmentioning

confidence: 99%

“…Direct assessment of programmers’ cognitive load while comprehending the code using EEG, which has been proposed as a reference to measure cognitive complexity in code comprehension scenarios ( Medeiros et al, 2019 , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

On the accuracy of code complexity metrics: A neuroscience-based guideline for improvement

et al. 2023

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“…They may overlap and encompass events within their time frame, such as stimulus presentations or participants' responses. They can be created by combining channels from various brain regions [38]. Segmentation is commonly used as it makes the signal more amenable to analysis.…”

Section: ) Semi-automated Basic Preprocessingmentioning

confidence: 99%

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

Kyriaki,

Koukopoulos,

Fidas

2024

IEEE Access

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Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment.Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users' cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments.

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“…Few studies look at interhemispheric differences (power disparities between the right and left hemispheres), while few look at non-directed functional connectivity measurements (i.e., statistical associations between spatially distinct brain areas). One research employs frequency band power crosscorrelations between electrodes, while another uses phaselocking values, which measure phase synchronization between pairs of electrodes ( [109]; [110]; [111]). The collected data, the BOLD contrast, is mentioned unambiguously in all fMRI experiments.…”

Section: A Types Of Data For Detecting Human Status (Rq1)mentioning

confidence: 99%

A Systematic Literature Review on Machine Learning Algorithms for Human Status Detection

2022

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Human status detection (HSD) is important to understand the status of users when interacting with various systems under different conditions. Recently, although various machine learning algorithms have been applied to analyze and detect human status, there are no guidelines to utilize machine learning algorithms to analyze physical, cognitive, and emotional aspects of human status. Therefore, this study aimed to investigate measures, tools, and machine learning algorithms for HSD by applying a systematic literature review method. We followed the preferred reporting items for systematic reviews and metaanalysis (PRISMA) model to answer three research questions related to the research objective. A total of 76 articles were identified using two hundred keyword combinations addressing topics under HSD in the fields of human factors and human-computer interaction (HCI). The results showed that research on HSD becomes important in industrial systems, focusing on how intelligent systems based on machine learning (ML) differ from earlier generations of automated systems, and what these differences necessarily imply for HCI to design and evaluation. The tools used to collect data for HSD on different parameters are broadly discussed. Recent HSD studies seem to focus on cognitive load and emotion, whereas prior studies have focused on the detection of physical effort. This research assists domain researchers in identifying HSD approaches using different ML algorithms that are suitable for use in their research.

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Software code complexity assessment using EEG features

Cited by 9 publications

References 13 publications

On the accuracy of code complexity metrics: A neuroscience-based guideline for improvement

On the accuracy of code complexity metrics: A neuroscience-based guideline for improvement

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

A Systematic Literature Review on Machine Learning Algorithms for Human Status Detection

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