Paper Info
Title
Software Code Complexity Assessment Using Eeg Features
Abstract
This paper provides a study using Electroencephalography (EEG) to investigate the brain activity during code comprehension tasks. Three different code complexity levels according to five complexity metrics were considered. The use of EEG for this purpose is relevant, since the existing studies were mostly focused on neuroimaging techniques. Using Leave-One-Subject-Out cross-validation procedure for 30 subjects, it was found that the features related with the Gamma activity were the most common in all the folds. Regarding the brain regions, right parietal was the most frequent region contributing with more features. A Linear Discriminant Analysis Classifier for task classification, obtained a F-Measure of 92.71% for Code complexity easy, 52.25% for Code complexity intermediate and 53.13% for Code complexity advanced, revealing an evidence of mental effort saturation with the code complexity degree. This suggests that current code complexity metrics do not capture cognitive load and might not be the best approach to assess bug risk.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856283
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Task analysis,Computer science,Cyclomatic complexity,Feature extraction,Software,Artificial intelligence,Linear discriminant analysis,Classifier (linguistics),Cognitive load,Machine learning,Electroencephalography
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Julio Medeiros141.45
Ricardo Couceiro200.68
João Castelhano300.34
M. Castelo Branco400.34
Gonçalo Duarte551.81
Catarina Duarte651.81
João Durães700.34
Henrique Madeira81307122.00
Paulo de Carvalho931.42
César Alexandre Teixeira10248.11