Complementarity in Software Code Complexity Metrics

This dataset supports the research on Complementarity in Software Code Complexity Metrics. The study integrates EEG (main) and eye-tracking data to analyze developer cognition while performing code comprehension tasks. The dataset contains raw and preprocessed EEG signals, eye-tracking recordings, and code region mappings used in the study. Contents The dataset includes: EEG Data: Preprocessed signals from 35 participants performing 4 randomized code tasks. Eye-Tracking Data: Gaze coordinates and fixation durations mapped to code regions. Code Regions: Defined areas of interest within the experimental code snippets. Survey Responses: Developer background and experience assessment. Data Collection and Preprocessing EEG preprocessing was conducted using EEGLAB in MATLAB, including: High-pass filtering (1Hz), low-pass filtering (40Hz) ICA-based artifact removal Average referencing Eye-tracking data was calibrated against code task images to ensure accuracy. Usage This dataset can be used for: Replicating and validating code understandability experiments Developing new cognitive-behavioral metrics for software engineering Enhancing AI models for predicting developer cognitive load Access & Citation To ensure transparency and reproducibility, this dataset follows Open Science principles and is made publicly available. Please cite this dataset using the DOI provided by Zenodo. You can access all the data used in this research through this Google Drive link.