Estimation of reading ability of program codes using features of eye movements

Abstract

A prediction model for code reading ability using eye movement features was developed, and analysed in order to evaluate reader's level of mastery and provide appropriate support. Sixty-nine features were extracted from eye movements during the reading of two program codes. These codes consisted of three areas of interest (AOIs) that were modules of code which performed 3 functions. Also, code reader's performance ability was estimated using responses to question surveys and item response theory. The relationships between estimated ability and the metrics of eye movements were generated using a support vector regression technique. Factors of the extracted metrics were analysed. These results confirm the relationship between code comprehension reading behaviour and reading comprehension performance.