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  • https://doi.org/10.1109/melecon53508.2022.9842909Copy DOI Icon

A Quick Review on Machine Learning Techniques in Code Comprehension and Code Review Estimated by Neurophysiological Data

  • Jun 14, 2022
  • Haytham Hijazi +5 more
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Abstract

Machine Learning has been extensively used in studies that utilized neurophysiological data (e.g., fMRI, EEG, ECG) in capturing brain and the Autonomic Nervous System (ANS) activity in various software engineering (SE) tasks such as code comprehension and development. However, there is a lack of research efforts to review machine learning techniques used in code comprehension and code review analysis as primary tasks of SE. This quick review partly covers the research body that used machine learning techniques in code comprehension and code review analysis using neurophysiological signals (i.e., neural and ANS activity) as their primary input feature. The analysis shows that more research is needed to investigate the potential of wearable biofeedback devices (i.e.., smart-watches that generate relevant data for machine learning methods) in capturing the ANS responses of software developers while performing intellectual and abstract tasks.

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