Expert programmers' eye-movements during source code reading are valuable sources that are considered to be associated with their domain expertise. We advocate a vision of new intelligent systems incorporating expertise of experts for software development tasks, such as issue localization, comment generation, and code generation. We present a conceptual framework of neural autonomous agents based on imitation learning (IL), which enables agents to mimic the visual attention of an expert via his/her eye movement. In this framework, an autonomous agent is constructed as a context-based attention model that consists of encoder/decoder network and trained with state-action sequences generated by an experts' demonstration. Challenges to implement an IL-based autonomous agent specialized for software development task are discussed in this paper.
@article{arxiv.1903.06320,
title = {Toward Imitating Visual Attention of Experts in Software Development Tasks},
author = {Yoshiharu Ikutani and Nishanth Koganti and Hideaki Hata and Takatomi Kubo and Kenichi Matsumoto},
journal= {arXiv preprint arXiv:1903.06320},
year = {2019}
}