Paper Reading AI Learner

Human Languages in Source Code: Auto-Translation for Localized Instruction

2019-09-10 15:06:58
Chris Piech, Sami Abu-El-Haija

Abstract

Computer science education has promised open access around the world, but access is largely determined by what human language you speak. As younger students learn computer science it is less appropriate to assume that they should learn English beforehand. To that end we present CodeInternational, the first tool to translate code between human languages. To develop a theory of non-English code, and inform our translation decisions, we conduct a study of public code repositories on GitHub. The study is to the best of our knowledge the first on human-language in code and covers 2.9 million Java repositories. To demonstrate CodeInternational's educational utility, we build an interactive version of the popular English-language Karel reader and translate it into 100 spoken languages. Our translations have already been used in classrooms around the world, and represent a first step in an important open CS-education problem.

Abstract (translated)

URL

https://arxiv.org/abs/1909.04556

PDF

https://arxiv.org/pdf/1909.04556