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Learning to generate pseudo-code from source code using statistical machine translation

Published: 09 November 2015 Publication History

Abstract

Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages. However, the great majority of source code has no corresponding pseudo-code, because pseudo-code is redundant and laborious to create. If pseudo-code could be generated automatically and instantly from given source code, we could allow for on-demand production of pseudo-code without human effort. In this paper, we propose a method to automatically generate pseudo-code from source code, specifically adopting the statistical machine translation (SMT) framework. SMT, which was originally designed to translate between two natural languages, allows us to automatically learn the relationship between source code/pseudo-code pairs, making it possible to create a pseudo-code generator with less human effort. In experiments, we generated English or Japanese pseudo-code from Python statements using SMT, and find that the generated pseudo-code is largely accurate, and aids code understanding.

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      cover image ACM Conferences
      ASE '15: Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering
      November 2015
      935 pages
      ISBN:9781509000241

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      Published: 09 November 2015

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      1. algorithms
      2. education
      3. statistical approach

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