Hideaki Hata

Pseudogen: A Tool to Automatically Generate Pseudo-Code from Source Code

2015

Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the …

Sentiment Classification using N-gram IDF and Automated Machine Learning

2019 · arXiv (Cornell University)

We propose a sentiment classification method with a general machine learning framework. For feature representation, n-gram IDF is used to extract software-engineering-related, dataset-specific, positive, neutral, and negative n-gram expressions. For classifiers, an automated machine learning …

How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem

2020 · arXiv (Cornell University)

Although many software development projects have moved their developer discussion forums to generic platforms such as Stack Overflow, Eclipse has been steadfast in hosting their self-supported community forums. While recent studies show forums share similarities …

Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation

2015

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. …