Sun 4 Nov 2018 11:45 - 12:00 at Rock Lake - NL4SE Workshop II

Good comments help developers understand software faster and provide better maintenance. However, comments are often missing, generally inaccurate, or out of date. Many of these problems can be avoided by automatic comment generation. This paper presents a method to generate informative comments directly from the source code using general-purpose techniques from natural language processing. We generate comments using an existing natural language model that couples words with their individual logical meaning and grammar rules, allowing comment generation to proceed by search from declarative descriptions of program text. We evaluate our algorithm on several classic algorithms implemented in Python.

Sun 4 Nov

Displayed time zone: Guadalajara, Mexico City, Monterrey change

10:30 - 12:00
NL4SE Workshop IINL4SE at Rock Lake
10:30
15m
Talk
Total Recall, Language Processing, and Software Engineering
NL4SE
10:45
15m
Talk
Is “Naturalness” a Result of Deliberate Choice?
NL4SE
Kevin Lee University of California at Davis, USA, Casey Casalnuovo University of California at Davis, USA
11:00
15m
Talk
A Fine-Grained Approach for Automated Conversion of JUnit Assertions to English
NL4SE
Danielle Gonzalez Rochester Institute of Technology, USA, Suzanne Prentice University of South Carolina, USA, Mehdi Mirakhorli Rochester Institute of Technology
11:15
15m
Talk
TestNMT: Function-to-Test Neural Machine Translation
NL4SE
Robert White University College London, UK, Jens Krinke University College London
11:30
15m
Talk
3CAP: Categorizing the Cognitive Capabilities of Alzheimer’s Patients in a Smart Home Environment
NL4SE
Kate M. Bowers , Reihaneh H. Hariri Oakland University, USA, Katey A. Price Albion College, USA
11:45
15m
Talk
Generating Comments from Source Code with CCGs
NL4SE
Sergey Matskevich Drexel University, USA, Colin Gordon Drexel University