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 However, comments are often missing, generally inaccurate, or out of date.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 NovDisplayed time zone: Guadalajara, Mexico City, Monterrey change
Sun 4 Nov
Displayed time zone: Guadalajara, Mexico City, Monterrey change
10:30 - 12:00 | |||
10:30 15mTalk | Total Recall, Language Processing, and Software Engineering NL4SE | ||
10:45 15mTalk | 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 15mTalk | 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 15mTalk | TestNMT: Function-to-Test Neural Machine Translation NL4SE | ||
11:30 15mTalk | 3CAP: Categorizing the Cognitive Capabilities of Alzheimer’s Patients in a Smart Home Environment NL4SE | ||
11:45 15mTalk | Generating Comments from Source Code with CCGs NL4SE |