Sun 4 Nov 2018 11:15 - 11:30 at Rock Lake - NL4SE Workshop II

Test generation can have a large impact on the software engineering process by decreasing the amount of time and effort required to maintain a high level of test coverage. This increases the quality of the resultant software while decreasing the associated effort. In this paper, we present TestNMT, an experimental approach to test generation using neural machine translation. TestNMT aims to learn to translate from functions to tests, allowing a developer to generate an approximate test for a given function, which can then be adapted to produce the final desired test.

We also present a preliminary quantitative and qualitative evaluation of TestNMT in both cross-project and within-project scenarios. This evaluation shows that TestNMT is potentially useful in the within-project scenario, where it achieves a maximum BLEU score of 21.2, a maximum ROUGE-L score of 38.67, and is shown to be capable of generating approximate tests that are easy to adapt to working tests.

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