Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2017
Abstract
Program assertions are useful for many program analysis tasks. They are however often missing in practice. In this work, we develop a novel approach for generating likely assertions automatically based on active learning. Our target is complex Java programs which cannot be symbolically executed (yet). Our key idea is to generate candidate assertions based on test cases and then apply active learning techniques to iteratively improve them. The experiments show that active learning really helps to improve the generated assertions.
Keywords
Active learning, Assertion generation, Testing
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ICSE '17: Proceedings of the 39th IEEE/ACM International Conference on Software Engineering: Buenos Aires, Argentina, May 20-28
First Page
155
Last Page
157
ISBN
9781538615898
Identifier
10.1109/ICSE-C.2017.87
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
PHAM, Long H.; SUN, Jun; and SUN, Jun.
Assertion generation through active learning. (2017). ICSE '17: Proceedings of the 39th IEEE/ACM International Conference on Software Engineering: Buenos Aires, Argentina, May 20-28. 155-157.
Available at: https://ink.library.smu.edu.sg/sis_research/4706
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICSE-C.2017.87