Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2020
Abstract
Regression testing aims to check the functionality consistency during software evolution. Although general regression testing has been extensively studied, regression testing in the context of video games, especially Massively Multiplayer Online Role-Playing Games (MMORPGs), is largely untouched so far. One big challenge is that game testing requires a certain level of intelligence in generating suitable action sequences among the huge search space, to accomplish complex tasks in the MMORPG. Existing game testing mainly relies on either the manual playing or manual scripting, which are labor-intensive and time-consuming. Even worse, it is often unable to satisfy the frequent industrial game evolution. The recent process in machine learning brings new opportunities for automatic game playing and testing. In this paper, we propose a reinforcement learning-based regression testing technique that explores differential behaviors between multiple versions of an MMORPGs such that the potential regression bugs could be detected. The preliminary evaluation on real industrial MMORPGs demonstrates the promising of our technique.
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), Adelaide, Australia, September 28 - October 2
First Page
692
Last Page
696
ISBN
9781728156194
Identifier
10.1109/ICSME46990.2020.00074
Publisher
IEEE
City or Country
Adelaide, Australia
Citation
WU, Yuechen; CHEN, Yingfeng; XIE, Xiaofei; YU, Bing; FAN, Changjie; and MA, Lei.
Regression testing of massively multiplayer online role-playing games. (2020). Proceedings of the 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), Adelaide, Australia, September 28 - October 2. 692-696.
Available at: https://ink.library.smu.edu.sg/sis_research/7109
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.