Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Thu 14 Nov 2019 10:40 - 11:00 at Cortez 2&3 - Deep Models Chair(s): Nazareno Aguirre

Game testing has been long recognized as a notoriously challenging task, which mainly relies on manual playing and scripting based testing in game industry. Even until recently, automated game testing still remains to be largely untouched niche. A key challenge is that game testing often requires to play the game as a sequential decision process. A bug may only be triggered until completing certain difficult intermediate tasks, which requires a certain level of intelligence. The recent success of deep reinforcement learning (DRL) sheds light on advancing automated game testing, without human competitive intelligent support. However, the existing DRLs mostly focus on winning the game rather than game testing. To bridge the gap, in this paper, we first perform an in-depth analysis of 1349 real bugs from four real-world commercial game products. Based on this, we propose four oracles to support automated game testing, and further propose Wuji, an on-the-fly game testing framework, which leverages evolutionary algorithm, DRL and multi-objective optimization to perform automatic game testing. Wuji balances between winning the game and exploring the space of the game. Winning the game allows the agent to make progress in the game, while space exploration increases the possibility of discovering bugs. We conduct a large-scale evaluation on a simple game and two popular commercial games. The results demonstrate the effectiveness of Wuji in exploring space and detecting bugs. Moreover, Wuji found 3 previously unknown bugs, which have been confirmed by the developers, in the commercial games.

Thu 14 Nov

10:40 - 12:20: Papers - Deep Models at Cortez 2&3
Chair(s): Nazareno AguirreDept. of Computer Science FCEFQyN, University of Rio Cuarto
ase-2019-papers10:40 - 11:00
Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement LearningACM SIGSOFT Distinguished Paper Award
Yan ZhengTianjin University, Xiaofei XieNanyang Technological University, Ting SuETH Zurich, Lei MaKyushu University, Jianye HaoTianjin University, Zhaopeng MengTianjin University, Yang LiuNanyang Technological University, Singapore, Ruimin ShenFuxi AI Lab in Netease, Yinfeng ChenFuxi AI Lab in Netease, Changjie FanFuxi AI Lab in Netease
ase-2019-papers11:00 - 11:20
A Study of Oracle Approximations in Testing Deep Learning Libraries
Mahdi NejadgholiConcordia University, Jinqiu YangConcordia University, Montreal, Canada
ase-2019-papers11:20 - 11:40
Property Inference for Deep Neural Networks
Divya GopinathCarnegie Mellon University, Hayes ConverseThe University of Texas at Austin, Corina S. PasareanuCarnegie Mellon University Silicon Valley, NASA Ames Research Center, Ankur TalyGoogle
ase-2019-papers11:40 - 12:00
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
Qianyu GuoTianjin University, Sen ChenNanyang Technological University, Singapore, Xiaofei XieNanyang Technological University, Lei MaKyushu University, Qiang HuKyushu University, Japan, Hongtao LiuTianjin University, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University, Li XiaohongTianJin University
ase-2019-Demonstrations12:00 - 12:10
DeepMutation++: a Mutation Testing Framework for Deep Learning Systems
Qiang HuKyushu University, Japan, Lei MaKyushu University, Xiaofei XieNanyang Technological University, Bing YuKyushu University, Japan, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University
ase-2019-Demonstrations12:10 - 12:20
DeepHunter: A Coverage-Guided Fuzzer for Deep Neural Networks
Xiaofei XieNanyang Technological University, Hongxu ChenNanyang Technological University, Yi LiNanyang Technological University, Lei MaKyushu University, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University