一句话：提出首个面向游戏软件的回归测试选择技术，将测试套件建模为状态转换图并结合变更影响分析，显著减少测试并发现回归缺陷。
论文关注视频游戏频繁更新与长时间测试使传统回归测试难以适用的问题。作者先通过经验研究分析游戏开发流程、演化缺陷和上下文敏感性，进而提出首个面向游戏软件的回归测试选择方法：将游戏测试套件建模为状态转换图（STG），建立 STG 状态/动作与美术资源、设计文件及源代码的依赖，并进行变更影响分析，选出可能受影响的测试路径。作者实现 GameRTS，在大型商业游戏三个版本、1,429 次提交的 10 个任务上评估。结果表明，GameRTS 多数任务仅选择一条 STG 轨迹，显著减少测试时间，并检出评估套件中全部回归缺陷；相比文件级 RTS，其选择的状态、动作和轨迹分别仅为 13.77%、23.97%、6.85%，还发现 2 个新的关键回归缺陷。
Continuous game quality assurance is of great importance to satisfy the increasing demands of users. To respond to game issues reported by users timely, game com-panies often create and maintain a large number of releases, updates, and tweaks in a short time. Regression testing is an essential technique adopted to detect regression issues during the evolution of the game software. However, due to the special characteristics of game software (e.g., frequent updates and long-running tests), traditional regression testing techniques are not directly applicable. To bridge this gap, in this paper, we perform an early exploratory study to investigate the challenges in regression testing of video games. We first performed empirical studies to better understand the game development process, bugs introduced during game evolution, and the context sensitivity. Based on the results of the study, we proposed the first regression test selection (RTS) technique for game software, which is a compromise between safety and practicality. In particular, we model the test suite of game software as a State Transition Graph (STG) and then perform the RTS on the STG. We establish the dependencies between the states/actions of STG and game files, including game art resources, game design files, and source code, and perform change impact analysis to identify the states/actions (in the STG) that potentially execute such changes. We implemented our framework in a tool, named GameRTS, and evaluated its usefulness on 10 tasks of a large-scale commercial game, including a total of 1,429 commits over three versions. The experimental results demonstrate the usefulness and effectiveness of GameRTS in game RTS. For most tasks, GameRTS only selected one trace from STG, which can significantly reduce the testing time. Furthermore, GameRTS detects all the regression bugs from the test evaluation suites. Compared with the file-level RTS, GameRTS selected fewer states/actions/traces (i.e., 13.77%, 23.97%, 6.85%). In addition, GameRTS identified 2 new critical regression bugs in the game.
@inproceedings{YuWXLMCHZ23,
title = {GameRTS: A Regression Testing Framework for Video Games},
author = {Jiongchi Yu and Yuechen Wu and Xiaofei Xie and Wei Le and Lei Ma and Yingfeng Chen and Jingyu Hu and Fan Zhang},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00122}
}