一句话：提出用强化学习训练智能体，在模拟人类游玩的同时对电子游戏进行负载测试，识别帧率下降区域。
电子游戏测试因需要持续且智能的用户交互，长期以人工测试为主。现有工作虽已用强化学习部分自动化功能测试，但尚少涉及其负载/性能测试。本文研究用强化学习训练智能体，使其能像人类一样游玩，并在探索过程中定位导致帧率（FPS）下降的游戏区域，从而支持负载测试。作者在三个游戏上评估该方法：其中两个游戏通过注入人工性能缺陷进行概念验证，第三个为开源3D游戏，使用训练好的智能体实施负载测试。结果表明该方法可行，能够发现造成FPS降低的区域，展现了强化学习用于游戏负载测试的潜力。
Different from what happens for most types of software systems, testing video games has largely remained a manual activity performed by human testers. This is mostly due to the continuous and intelligent user interaction video games require. Recently, reinforcement learning (RL) has been exploited to partially automate functional testing. RL enables training smart agents that can even achieve super-human performance in playing games, thus being suitable to explore them looking for bugs. We investigate the possibility of using RL for load testing video games. Indeed, the goal of game testing is not only to identify functional bugs, but also to examine the game's performance, such as its ability to avoid lags and keep a minimum number of frames per second (FPS) when high-demanding 3D scenes are shown on screen. We define a methodology employing RL to train an agent able to play the game as a human while also trying to identify areas of the game resulting in a drop of FPS. We demonstrate the feasibility of our approach on three games. Two of them are used as proof-of-concept, by injecting artificial performance bugs. The third one is an open-source 3D game that we load test using the trained agent showing its potential to identify areas of the game resulting in lower FPS.
DOI 原文 ·
@inproceedings{paperbot6929,
title = {Using reinforcement learning for load testing of video games},
author = {Rosalia Tufano and Simone Scalabrino and Luca Pascarella and Emad Aghajani and Rocco Oliveto and Gabriele Bavota},
booktitle = {ICSE},
year = {2022},
doi = {10.1145/3510003.3510625}
}