集成强化学习的基于BDD的框架：一种电子游戏自动化测试方法
BDD-Based Framework with RL Integration: An approach for videogames automated testing
AI总结：
针对电子游戏复杂度提升导致脚本化自动化测试遇瓶颈的问题，提出集成行为驱动开发（BDD）与强化学习（RL）的测试框架，助力游戏工作室通过自动化测试提升游戏质量，论文为相关提案与在研框架的立场性总结。
摘要：
测试在软件开发中至关重要，但在电子游戏领域，测试流程与传统软件开发实践存在差异。游戏开发者通常依赖配备检查清单的人工测试人员评估各类要素。大型游戏开发商虽已采用基于脚本的机器人开展自动化测试，但电子游戏复杂度的不断提升正逼近脚本化解决方案的极限，亟需采用更先进的测试策略。为帮助游戏工作室通过自动化测试提升游戏质量，我们提出将行为驱动开发（Behavior Driven Development，BDD）与强化学习（Reinforcement Learning，RL）相集成。本立场论文概述了我们的提案及正在开发的框架。
英文摘要：
Testing plays a vital role in software development, but in the realm of video games, the process differs from traditional software development practices. Game developers typically rely on human testers who are provided with checklists to evaluate various elements. While major game developers already employ automated testing using script-based bots, the increasing complexity of video games is pushing the limits of scripted solutions, necessitating the adoption of more advanced testing strategies. To assist game studios in enhancing the quality of their games through automated testing, we propose the integration of Behavior Driven Development (BDD) with Reinforcement Learning (RL). This positional paper summarizes our proposal and framework under development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
