用调用栈指导智能体，让游戏自动化测试更精准高效
CA2: Code-Aware Agent for Automated Game Testing
- 基于调用栈和游戏状态学习测试策略
- 相比不使用代码信息的方法，覆盖率显著提升
- 适合游戏开发与自动化测试工程师参考
自动化游戏测试对验证游戏功能至关重要，但成本高且耗时。人工测试常遗漏边缘情况，现有自动化方法难以实现全代码覆盖。以往研究尝试用强化学习进行游戏测试，但未利用调用栈等内部代码信号。本文提出代码感知智能体CA2，通过接收当前函数调用轨迹和游戏状态，学习到达特定目标函数的策略。我们在两类环境中（基于状态和基于图像）进行了代码插桩，支持高效提取调用栈。实验表明，CA2在多个指标上持续优于不使用调用栈信息的基线方法。结果证明，引入调用栈等代码信号可实现更有效、更精准的游戏测试。
原文摘要 · Abstract (English)
Automated game testing is important for verifying game functionality, but it remains a costly and time-consuming process. Manual testing often misses edge cases, and current automated methods struggle to provide full code coverage. Prior work has explored reinforcement learning (RL) for game testing, but without leveraging internal code signals such as the call stack. We present Code Aware Agent (CA2), which uses call stack information to learn effective testing strategies. The agent receives the current function call trace along with the game state and learns to reach specific target functions. We instrument two types of environments, 1) State-based and 2) Image-based, with support for efficient call stack extraction. Through experimental evaluation, we find that CA2 achieves consistent improvement over the non-code aware baselines, which does not leverage call stack information. Our results show that incorporating code signals like the call stack enables more effective and targeted game testing.
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