AI 中文总结
本文将游戏平衡回归测试建模为有限模拟预算下的边界发现问题，提出BBExplorer方法，经两款不同复杂度游戏实验验证，可高效识别平衡与非平衡区域边界，适用于非确定性、预算受限系统的边界导向测试。
AI 中文摘要
软件测试通常依赖可复现执行、稳定正确性标准等假设，但许多现代软件系统存在非确定性执行、庞大行为空间，导致穷举探索不现实、单次运行判断不可靠，难以界定可接受行为与问题行为的边界。竞争性多人游戏是这类系统的典型挑战实例，需维持平衡以避免单一策略主导，微小参数变化即可引发平衡突然破坏，检测此类故障需在非确定性结果和高维参数空间下重复模拟。本文将游戏平衡回归测试建模为有限模拟预算下的边界发现问题，目标是高效识别平衡与非平衡区域边界附近的输入。为解决该问题，提出BBExplorer，结合多方向候选生成、感知预算的两阶段筛选、自适应步长收缩以优化边界。在两款复杂度不同的游戏上的实验结果表明，该方法在低维环境中表现优异，在高维环境中仍有效，且在未见过的随机种子和阈值设置下展现出稳定的边界行为。这些结果表明，BBExplorer对实际平衡回归测试有效，更广泛而言，对非确定性、预算受限系统中面向边界的测试也有效。
英文摘要
Software testing often relies on assumptions such as reproducible executions and stable correctness criteria. However, many modern software systems exhibit non-deterministic executions and large behavior spaces, making exhaustive exploration impractical and single-run judgments unreliable. These characteristics make it difficult to identify where acceptable behavior ends and problematic behavior begins. Competitive multiplayer games represent a challenging instance of such systems, where balance must be maintained so that no single strategy dominates. Even small parameter changes can trigger abrupt balance disruption, yet detecting such failures requires repeated simulations under non-deterministic outcomes and high-dimensional parameter spaces. In this paper, we formulate game balance regression testing as a boundary-discovery problem under a finite simulation budget. The objective is to efficiently identify inputs near the boundary that separates balanced and unbalanced regions. To address this problem, we propose BBExplorer, which combines multi-directional candidate generation, budget-aware two-stage screening, and adaptive step-size shrinkage for boundary refinement. Experimental results on two games with different levels of complexity show that the approach is strong in low-dimensional settings and remains effective in higher-dimensional ones. It also exhibits stable boundary behavior across unseen random seeds and threshold settings. These results indicate that BBExplorer is effective for practical balance regression testing and, more broadly, for boundary-oriented testing in non-deterministic, budget-constrained systems.
CommentsAccepted to ASE 2026 Research Track
