发表机构
Radboud University, Institute iCIS; University of Passau; TNO-ESI（拉德堡德大学，iCIS研究所; 帕绍大学; TNO-ESI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对Neatest测试生成器难以扩展且覆盖不等于游戏玩法的问题，提出结合基于模型的测试，用抽象游戏模型引导神经进化，在13个Scratch游戏上分支覆盖率提升7%。
AI 中文摘要
自动生成游戏的测试输入具有挑战性，因为测试生成器必须掌握游戏以到达高级程序状态，同时还要确保对游戏固有的重度程序随机化具有鲁棒性。因此，测试生成器Neatest优化由神经网络组成的测试套件，这些神经网络能够到达高级程序状态并对程序随机化具有鲁棒性，因为它们基于当前程序状态动态生成测试输入。Neatest是一种白盒测试方法，旨在通过神经进化为每个尚未覆盖的代码语句或分支生成一个网络代理。由于这种迭代式测试生成方法，该算法难以扩展到可能包含数千个分支的大型程序。此外，覆盖游戏中的每个语句或分支通常并不等同于按预期方式玩游戏。为了缓解这些缺点，我们提出将Neatest与基于模型的测试方法相结合，该方法允许游戏测试人员通过抽象游戏模型定义测试场景。测试生成器不再优化网络以到达程序的所有分支或语句，而是训练网络以复制抽象游戏模型所表达的具体的期望测试行为。对13个不同流派的Scratch游戏的评估表明，Neatest与基于模型的测试相结合，能够优化复制游戏模型中定义的期望游戏行为的代理，同时与传统代码引导的Neatest方法相比，分支覆盖率提高了7%。
英文摘要
Automatically generating test inputs for games is challenging, as test generators must master the game to reach advanced program states while also ensuring robustness against the heavy program randomisation inherent to games. The test generator Neatest therefore optimises test suites consisting of neural networks that reach advanced program states and are robust to program randomisation, as they generate test inputs dynamically based on the current program state. Neatest is a white-box testing approach that aims to generate a network agent for each yet-uncovered statement or branch of the code using neuroevolution. Due to this iterative test generation approach, the algorithm does not scale well to larger programs that may contain thousands of branches. Furthermore, covering every statement or branch in a game often does not correspond to playing the game as intended. To alleviate these shortcomings, we propose combining Neatest with a model-based testing approach that allows game testers to define test scenarios via abstract game models. The test generator then no longer optimises networks to reach all branches or statements of a program, but instead trains networks to replicate the concrete desired testing behaviour expressed by the abstract game model. An evaluation on 13 Scratch games across varying genres demonstrates that Neatest, combined with model-based testing, is able to optimise agents that replicate the desired gameplay behaviour defined in the game models while increasing achieved branch coverage by 7% compared to the traditional code-guided Neatest approach.
CommentsAccepted at the 38th International Conference on Testing Software and Systems (ICTSS 2026)
