基于模型的机器人软件测试生成：自动机与信念-愿望-意图Agent
Model-based Test Generation for Robotic Software: Automata versus Belief-Desire-Intention Agents
摘要：
机器人代码需要验证以确保其安全性和功能正确性，特别是当机器人与人交互时。在模拟中测试真实代码是一个可行的选择。然而，生成覆盖罕见场景的测试，以及执行大多数代码，是一个挑战，因为环境和软件之间的交互复杂性而放大了。基于模型的测试生成方法可以自动化手动过程，并有助于在测试期间达到罕见的场景。在本文中，我们比较使用信念-欲望-意图（BDI）代理作为模型的测试生成与更传统的基于自动机的技术，利用模型检验，在实用性，性能，可转移到不同的情况下，和探索（“覆盖”），通过两个案例研究：合作制造任务，和家庭护理方案。结果突出了使用BDI代理进行测试生成的优势。BDI代理自然地模仿人机交互（HRI）中的代理，因此比自动机更具表达力。与基于模型检验自动机的测试生成相比，基于BDI的测试生成的性能至少一样高，并且所实现的覆盖率更高或等同。
英文摘要：
Robotic code needs to be verified to ensure its safety and functional correctness, especially when the robot is interacting with people. Testing real code in simulation is a viable option. However, generating tests that cover rare scenarios, as well as exercising most of the code, is a challenge amplified by the complexity of the interactions between the environment and the software. Model-based test generation methods can automate otherwise manual processes and facilitate reaching rare scenarios during testing. In this paper, we compare using Belief-Desire-Intention (BDI) agents as models for test generation with more conventional automata-based techniques that exploit model checking, in terms of practicality, performance, transferability to different scenarios, and exploration (`coverage'), through two case studies: a cooperative manufacturing task, and a home care scenario. The results highlight the advantages of using BDI agents for test generation. BDI agents naturally emulate the agency present in Human-Robot Interactions (HRIs), and are thus more expressive than automata. The performance of the BDI-based test generation is at least as high, and the achieved coverage is higher or equivalent, compared to test generation based on model checking automata.
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。
