Context:

Agent-based models play an important role in simulating complex emergent phenomena and supporting critical decisions. In this context, a software fault may result in poorly informed decisions that lead to disastrous consequences. The ability to rigorously test these models is therefore essential.

Objective:

Our objective is to summarise the state-of-the-art techniques for test case generation in agent-based models and identify future research directions.

Method:

We have conducted a systematic literature review in which we pose five research questions related to the key aspects of test case generation in agent-based models: What are the information artifacts used to generate tests? How are these tests generated? How is a verdict assigned to a generated test? How is the adequacy of a generated test suite measured? What level of abstraction of an agent-based model is targeted by a generated test?

Results:

Out of the 464 initial search results, we identified 24 primary publications. Based on these primary publications, we formed a taxonomy to summarise the state-of-the-art techniques for test case generation in agent-based models. Our results show that whilst the majority of techniques are effective for testing functional requirements at the agent and integration levels of abstraction, there are comparatively few techniques capable of testing society-level behaviour. Furthermore, the majority of techniques cannot test non-functional requirements or “soft goals”.

Conclusions:

This paper reports insights into the key developments and open challenges concerning test case generation in agent-based models that may be of interest to both researchers and practitioners. In particular, we identify the need for test case generation techniques that focus on societal and non-functional behaviour, and a more thorough evaluation using realistic case studies that feature challenging properties associated with a typical agent-based model.

中文翻译：

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基于代理的模型的测试用例生成：系统的文献综述

语境：

基于代理的模型在模拟复杂的突发现象和支持关键决策方面发挥着重要作用。在这种情况下，软件故障可能会导致决策不充分，从而导致灾难性后果。因此，严格测试这些模型的能力至关重要。

客观的：

我们的目标是总结在基于代理的模型中生成测试用例的最新技术，并确定未来的研究方向。

方法：

我们进行了系统的文献综述，其中提出了五个与基于代理的模型中的测试案例生成的关键方面有关的研究问题：用于生成测试的信息工件是什么？这些测试是如何产生的？裁决如何分配给生成的测试？如何衡量生成的测试套件的充分性？生成的测试针对的是基于代理的模型的抽象级别？

结果：

在464个初始搜索结果中，我们确定了24个主要出版物。基于这些主要出版物，我们形成了一种分类法，以总结基于代理的模型中测试用例生成的最新技术。我们的结果表明，尽管大多数技术可以有效地测试代理和抽象级别的功能需求，但是相对而言，很少有能够测试社会行为的技术。此外，大多数技术无法测试非功能性需求或“软目标”。

结论：

本文报告了有关基于代理的模型中测试案例生成的关键发展和开放挑战的见解，研究人员和从业人员都可能会感兴趣。特别是，我们确定需要针对社会和非功能性行为的测试用例生成技术，以及使用具有与典型基于代理的模型相关的具有挑战性的特征的现实案例研究进行更全面的评估。