## 1 Introduction  
The use of multi-agent systems (MASs) has increased in industrial and academic applications due to their capacity to deal with physically or logically distributed problems. MASs solve complex problems with entities called _agents_, using their collaborative and autonomous properties [1]. They have been developed for a wide range of applications, including diagnostics, condition monitoring, power systems restoration, market simulation, network control, automation, logistics, manufacturing, operational research and management science, sociotechnical studies, industrial engineering, technological innovation, control engineering, and workflow systems [2]. Moreover, they set singular characteristics that pertain to agents such as reactivity, proactivity, autonomy, and social capacity [3]. However, a MAS still demands reliable tools for verification activities, including testing procedures. Testing MASs is not a trivial task. It deals with systems that have properties such as autonomy and are deliberative in an open world, which makes them sensitive to the context [4]. Some applications, especially those with real-time constraints and more critical requisites, require some level of guarantee about their risks. They include modern applications such as cyberphysical systems integrated with digital twins for controlled objects [5]. A constant criticism when using MAS approaches is the lack of determinism on a large scale. For example, a recent analysis of COVID-19 pandemic models highlights a lack of transparency, reproducibility, and an urgent need for more rigorous validation, especially those based on MAS models [6; 7]. Testing is just one activity of a complete verification and validation framework. It provides a way to assess the likelihood that software will fail during use and is broadly divided into dynamic software testing and formal verification [8]. Nevertheless, some works propose combining these techniques, formal methods, simulation, and experiments, increasing the coverability, i.e., exploring the state space of MAS solutions [9; 10]. In addition, MASs is a very broad research topic, including machine learning and other non-symbolic issues, and testing those approaches is entirely different from testing symbolic approaches. This topic is inside a broad area named engineering multi-agent systems, which covers subtopics such as agent systems verification [11], requirements engineering for developing MASs [12], and testing for MASs [13], the latter one more closely related with this work. The MAS social level has, formally or informally, an underlying organizational model. Organizational frameworks can reduce the amount of interaction between agents, reduce or explicitly increase system redundancy, and formalize high-level system goals. A single agent cannot be aware of all social knowledge [14]. An organization establishes a set of behavioral constraints that a group of agents employs to control agent autonomy and achieve their goals efficiently [15]. With an organizational modeling language that explicitly decomposes a specification into structural, functional, and deontic dimensions, the \(\mathcal{M}\)oise+ is one of the most referenced organizational models. Organizations are described using notions such as _roles_, _groups_, and _links_ in the structural dimension. A system is described by global collective goals that must be achieved in the functional dimension. Finally, the deontic dimension binds the structural and functional aspects together, defining _permissions_ for each role and mission _obligations_[16]. Usually, an MAS based on a \(\mathcal{M}\)oise+ organization is implemented with Jason, an agent platform based on an extension of an agent-oriented programming language called AgentSpeak(L) [17]. High-level Petri nets are a widely used modeling, specification, and validation tool for information system behavior in general. Combining a simple graphical notation with a mathematical foundation is their differential when compared with correlate techniques.