Debugging in the Domain-Specific Modeling Languages for multi-agent systems

Abstract

In many cases, developers face challenges while implementing Multi-Agent Systems (MAS) due to the complexity of expanding software systems, despite the presence of numerous agent programming environments and platforms. To tackle this complexity, Model-driven Engineering (MDE) can be employed at a higher level of abstraction and component modeling before diving into MAS development, which helps alleviate the intricacies. Probably, the most effective method of incorporating MDE into Multi-Agent Systems (MAS) is to adapt Domain-Specific Modeling Languages (DSMLs) along with integrated development environments (IDEs). These tools make it easier to model the system and generate the necessary code for the development process. Although existing MAS DSML IDEs offer some control over systems modeled based on the language’s syntax and semantics, they lack built-in debugging support. This deficiency leads to uncertainty among agent developers about the accuracy of models prepared during the design phase. To address this issue, this study proposes a comprehensive debugging framework (MASDebugFW) that facilitates the design of agent components within modeling environments. The framework’s utilization commences with modeling MASs using a design language, and then converting these design model instances into a runtime model. Following that, the runtime model undergoes simulation using an integrated simulator specifically designed for debugging purposes. Additionally, the framework includes a simulation environment model and a control mechanism to manage the simulation process effectively. These features further enhance the debugging capabilities and overall functionality of MASDebugFW. Furthermore, we have qualitatively and quantitatively evaluated MASDebugFW, subjecting all obtained results to statistical analysis. The evaluation results show that, on average, the implemented framework reduces debugging time by around 45%, leading to more efficient debugging processes. Moreover, it significantly enhances bug detection and repair capabilities, as it increases the number of bugs fixed in the models by approximately 50%.