Middle-out domain-specific aspect languages and their application in agent-based modelling runtime inspection

Maddra, Craig Ashley

Abstract

Domain-Specific Aspect Languages (DSALs) are a valuable tool for separating cross-cutting concerns, particularly within fields with endemic cross-cutting practices. Agent-Based Modelling (ABM) runtime inspection, which cuts across the core concern of model development, serves as a prime example. Despite their usefulness, DSALs face multiple adoption issues: the literature regarding their development and use is incohesive, coupling to a weave target hinders re-use, and available tooling is immature compared to Domain-Specific Languages (DSLs). We believe these issues can be aided by furthering DSL middle-out techniques for DSALs.

We first define the background of what a DSAL is and how they may be used, moving onto how we can use DSL techniques to further DSALs. We develop a middle-out semantic model approach for developing domain-level DSALs with transparent aspect orientation using adaptions of DSL techniques. We have implemented the approach for model-specific DSALs for the in-house framework Animaux, and as middleware-specific DSAL for agent messages in the JADE framework, which can be specialised to models using extension DSALs. We give illustrative result cases using our implementations to provide a base of the user development costs and performance of this approach.

In conclusion, we believe the adoption of these technologies aids ABM applications and encourage future work in similar fields. This thesis has given a base philosophy toward DSLs, a novel approach for the development of middle-out DSALs and illustrative cases of this approach.

Citation

Maddra, C. A. (2019). Middle-out domain-specific aspect languages and their application in agent-based modelling runtime inspection. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4221550

• Thesis Type: Thesis
• Deposit Date: May 13, 2019
• Publicly Available Date: Feb 23, 2023
• Keywords: Computer science
• Public URL: https://hull-repository.worktribe.com/output/4221550
• Additional Information: School of Engineering and Computer Science, The University of Hull
• Award Date: Apr 1, 2019