One of the solutions to the software complexity crisis of this era is the proposition of self-managing systems like autonomous and autonomic systems. The idea has gained wide acceptance in the IT industry but it has also introduced the challenge of specification and development of such systems. Swarm intelligence is finding its applications in research and design of self-managing systems because of the coincidental resemblance between the two domains. However, specification of a swarm-based self-managing system is faced with the difficulty of specifying the complex evolving behavior. 
This thesis presents an adaptation of a mathematical technique known as Category Theory to serve as a ‘reasoning and modeling’ paradigm for specifying high-level behavioral patterns of a swarm-based self-managing systems. The crux of this paradigm is the formal categorical modeling language (CML). CML syntax and semantics have been defined using an EBNF-based context-free grammar. The language helps to generate a formal specification of different scenarios/behavioral patterns of a swarm-based system. Moreover, a prototype tool has been implemented as part of this research work to serve as a modeling tool based on CML. In this thesis, NASA’s ANTS-based Prospecting Asteroid Mission (PAM) serves as a case study to analyze the applicability and usability of CML as a formal method of choice.