No abstract

Agile development processes are popular when attempting to respond to changing requirements in a controlled manner; however, selecting an ill-suited process may increase project costs and risk. Before adopting a seemingly promising agile approach, we desire to evaluate the approach's applicability in the context of the specific product, organization, and staff. Simulation provides a means to do this. However, in order to simulate agile processes we require both the ability to model individual behavior as well as the decoupling of the process and product. To our knowledge, no existing simulator nor underlying simulation model provide a means to do this. To address this gap, we introduce a process simulation reference model that provides the constructs and relationships for capturing the interactions among the individuals, product, process, and project in a holistic fashion-a necessary first step towards an agile-process evaluation environment.

Simulating agile processes prior to adoption can reduce the risk of enacting an ill-tting process. Agent-based simulation is wellsuited to capture the individual decision-making valued in agile. Yet, agile's lightweight nature creates simulation di culties as agents must ll-in gaps within the speci ed process. Deliberative agents can do this given a suitable planning domain model. However, no such model, nor guidance for creating one, currently exists. In this work, we propose a means for constructing an agile planning domain model suitable for agent-based simulation. As such, the domain model must ensure that all activity sequences derived from the model are executable by a software agent. We prescribe iterative elaboration and decomposition of an existing process to generate successive internally-complete and-consistent domain models, thereby ensuring plans derived from the model are valid. We then demonstrate how to generate a domain model and exemplify its use in planning the actions of a single agent. CCS CONCEPTS •Computing methodologies → Modeling methodologies; Agent / discrete models; Planning and scheduling; •Software and its engineering → Agile software development;