论文

Software development projects are difficult to manage due to the high uncertainty in their various phases. Simulation is one of the tools that has been used to help software project managers produce project plans. Research into software process simulation modeling (SPSM) shows the dominance of discrete-event simulation and system dynamics. This paper supports the use of ABS in SPSM. We propose a practical effort function to estimate developers’ behavior. The other contribution of this paper is to demonstrate how the ABS model can be developed, calibrated and validated using data readily available to many software development companies/departments. This paper focuses on the construction phase of a tailored Rational Unified Process used in a geographically distributed software development department at AVL. The results look promising but more work needs to be done to include ABS into one of the mainstream simulation paradigms in SPSM

When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity across individuals and in the network of interactions among them. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Obviously, deterministic models yield a single trajectory for each parameter set, while stochastic models yield a distribution of outcomes.