Design of Traditional/Hybrid Software Project Tracking Technique: State Space Approach

Software projects are required to be tracked during their execution for controlling them. According to state space approach, the tracking problem leads us to have a project state transition model and project status model. A key factor in modeling software projects is to model the project with uncertainty involved in the parameters related to project state transition model and project status model. Traditional/Hybrid software project tracking technique is formulated, modeled with state space approach in plan-space and execution-space, and simulated using discrete event simulation. The uncertainty considered here is ontological that modeled as a normal distribution using an approximation method. The state space model consists of project-state transition equation and project-measurement equation, in plan-space, and it is formulated with Monte Carlo method in execution-space. The developed state space model is used to track status of the traditional/hybrid project. The project is executed with an iterative and incremental development process. With Monte Carlo simulation runs, simulation result shows the variation in ontological uncertainty iteration-wise, both individually and cumulatively, and the effect of uncertainty on project status, by showing project status in execution-space and plan-space. Besides, the project completion somewhere during the last iteration is shown with simulation. Key-Words: Ontological Uncertainty, Normal Distribution function, State-Space Model, Traditional Project, Hybrid Project, Monte Carlo Simulation, Discrete Event Simulation, Execution-Space, Plan-Space.