An Empirical Approach for the Assessment of Scheduling Risk in a Large Globally Distributed Industrial Software Project

We describe an empirical approach for the assessment of the scheduling risk in a large globally distributed industrial software project. We analyzed two and half years of project data and we identified some factors that are likely to impact team productivity and that could affect the teampsilas ability to meet its schedule objective. We built an analytical model to capture teampsilas productivity variability, which can be used to approximate the total time required to complete the project. Our approach is designed to be used by project management groups to perform risk assessment early in the software development process.