Distributed Software Development today is one of the most widely used and implemented software development strategies in the industry [1]. Some of the major advantages of this methodology are 24 hour work-cycle [2], increased diversity of resources, reduced labor costs, decreased time of iteration cycle and diverse skillset of the workforce [3]. Although it has proven to be quite efficient and practical, there are ample reasons from previous research [4][5] in this field that show that this development approach is uncertain in terms of quality of product developed, speed and expenses. Factors such as presence of multiple stakeholders, lack of effective communication among sites, cultural differences among the workforce and presence of a diverse range of system variables brings a level of uncertainty into the system. A method is required to simulate iterations of the software development lifecycle and understand the effect of changes in system variables/stakeholders involved. This would help project managers, business analysts and other parties involved from different sites to examine the effect of changes in one variable at any point to the other variables and inspect its short and long term consequence on the project plan and deliverables. Problems leading to faulty product development, failure in conforming to all the lifecycle requirements, decreased customer satisfaction, unforeseen expenses and inability to meet deadlines can be avoided by predicting changes using those predictions to make better decisions.

In this thesis, I have created a simulated model of Distributed Software Development using the concept of System Dynamics [6]. My main purpose is to define the different variables, and stakeholders involved in this methodology. Furthermore, I aim to define relationships among them, analyze and draw sufficient conclusions that would help understand and decrease uncertainty. As an example, the results of the simulation show prediction of change of the number of customers, features released with time for the given product as other variables in the system change. This can help project directors, managers and leads to make better informed decisions about the steps they can take to maximize their product growth in the market.