The Dynamics of Software Project Staffing: A System Dynamics Based Simulation Approach
IEEE Transactions on Software Engineering
Software project dynamics: an integrated approach
Software project dynamics: an integrated approach
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
The Mythical Man-Month: Essays on Softw
The Mythical Man-Month: Essays on Softw
Qualitative Simulation Model for Software Engineering Process
ASWEC '06 Proceedings of the Australian Software Engineering Conference
ICSP '09 Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes
A framework for adopting software process simulation in CMMI organizations
ICSP'07 Proceedings of the 2007 international conference on Software process
Achieving software project success: a semi-quantitative approach
ICSP'07 Proceedings of the 2007 international conference on Software process
Impact of process simulation on software practice: an initial report
Proceedings of the 33rd International Conference on Software Engineering
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Software process simulation models hold out the promise of improving project planning and control. However, purely quantitative models require a very detailed understanding of the software process, i.e. process knowledge represented quantitatively. When such data is lacking, quantitative models impose severe constraints, restricting the model's value. In contrast, qualitative models display all possible behaviors but only in qualitative terms. This paper illustrates the value and flexibility of semi-quantitative modeling by developing a model of the software staffing process and comparing it with other quantitative staffing models. We show that the semi-quantitative model provides more insights into the staffing process and more confidence in the outcomes than the quantitative models by achieving a tradeoff between quantitative and qualitative simulation. In particular, the semi-quantitative simulation produces a set of possible outcomes with the ranges of real numeric values. The semi-quantitative model allows us to determine the solution boundaries for specific scenarios under the conditions of limited knowledge.