ICSP '09 Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes
A reusable model of causal graph in qualitative reasoning
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
The economic impact of software process variations
ICSP'07 Proceedings of the 2007 international conference on Software process
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
Semi-quantitative simulation modeling of software engineering process
SPW/ProSim'06 Proceedings of the 2006 international conference on Software Process Simulation and Modeling
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Software process simulation models hold out the promise of improving project planning and control. However, quantitative models require a very detailed understanding of the software process. In particular, process knowledge needs to be represented quantitatively which requires extensive, reliable software project data. When such data is lacking, quantitative models must impose severe constraints, restricting the value of the models. In contrast qualitative models are able to cope with imprecise knowledge by reasoning at a more abstract level. This paper illustrates the value and flexibility of qualitative models by developing a model of the software staffing process and comparing it with other quantitative staffing models. We show that the qualitative model provides more insights into the staffing process than the quantitative models because it requires fewer constraints and can thus simulate more behaviors. In particular, the qualitative model produces three possible outcomes: adding staff can increases project duration (i.e. Brooks' Law), adding staff may not affect duration, or adding staff may decrease duration. The qualitative model allows us to determine the conditions under which the different outcomes can occur.