A Study of Open Source Evolution Data using Qualitative Simulation

Smith, Neil; Capiluppi, Andrea and Ramil, Juan F. (2005). A Study of Open Source Evolution Data using Qualitative Simulation. Software Process: Improvement and Practice, 10(3) pp. 287–300.

DOI: https://doi.org/10.1002/spip.230


Simulation modelling of growth trends and other empirical data from software systems can reveal the main drivers of software evolution and help improve understanding and management of the software evolution phenomenon. This article reports on the application of qualitative abstraction techniques to data from 25 open source software (OSS) systems. After justifying the use of qualitative simulation techniques for the software process domain, the article presents an analysis of the support from OSS-derived data to previously developed qualitative simulation models, which were inspired by observations from the domain of proprietary systems. The analysis involved comparison of model output and qualitatively abstracted growth trends. The analysis also involved the comparison of the transitions in trends of functional size and complexity to those predicted by the models in a sub-set of 21 systems for which complexity data was available. The results indicate that models are able to replicate some of the features in the data. This, in turn, suggests that the study of the relationship between size and complexity and its interaction via feedback loops has a role in explaining the long-term evolutionary behaviour of OSS systems. This is not surprising since it is well known that the evolution of all type of real-world software systems, proprietary and OSS, is driven by feedback from their stakeholders.

Viewing alternatives


Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions