Comparative Analysis of Evolving Software Systems Using the Gini Coefficient

Vasa, Rajesh; Lumpe, Markus; Branch, Philip; Nierstrasz, Oscar (2009). Comparative Analysis of Evolving Software Systems Using the Gini Coefficient. International Conference on Software Maintenance. Proceedings, pp. 179-188. Washington, DC: IEEE 10.1109/ICSM.2009.5306322

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Software metrics offer us the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics --- for example, in terms of "average" values --- can be highly misleading. Many metrics, it turns out, are distributed like wealth --- with high concentrations of values in selected locations. We propose to analyze software metrics using the Gini coefficient, a higher-order statistic widely used in economics to study the distribution of wealth. Our approach allows us not only to observe changes in software systems efficiently, but also to assess project risks and monitor the development process itself. We apply the Gini coefficient to numerous metrics over a range of software projects, and we show that many metrics not only display remarkably high Gini values, but that these values are remarkably consistent as a project evolves over time.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF)
08 Faculty of Science > Institute of Computer Science (INF) > Software Composition Group (SCG)

UniBE Contributor:

Nierstrasz, Oscar Marius

ISSN:

1063-6773

ISBN:

978-1-4244-4897-5

Publisher:

IEEE

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 15:22

Last Modified:

13 Sep 2017 08:40

Publisher DOI:

10.1109/ICSM.2009.5306322

BORIS DOI:

10.7892/boris.37171

URI:

https://boris.unibe.ch/id/eprint/37171 (FactScience: 207120)

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