Determining what factors can influence the successful outcome of a software project has been labeled by many scholars and software engineers as a difficult problem. In this paper we use machine learning to create a model that can determine the stage a software project has obtained with some accuracy. Our model uses 8 Open Source project metrics to determine the stage a project is in. We validate our model using two performance measures; the exact success rate of classifying an Open Source Software project and the success rate over an interval of one stage of its actual performance using different scales of our dependent variable. In all cases we obtain an accuracy of above 70% with one away classification (a classification which is away by one) and about 40% accuracy with an exact classification. We also determine the factors (according to one classifier) that uses only eight variables among all the variables available in SourceForge, that determine the health of an OSS project.
|Title of host publication||Open Source Software: Quality Verification: 9th IFIP WG 2.13 International Conference, OSS 2013, Koper-Capodistria, Slovenia, June 25-28, 2013. Proceedings|
|Editors||Etiel Petrinja, Giancarlo Succi, Nabil El Ioini, Alberto Sillitti|
|Publication status||Published - 2013|
|Name||IFIP advances in information and communication technology|