Open Source Software (OSS) often relies on large repositories, like SourceForge, for initial incubation. The OSS repositories offer a large variety of meta-data providing interesting information about projects and their success. In this paper we propose a data mining approach for training classifiers on the OSS meta-data provided by such data repositories. The classifiers learn to predict the successful continuation of an OSS project. The successfulness' of projects is defined in terms of the classifier confidence with which it predicts that they could be ported in popular OSS projects (such as FreeBSD, Gentoo Portage).
Information systems success is one of the most widely used dependent variables in information systems (IS) research, but research on free libre and open source software (FLOSS) often fails to appropriately conceptualize this important concept. In this article, we reconsider what success means within a FLOSS context. We first review existing models of IS success and success variables used in FLOSS research and assess them for their usefulness, practicality and fit to the FLOSS context. Then, drawing on a theoretical model of group effectiveness in the FLOSS development process, as well as an on-line discussion with developers, we present additional concepts that are central to an appropriate understanding of success for FLOSS. In order to examine the practicality and validity of this conceptual scheme, the second half of our article presents an empirical study that demonstrates operationalizations of the chosen measures and assesses their internal validity. We use data from SourceForge to measure the project's effectiveness in team building, the speed of the project at responding to bug reports and the project's popularity. We conclude by discussing the implications of this study for our proposed extension of IS success in the context of FLOSS development and highlight future directions for research. Copyright © 2006 John Wiley & Sons, Ltd. A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success. However, the dependent variable in these studies-I S success-has been an elusive one to define. Different researchers have addressed different aspects of success, making comparisons difficult and the prospect of building a cumulative tradition for I S research similarly elusive. To organize this diverse research, as well as to present a more integrated view of the concept of I S success, a comprehensive taxonomy is introduced. This taxonomy posits six major dimensions or categories of I S success-SYSTEM QUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. Using these dimensions, both conceptual and empirical studies are then reviewed a total of 180 articles are cited and organized according to the dimensions of the taxonomy. Finally, the many aspects of I S success are drawn together into a descriptive model and its implications for future I S research are discussed. 