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).
. The most popular model for measuring information systems' (IS) success is the one proposed by DeLone and McLean @cite_4 . They actually introduce six interrelated factors of success: 1) system quality, 2) information quality, 3) use, 4) user satisfaction, 5) individual impact, and 6) organizational impact. Based on this approach, Seddon @cite_7 reexamined the factors that can measure success and concluded that the related factors are system quality, information quality, perceived usefulness, user satisfaction and IS use. Based on those approaches, a number of measures that can be used to assess the success in FLOSS is presented by in @cite_10 . These measures are defined based on the results of a statistical analysis applied to a subject of project data in FLOSS. Specifically the empirical study was based on a subset of SourceForge projects. In this paper we propose another such measure, that can be added to the and the factors of the proposed models of success.
@cite_10 @cite_4 @cite_7
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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. This paper reports on an empirically based system that automatically resolves VP ellipsis in the 644 examples identified in the parsed Penn Treebank. The results reported here represent the first systematic corpus-based study of VP ellipsis resolution, and the performance of the system is comparable to the best existing systems for pronoun resolution. The methodology and utilities described can be applied to other discourse-processing problems, such as other forms of ellipsis and anaphora resolution.The system determines potential antecedents for ellipsis by applying syntactic constraints, and these antecedents are ranked by combining structural and discourse preference factors such as recency, clausal relations, and parallelism. The system is evaluated by comparing its output to the choices of human coders. The system achieves a success rate of 94.8 , where success is defined as sharing of a head between the system choice and the coder choice, while a baseline recency-based scheme achieves a success rate of 75.0 by this measure. Other criteria for success are also examined. When success is defined as an exact, word-for-word match with the coder choice, the system performs with 76.0 accuracy, and the baseline approach achieves only 14.6 accuracy. Analysis of the individual components of the system shows that each of the structural and discourse constraints used are strong predictors of the antecedent of VP ellipsis. With the United States joining other countries in national efforts to reap the many benefits that use of health information technology can bring for health care quality and savings, sobering reports recall the complexity and difficulties of implementing even smaller-scale systems. Despite best practice research that identified success factors for health information technology projects, a majority, in some sense, still fail. Similar problems plague a variety of different kinds of applications, and have done so for many years. Ten AMIA working groups sponsored a workshop at the AMIA Fall 2006 Symposium. It was entitled “Avoiding The F-Word: IT Project Morbidity, Mortality, and Immortality” and focused on this under-addressed problem. Participants discussed communication, workflow, and quality; the complexity of information technology undertakings; the need to integrate all aspects of projects, work environments, and regulatory and policy requirements; and the difficulty of getting all the parts and participants in harmony. While recognizing that there still are technical issues related to functionality and interoperability, discussion affirmed the emerging consensus that problems are due to sociological, cultural, and financial issues, and hence are more managerial than technical. Participants drew on lessons from experience and research in identifying important issues, action items, and recommendations to address the following: what “success” and “failure” mean, what contributes to making successful or unsuccessful systems, how to use failure as an enhanced learning opportunity for continued improvement, how system successes or failures should be studied, and what AMIA should do to enhance opportunities for successes. The workshop laid out a research agenda and recommended action items, reflecting the conviction that AMIA members and AMIA as an organization can take a leadership role to make projects more practical and likely to succeed in health care settings.