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This paper demonstrates the use of Data Mining (DM) techniques in exploratory research. A robust model for identifying the factors that explain the success of Open Source Software (OSS) projects is created, validated and tested. The predictive modeling techniques of Logistic Regression (LR), Decision Trees (DT) and Neural Networks (NN) are used together in(More)
In this paper, we define and validate a new multidimensional measure of Open Source Software (OSS) project survivability, called Project Viability. Project viability has three dimensions: vigor, resilience, and organization. We define each of these dimensions and formulate an index called the Viability Index (VI) to combine all three dimensions. Archival(More)
As the use of Open Source Software (OSS) systems increases in the corporate environment, it is important to examine the maintenance process of these projects. OSS projects allow end users to directly submit reports in case of any operational issues. Timely resolution of these defect reports requires effective management of maintenance resources. This study(More)
This paper develops tests and validates a model for the antecedents of open source software (OSS) defects, using Data and Text Mining. The public archives of OSS projects are used to access historical data on over 5,000 active and mature OSS projects. Using domain knowledge and exploratory analysis, a wide range of variables is identified from the process,(More)
Open Source Software (OSS) development and use has increased significantly over recent years. Therefore, there is a need to analyze and understand these projects. Software quality is an important characteristic effecting overall system lifecycle cost, performance and useful life. The existing models for software quality are based on empirical analysis of(More)
Healthcare information systems collect massive amounts of textual and numeric information about patients, visits, prescriptions, physician notes and more. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. However,(More)