Software Projects Success Factors Identification using Data Mining

Software project management is the art and science of planning and leading software projects to achieve predetermined corporate goals. It requires knowledge of the entire software development lifecycle. The project manager's main responsibility is to ensure a successful project outcome. Project success is normally defined as achieving desired project objectives and features within desired cost and schedule. Many factors affect project success including dealing with gathering requirements, customer involvements and project management. Several researchers have investigated the success or failure of software projects using statistical approaches. In this paper, a Web based survey and interviews are used to collect project data, about requirements, project sponsor and customers. Tools such as association, neural networks, clustering, Naive Bayes and decision tree are used to discover common characteristics and rules that govern project success and failure. The results show the power of data mining algorithms to discover the most important factors and associations in project success and failure. Results showed that each mining algorithm has a particular strength to provide knowledge and make predictions about project success opportunities