Abstract
Independently from the nature of a project, process management variables like cost, quality, schedule, and scope are critical decision factors for a good and successful execution of a project. In software engineering, project planning and execution are highly influenced by the creative nature of all the individuals involved with the project. Thus, managing the risks of different project stages is a key task with extreme importance for project managers (and sponsors) that should be focused on control and monitoring effectively the referred variables, as well as all the others concerned with their context. In this work, we used a small “cocktail” of data mining techniques and methods to explore potential correlations and influences contained in some of the most relevant parameters related to experience, complexity, organization maturity and project innovation in Software Engineering, developing in a model that could be deployed in any project management process, assisting project managers in planning and monitoring the state of one project (or program) under its supervision.
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Santos, J., Belo, O. (2013). Estimating Risk Management in Software Engineering Projects. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2013. Lecture Notes in Computer Science(), vol 7987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39736-3_7
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DOI: https://doi.org/10.1007/978-3-642-39736-3_7
Publisher Name: Springer, Berlin, Heidelberg
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