Abstract
Since the arising of software engineering many efforts have been devoted to improve the software development process. More recently, software quality has received attention from researchers due to the importance that software has gained in supporting all levels of the organizations. New methods, techniques, and tools were created to increase the quality and productivity of the software development process. Approaches based on the practitioners’ experience, for example, or on the analysis of the data generated during the development process, have been adopted. This paper follows the second path by applying data mining procedures to figure out variables from the development process that most affect the software quality. The premise is that the quality of decision making in management of software projects is closely related to information gathered during the development process. A case study is presented in which some regression models were built to explore this idea during the phases of testing, approval, and production. The results can be applied, mainly, to help the development managers in focusing those variables to improve the quality of the software as a final product.
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do Prado, H.A., Bianchi Campos, F., Ferneda, E., Nunes Cornelio, N., Haendchen Filho, A. (2013). Prediction of Software Quality Based on Variables from the Development Process. In: Graña, M., Toro, C., Howlett, R.J., Jain, L.C. (eds) Knowledge Engineering, Machine Learning and Lattice Computing with Applications. KES 2012. Lecture Notes in Computer Science(), vol 7828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37343-5_8
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DOI: https://doi.org/10.1007/978-3-642-37343-5_8
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