Knowledge discovery from software engineering measurement data

Knowledge discovery from software engineering measurement data is essential in deriving the right conclusions from performed experiments. Different analysis techniques may provide data analysts with different and complementary insights into the studied phenomena. In this paper, two data analysis techniques are compared from both the theoretical and the experimental point of view: Rough Sets and Logistic Regression. We have applied both techniques to the same data set. Results obtained with the two analysis technique are discussed and compared. A hybrid approach is built, by integrating different and supplementing knowledge on the reliability of investigated modules obtained from either approach. This knowledge can be reused in the organizational framework of a company-wide experience factory. The empirical study was performed as part of the ESPRIT/ESSI project CEMP on a real-life maintenance project, the DATATRIEVE project carried out at Digital Engineering Italy.