![]() |
Empirical Modeling:Optimized Set Reduction |
Problem |
In order to plan, control, and evaluate the software development process,
one needs to collect and analyze historical data from similar projects.
Classical techniques for data analysis have limitation when used on software
engineering data due to the following inherent constraints:
|
Goal |
Based on the specific constraints of software engineering, design alternatives to classical techniques for data analysis in order to build more interpretable, more accurate, easier to use empirical models. |
Keywords |
Data analysis, classification, prediction, empirical modeling, machine learning, stochastic modeling, OSR, quality evaluation |
Participants |
Lionel Briand, Bill Thomas, Chris Hetmanski, Victor R. Basili |
References |
![]() L. Briand , W. Thomas , C. Hetmanski. In Proc. of the 15th Int'l Conf. on Software Engineering, pp. 55-65, Baltimore, May 1993. ![]() L. Briand , V. Basili, and C. Hetmanski IEEE Transactions on Software Engineering, 19(11):1028-1044, November 1993. ![]() L. Briand , V. Basili, and W. Thomas . IEEE Transactions on Software Engineering, 18(11):931-942, November 1992. |