Title :
A pattern recognition approach for software engineering data analysis
Author :
Briand, Lionel C. ; Basili, Victor R. ; Thomas, William M.
Author_Institution :
Maryland Univ., College Park, MD, USA
fDate :
11/1/1992 12:00:00 AM
Abstract :
In order to plan, control, and evaluate the software development process, one needs to collect and analyze data in a meaningful way. Classical techniques for such analysis are not always well suited to software engineering data. A pattern recognition approach for analyzing software engineering data, called optimized set reduction (OSR), that addresses many of the problems associated with the usual approaches is described. Methods are discussed for using the technique for prediction, risk management, and quality evaluation. Experimental results are provided to demonstrate the effectiveness of the technique for the particular application of software cost estimation
Keywords :
pattern recognition; project management; software cost estimation; software quality; optimized set reduction; pattern recognition; prediction; quality evaluation; risk management; software cost estimation; software engineering data analysis; Application software; Costs; Data analysis; Machine learning; Pattern recognition; Predictive models; Productivity; Programming; Risk management; Software engineering;
Journal_Title :
Software Engineering, IEEE Transactions on
