An empirical study of using rule induction and rough sets to software cost estimation
This paper concerns problems of applying the approach based on rough sets and rule induction to a software engineering data analysis. More precisely, we focus our interest on a software cost estimation problem, which includes predicting the effort required to develop a software system basing on values of cost factors. The case study of analysing the COCOMO data set, containing descriptions of representative historical projects, allows us to discuss how this approach could be used to: identify the most discriminatory cost factors, extract meaningful rule representation of classification knowledge from data, construct accurate rule based classifiers.
rough sets software engineering machine learning preference modeling software cost prediction cocomo model