An empirical validation of software cost estimation models
Communications of the ACM
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
C4.5: programs for machine learning
C4.5: programs for machine learning
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Journal of Systems and Software - Special issue on empirical studies of software development and evolution
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Software Engineering Economics
Software Engineering Economics
Machine Learning
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Machine Learning and Data Mining; Methods and Applications
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Importance and Interaction of Conditions in Decision Rules
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Rough Neural Network for Software Change Prediction
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Towards a Software Change Classification System: A Rough Set Approach
Software Quality Control
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Data mining tasks and methods: Classification: multicriteria classification
Handbook of data mining and knowledge discovery
Rough Set Based Data Analysis in Goal Oriented Software Measurement
METRICS '96 Proceedings of the 3rd International Symposium on Software Metrics: From Measurement to Empirical Results
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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.