An empirical study of using rule induction and rough sets to software cost estimation
Fundamenta Informaticae - Special issue on theory and applications of soft computing (TASC04)
Approaches to Conflict Dynamics Based on Rough Sets
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Software Defect Classification: A Comparative Study with Rough Hybrid Approaches
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
A Rough-Hybrid Approach to Software Defect Classification
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Software defect prediction based on source code metrics time series
Transactions on rough sets XIII
Conflict analysis and information systems: a rough set approach
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Generalized conflict and resolution model with approximation spaces
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
The rough set exploration system
Transactions on Rough Sets III
Approaches to Conflict Dynamics Based on Rough Sets
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
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The basic contribution of this paper is the presentation of two methods that can be used to design a practical software change classification system based on data mining methods from rough set theory. These methods incorporate recent advances in rough set theory related to coping with the uncertainty in making change decisions either during software development or during post-deployment of a software system. Two well-known software engineering data sets have been used as means of benchmarking the proposed classification methods, and also to facilitate comparison with other published studies on the same data sets. Two technologies in computation intelligence (CI) are used in the design of the software change classification systems described in this paper, namely, rough sets (a granular computing technology) and genetic algorithms. Using 10-fold cross validated paired t-test, this paper also compares the rough set classification learning method with the Waikato Environment for Knowledge Analysis (WEKA) classification learning method. The contribution of this paper is the presentation of two models for software change classification based on two CI technologies.