Software Defect Classification: A Comparative Study with Rough Hybrid Approaches

Authors:
Sheela Ramanna;Rajen Bhatt;Piotr Biernot
Affiliations:
Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba R3B 2E9, Canada;Samsung India Software Center, Noida-201305, Uttar Pradesh, India;Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba R3B 2E9, Canada
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

Citing 11
Cited 1

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Elements of Software Science (Operating and programming systems series)

Elements of Software Science (Operating and programming systems series)
Software Engineering: An Engineering Approach

Software Engineering: An Engineering Approach
A Metrics Suite for Object Oriented Design

IEEE Transactions on Software Engineering
Induction of Decision Trees

Machine Learning
Towards a Software Change Classification System: A Rough Set Approach

Software Quality Control
An Application of Fuzzy Clustering to Software Quality Prediction

ASSET '00 Proceedings of the 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSET'00)
On the extension of functional dependency degree from crisp to fuzzy partitions

Pattern Recognition Letters
A Complexity Measure

IEEE Transactions on Software Engineering
A comparative study on heuristic algorithms for generating fuzzydecision trees

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Refinement of generated fuzzy production rules by using a fuzzy neural network

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Software defect prediction based on source code metrics time series

Transactions on rough sets XIII

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is an extension of our earlier work in combining strengths of rough set theory and neuro-fuzzy decision trees in classifying software defect data. The extension includes the application of a rough-fuzzy classification trees to classifying defects. We compare classification results for five methods: rough sets, neuro-fuzzy decision trees, partial decision trees, rough-neuro-fuzzy decision trees and rough-fuzzy classification trees. The analysis of the results include a paired t-test for accuracy and number of rules. The results demonstrate that there is improvement in classification accuracy with the rough fuzzy classification trees with a minimal set of rules. The contribution of this paper is a comparative study of several hybrid approaches in classifying software defect data.