Handbook of software reliability engineering
Handbook of software reliability engineering
Analogy-Based Practical Classification Rules for Software Quality Estimation
Empirical Software Engineering
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
Empirical Software Engineering
Reliability and Validity in Comparative Studies of Software Prediction Models
IEEE Transactions on Software Engineering
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
The adjusted analogy-based software effort estimation based on similarity distances
Journal of Systems and Software
Expert Systems with Applications: An International Journal
The landscape adaptive particle swarm optimizer
Applied Soft Computing
Attribute Selection in Software Engineering Datasets for Detecting Fault Modules
EUROMICRO '07 Proceedings of the 33rd EUROMICRO Conference on Software Engineering and Advanced Applications
Expert Systems with Applications: An International Journal
A new particle swarm optimization for the open shop scheduling problem
Computers and Operations Research
Combining techniques for software quality classification: An integrated decision network approach
Expert Systems with Applications: An International Journal
Application of grey system theory in telecare
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The inherent uncertainty and incomplete information of the software development process presents particular challenges for identifying fault-prone modules and providing a preferred model early enough in a development cycle in order to guide software enhancement efforts effectively. Grey relational analysis (GRA) of grey system theory is a well known approach that is utilized for generalizing estimates under small sample and uncertain conditions. This paper examines the potential benefits for providing an early software-quality classification based on improved grey relational classifier. The particle swarm optimization (PSO) approach is adopted to explore the best fit of weights on software metrics in the GRA approach for deriving a classifier with preferred balance of misclassification rates. We have demonstrated our approach by using the data from the medical information system dataset. Empirical results show that the proposed approach provides a preferred balance of misclassification rates than the grey relational classifiers without using PSO. It also outperforms the widely used classifiers of classification and regression trees (CART) and C4.5 approaches.