Combining techniques for software quality classification: An integrated decision network approach

Authors:
Nan-Hsing Chiu
Affiliations:
Department of Information Management, Ching Yun University, 229, Chien-Hsin Rd., Jung-Li, Taoyuan, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 21
Cited 0

Bagging predictors

Machine Learning
Theoretical Views of Boosting

EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Investigation of Logistic Regression as a Discriminant of Software Quality

METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Using Public Domain Metrics To Estimate Software Development Effort

METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Combining Software Quality Predictive Models: An Evolutionary Approach

ICSM '02 Proceedings of the International Conference on Software Maintenance (ICSM'02)
Combining techniques to optimize effort predictions in software project management

Journal of Systems and Software
Genetic Programming-Based Decision Trees for Software Quality Classification

ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study

Empirical Software Engineering
Application of neural networks for software quality prediction using object-oriented metrics

Journal of Systems and Software
Reliability and Validity in Comparative Studies of Software Prediction Models

IEEE Transactions on Software Engineering
A Novel Method for Early Software Quality Prediction Based on Support Vector Machine

ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability 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
Regression via Classification applied on software defect estimation

Expert Systems with Applications: An International Journal
Applying Particle Swarm Optimization to Prioritizing Test Cases for Embedded Real Time Software Retesting

CITWORKSHOPS '08 Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops
Hardware Software Partitioning Problem in Embedded System Design Using Particle Swarm Optimization Algorithm

CISIS '08 Proceedings of the 2008 International Conference on Complex, Intelligent and Software Intensive Systems
Using Kaplan-Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patients

Expert Systems with Applications: An International Journal
Accuracy and efficiency comparisons of single- and multi-cycled software classification models

Information and Software Technology
Particle swarm optimization for prototype reduction

Neurocomputing
Applying fuzzy neural network to estimate software development effort

Applied Intelligence
An early software-quality classification based on improved grey relational classifier

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Accurately predicting fault-prone modules is a major problem in quality control of a software system during software development. Selecting an appropriate suggestion from various software quality classification models is a difficult decision for software project managers. In this paper, an integrated decision network is proposed to combine the well-known software quality classification models in providing the summarized suggestion. A particle swarm optimization algorithm is used to search for suitable combinations among the software quality classification models in the integrated decision network. The experimental results show that the proposed integrated decision network outperforms the independent software quality classification models. It also provides an appropriate summary for decision makers.