Dynamic Coupling Measurement for Object-Oriented Software
IEEE Transactions on Software Engineering
Object oriented software quality prediction using general regression neural networks
ACM SIGSOFT Software Engineering Notes
Object-oriented software fault prediction using neural networks
Information and Software Technology
A Fault Prediction Model with Limited Fault Data to Improve Test Process
PROFES '08 Proceedings of the 9th international conference on Product-Focused Software Process Improvement
Estimating software readiness using predictive models
Information Sciences: an International Journal
Information Sciences: an International Journal
Review: A systematic review of software fault prediction studies
Expert Systems with Applications: An International Journal
A cohesion metric proposal for object-oriented systems: COMIAS
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
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ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Investigating fault prediction capabilities of five prediction models for software quality
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Risk chain prediction metrics for predicting fault proneness in object oriented systems
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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ACM SIGSOFT Software Engineering Notes
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This paper presents the application of neural networksin software quality estimation using object-orientedmetrics. Quality estimation includes estimating reliabilityas well as maintainability of a software. Reliability istypically measured as the number of defects.Maintenance effort can be measured as the number oflines changed per class. In this paper, two kinds ofinvestigation are performed. The first on predicting thenumber of defects in a class and the second on predictingthe number of lines change per class. Two neuralnetwork models are used, they are Ward neural networkand General Regression neural network (GRNN). Object-orienteddesign metrics concerning inheritance relatedmeasures, complexity measures, cohesion measures,coupling measures and memory allocation measures areused as the independent variables. GRNN network modelis found to predict more accurately than Ward networkmodel.