Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Software metrics for object-oriented systems
CSC '92 Proceedings of the 1992 ACM annual conference on Communications
C4.5: programs for machine learning
C4.5: programs for machine learning
Object-oriented metrics that predict maintainability
Journal of Systems and Software - Special issue on object-oriented software
Object-oriented software metrics: a practical guide
Object-oriented software metrics: a practical guide
Characterizing and modeling the cost of rework in a library of reusable software components
ICSE '97 Proceedings of the 19th international conference on Software engineering
An investigation into coupling measures for C++
ICSE '97 Proceedings of the 19th international conference on Software engineering
Exploring the relationship between design measures and software quality in object-oriented systems
Journal of Systems and Software
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Using Classification Trees for Software Quality Models: Lessons Learned
HASE '98 The 3rd IEEE International Symposium on High-Assurance Systems Engineering
Machine Learning Method for Software Quality Model Building
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Reusability Hypothesis Verification using Machine Learning Techniques: A Case Study
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Investigation of Metrics for Object-Oriented Design Logical Stability
CSMR '03 Proceedings of the Seventh European Conference on Software Maintenance and Reengineering
Information and Software Technology
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The stability of a class in object-oriented system is one software quality characteristic that is important to assess at the early development stages. However, a direct measure of this software quality characteristic is not possible. Nonetheless, it can be predicted based on other measurable software attributes such as cohesion, coupling, and complexity. Many metrics have been proposed to assess these software attributes and for this purpose, prediction models have been widely used. However, in almost all cases, these models were not efficient when used to predict the quality characteristics (stability or other) of new unseen software as their prediction accuracy decreases significantly. In this paper, we present a heuristic approach that relies on the adaptation and recombination of already built predictive models to new unseen software.The predictive models are all rule-based models and the approach is tested on the stability of classes in an object-oriented software system. We compare our results to the machine learning algorithm C4.5, and we show that our approach out-beats it.