The State of Software Maintenance
IEEE Transactions on Software Engineering
The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
Practical Software Maintenance: Best Practices for Managing Your Software Investment
Practical Software Maintenance: Best Practices for Managing Your Software Investment
Computational Intelligence in Software Engineering
Computational Intelligence in Software Engineering
An Experimental Comparison of the Maintainability of Object-Oriented and Structured Design Documents
ICSM '97 Proceedings of the International Conference on Software Maintenance
Assessment of Maintainability in Object-Oriented Software
TOOLS '01 Proceedings of the 39th International Conference and Exhibition on Technology of Object-Oriented Languages and Systems (TOOLS39)
A Methodology for Constructing Maintainability Model of Object-Oriented Design
QSIC '04 Proceedings of the Quality Software, Fourth International Conference
A Change Impact Dependency Measure for Predicting the Maintainability of Source Code
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
Sensitivity analysis of fuzzy and neural network models
ACM SIGSOFT Software Engineering Notes
On the Automatic Modularization of Software Systems Using the Bunch Tool
IEEE Transactions on Software Engineering
Use of Genetic Algorithm for Software Maintainability Metrics' Conditioning
ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
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The software maintainability can be ensured by carefully control of its software development process. An early measurement of maintainability starting from design phase is always desirable to produce maintainable software. Some of the researchers have tried to use soft computing techniques to measure maintainability. In spite of their reported validations, these models are not calibrated and no attention has been paid to evaluate and improve the stability of these methods. An attempt has been made in this paper to evaluate and compare several methodologies for improving the numerical stability of a fuzzy logic based maintainability metrics system. Tuning of fuzzy system parameters is carried out using genetic algorithm with system condition number as objective function for optimization. A number of alternates are considered, in which training data sets are generated using different methods and these sets are used to evaluate objective functions in GA and accordingly fuzzy parameters are tuned. In order to show the advantage of such stability improvement, real projects' maintainability data is used and our study indicates that fuzzy model performance gets increased after conditioning.