Case-based reasoning
Effective methods for software testing
Effective methods for software testing
Experimental software engineering: a report on the state of the art
Proceedings of the 17th international conference on Software engineering
Automated test data generation for programs with procedures
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
An Experiment to Assess the Cost-Benefits of Code Inspections in Large Scale Software Development
IEEE Transactions on Software Engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
A replicated assessment and comparison of common software cost modeling techniques
Proceedings of the 22nd international conference on Software engineering
Comparing case-based reasoning classifiers for predicting high risk software components
Journal of Systems and Software
Comparing Software Prediction Techniques Using Simulation
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Empirical Software Engineering
Emerald: Software Metrics and Models on the Desktop
IEEE Software
Building a Case-Based Help Desk Application
IEEE Expert: Intelligent Systems and Their Applications
Data Mining and Knowledge Discovery: Making Sense Out of Data
IEEE Expert: Intelligent Systems and Their Applications
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Tree-Based Software Quality Estimation Models For Fault Prediction
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Estimating Software Project Effort by Analogy Based on Linguistic Values
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Predicting Fault-Prone Modules with Case-Based Reasoning
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Modeling software quality: the Software Measurement Analysis and Reliability Toolkit
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Analogy-Based Practical Classification Rules for Software Quality Estimation
Empirical Software Engineering
Application of an Attribute Selection Method to CBR-Based Software Quality Classification
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Software defect prediction using artificial immune recognition system
SE'07 Proceedings of the 25th conference on IASTED International Multi-Conference: Software Engineering
Integrating fuzzy theory and hierarchy concepts to evaluate software quality
Software Quality Control
Risk analysis of software process measurements
Software Quality Control
Information Sciences: an International Journal
Multi-agent Based Personal File Management Using Case Based Reasoning
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A quantitative evaluation of aspect-oriented software quality model (AOSQUAMO)
ACM SIGSOFT Software Engineering Notes
Managing computer files via artificial intelligence approaches
Artificial Intelligence Review
Intelligent project approval cycle for local government: case-based reasoning approach
Proceedings of the 3rd international conference on Theory and practice of electronic governance
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
A bayesian network based approach for software defects prediction
ACM SIGSOFT Software Engineering Notes
Customization support for CBR-based defect prediction
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Applying the Mahalanobis-Taguchi strategy for software defect diagnosis
Automated Software Engineering
The Journal of Supercomputing
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The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored. Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the CBR models have better performance than models based on multiple linear regression.