Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques
Empirical Software Engineering
Analogy-Based Practical Classification Rules for Software Quality Estimation
Empirical Software Engineering
Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study
Empirical Software Engineering
An empirical study of predicting software faults with case-based reasoning
Software Quality Control
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Customization support for CBR-based defect prediction
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Software defect prediction using Bayesian networks
Empirical Software Engineering
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Abstract: The paper presents the Software Measurement Analysis and Reliability Toolkit (SMART) which is a research tool for software quality modeling using case based reasoning (CBR) and other modeling techniques. Modern software systems must have high reliability. Software quality models are tools for guiding reliability enhancement activities to high risk modules for maximum effectiveness and efficiency. A software quality model predicts a quality factor, such as the number of faults in a module, early in the life cycle in time for effective action. Software product and process metrics can be the basis for such fault predictions. Moreover, classification models can identify fault prone modules. CBR is an attractive modeling method based on automated reasoning processes. However, to our knowledge, few CBR systems for software quality modeling have been developed. SMART addresses this area. There are currently three types of models supported by SMART: classification based on CBR, CBR classification extended with cluster analysis, and module-order models, which predict the rank-order of modules according to a quality factor. An empirical case study of a military command, control, and communications applied SMART at the end of coding. The models built by SMART had a level of accuracy that could be very useful to software developers.