Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
C4.5: programs for machine learning
C4.5: programs for machine learning
IEEE Transactions on Software Engineering - Special issue on software reliability
Case-based reasoning
Selected papers of the sixth annual Oregon workshop on Software metrics
Experimental software engineering: a report on the state of the art
Proceedings of the 17th international conference on Software engineering
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Improved models of software quality
Improved models of software quality
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
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
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
Accuracy of software quality models over multiple releases
Annals of Software Engineering
Empirical Software Engineering
Balancing Misclassification Rates in Classification-TreeModels of Software Quality
Empirical Software Engineering
Data Mining and Knowledge Discovery: Making Sense Out of Data
IEEE Expert: Intelligent Systems and Their Applications
Investigation of Logistic Regression as a Discriminant of Software Quality
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Experience from Replicating Empirical Studies on Prediction Models
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Identification of Green, Yellow and Red Legacy Components
ICSM '98 Proceedings of the International Conference on Software Maintenance
Software Metrics Model For Integrating Quality Control And Prediction
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Evolutionary Neural Networks: A Robust Approach to Software Reliability Problems
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Building Software Quality Classification Trees: Approach, Experimentation, Evaluation
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
ISSRE '02 Proceedings of the 13th 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
Evaluating indirect and direct classification techniques for network intrusion detection
Intelligent Data Analysis
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
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The primary aim of risk-based software quality classification models is to detect, prior to testing or operations, components that are most-likely to be of high-risk. Their practical usage as quality assurance tools is gauged by the prediction-accuracy and cost-effective aspects of the models. Classifying modules into two risk groups is the more commonly practiced trend. Such models assume that all modules predicted as high-risk will be subjected to quality improvements. Due to the always-limited reliability improvement resources and the variability of the quality risk-factor, a more focused classification model may be desired to achieve cost-effective software quality assurance goals. In such cases, calibrating a three-group (high-risk, medium-risk, and low-risk) classification model is more rewarding. We present an innovative method that circumvents the complexities, computational overhead, and difficulties involved in calibrating pure or direct three-group classification models. With the application of the proposed method, practitioners can utilize an existing two-group classification algorithm thrice in order to yield the three risk-based classes. An empirical approach is taken to investigate the effectiveness and validity of the proposed technique. Some commonly used classification techniques are studied to demonstrate the proposed methodology. They include, the C4.5 decision tree algorithm, discriminant analysis, and case-based reasoning. For the first two, we compare the three-group model calibrated using the respective techniques with the one built by applying the proposed method. Any two-group classification technique can be employed by the proposed method, including those that do not provide a direct three-group classification model, e.x., logistic regression and certain binary classification trees, such as CART. Based on a case study of a large-scale industrial software system, it is observed that the proposed method yielded promising results. For a given classification technique, the expected cost of misclassification of the proposed three-group models were significantly better (generally) when compared to the technique驴s direct three-group model. In addition, the proposed method is also evaluated against an alternate indirect three-group classification method.