A tool for discriminant analysis and classification of software metrics
Information and Software Technology
Rigorous definition and analysis of program complexity measures: an example using nesting
Journal of Systems and Software
The three Rs of software automation: re-engineering, repository, reusability
The three Rs of software automation: re-engineering, repository, reusability
The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
Software quality classification model based on McCabe's complexity measure
Journal of Systems and Software - Special issue on achieving quality in software
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
Complexity Measure Evaluation and Selection
IEEE Transactions on Software Engineering
The effect of interface complexity on program error density
ICSM '96 Proceedings of the 1996 International Conference on Software Maintenance
IEEE Transactions on Software Engineering
Software Structure Metrics Based on Information Flow
IEEE Transactions on Software Engineering
Evaluating the applicability of reliability prediction models between different software
Proceedings of the International Workshop on Principles of Software Evolution
Controlling Overfitting in Classification-Tree Models ofSoftware Quality
Empirical Software Engineering
Uncertain Classification of Fault-Prone Software Modules
Empirical Software Engineering
Balancing Misclassification Rates in Classification-TreeModels of Software Quality
Empirical Software Engineering
Data Mining of Software Development Databases
Software Quality Control
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques
Empirical Software Engineering
Entropies as Measures of Software Information
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Classification Tree Models of Software Quality Over Multiple Releases
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
Improving Tree-Based Models of Software Quality with Principal Components Analysis
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
Empirical Software Engineering
Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study
Empirical Software Engineering
Anomaly-based fault detection in pervasive computing system
Proceedings of the 5th international conference on Pervasive services
The Journal of Supercomputing
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A methodology for constructing an optimum software quality classification tree using software complexity metrics to discriminate between high-quality modules and low-quality modules is proposed. The process of tree generation is an application of the AIC (Akaike Information Criterion) procedures to the binomial distribution. AIC procedures are based on maximum likelihood estimation and the least number of complexity metrics. It is an improvement of the software quality classification tree generation method proposed by Porter and Selby from the viewpoint the complexity metrics are minimized. The problems of their method are that the software quality prediction model is unstable because it reflects observational errors in real data too much and there is no objective criterion for determining whether the discrimination is appropriate or not at a deep nesting level of the classification tree when the number of sample modules gets smaller. To solve these problems a new metric is introduced and its validity is theoretically and experimentally verified. In our examples, complexity metrics written in C language such as lines of source code, Halstead's software science, McCabe's cyclomatic number, Henry and Kafura's fan-in/out, Howatt and Baker's s cope number and reuse-ratio, are investigated. Our experiments with a medium-sized piece of software (85 thousand lines of source code: 562 samples) show that the software quality classification tree generated by our new metric identifies the target class of the observed modules more efficiently using the minimum number of complexity metrics without any decrease of the correct classification ratio (76%-72%) than the conventional classification tree.