Learning from Examples: Generation and Evaluation of Decision Trees for Software Resource Analysis
IEEE Transactions on Software Engineering - Special Issue on Artificial Intelligence in Software Applications
The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Selected papers of the sixth annual Oregon workshop on Software metrics
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Accuracy of software quality models over multiple releases
Annals of Software Engineering
Emerald: Software Metrics and Models on the Desktop
IEEE Software
Detection of Fault-Prone Software Modules During a Spiral Life Cycle
ICSM '96 Proceedings of the 1996 International Conference on Software Maintenance
Determining Fault Insertion Rates for Evolving Software Systems
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Application of multivariate analysis for software fault prediction
Software Quality Control
Proceedings of the 28th international conference on Software engineering
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Software developers are very interested in targeting software enhancement activities before release, so that rework of faulty modules can be avoided. Credible predictions of which modules are likely to have faults discovered by customers can be the basis for selecting modules for enhancement. Many case studies in the literature build models to predict which modules will be fault-prone without regard to subsystems defined by the system's functional architecture. Our hypothesis is this: models that are specially built for subsystems will be more accurate than a system-wide model applied to each subsystem's modules. In other words, the subsystem that a module belongs to can be valuable information in software quality modeling.This paper presents an empirical case study, which compared software quality models of an entire system to models of a major functional subsystem. The study modeled a very large telecommunications system with classification trees built by the Classification And Regression Trees algorithm (CART). For predicting subsystem quality, we found that a model built with training data on the subsystem alone was more accurate than a similar model built with training data on the entire system. We concluded that characteristics of the subsystem's modules were not similar to those of the system as a whole, and thus, information on subsystems can be valuable.