A comparative study of predictive models for program changes during system testing and maintenance

By modeling the relationship between software complexity attributes and software quality attributes, software engineers can take actions early in the development cycle to control the cost of the maintenance phase. The effectiveness of these model-based actions depends heavily on the predictive quality of the model. An enhanced modeling methodology that shows significant improvements in the predictive quality of regression models developed to predict software changes during maintenance is applied here. The methodology reduces software complexity data to domain metrics by applying principal components analysis. It then isolates clusters of similar program modules by applying cluster analysis to these derived domain metrics. Finally, the methodology develops individual regression models for each cluster. These within-cluster models have better predictive quality than a general model fitted to all of the observations.<>