The current software market favors software development organizations that apply software quality models. Software engineers fit quality models to data collected from past projects. Predictions from these models provide guidance in setting schedules and allocating resources for new and ongoing development projects. To improve model stability and predictive quality, engineers select models from the orthogonal linear combinations produced using principal components analysis. However, recent research revealed that the principal components underlying source code measures are not necessarily stable across software products. Thus, the principal components underlying the product used to fit a regression model can vary from the principal components underlying the product for which we desire predictions. We investigate the impact of this principal components instability on the predictive quality of regression models. To achieve this, we apply an analytical technique for accessing the aptness of a given model to a particular application.
展开▼
