Peter Guasti

Exploring the behaviour of neural network software quality models

In the paper, we explore the behaviour of neural network software quality models. We select a multiple regression quality model from the principal components of software complexity metrics collected from a large commercial software system. We train two neural networks; one with the complete set of principal components and one with the set of components selected by multiple regression model selection. Comparisons of the three models, for two quality measures gathered from five related software systems, lead to a better understanding of neural network software quality models, and the relationship between software complexity metrics and software quality metrics.

Improving neural network models of defect content in complex software systems

Accurately predicting the number of defects in program modules is a major problem in the quality control of large software development efforts. With good estimates early in the software development cycle, software engineers can take actions to avoid or prepare for emerging quality problems. Some source code measures are closely related to the distribution of defects across program modules. Using these relationships, software engineers develop models that provide early defect content estimates. Work with neural network based models has demonstrated their utility for this purpose. In this paper, we expand upon early neural network results for predicting the number of defects in program modules.