Collaboration with a large telecommunications equipment manufacturer is providing the basis for credible case studies, where the system studied is being developed by a group of professionals in an industrial environment, and is large enough to be comparable to other industry projects. The data-collection phase acquires historical data on projects where actual software quality is known. A case study builds and evaluates models that could have been developed during the historical project, calculating predictions that could have been made, and evaluating the accuracy of those predictions against the actual software quality. Such studies illustrate the usefulness of a proposed metric in a real-world setting, and provide practical examples of how to create software quality models. This project is an important step toward the long-term goal to develop modeling methods for predicting software quality.
Software metrics are the foundation of such models.
Project for NSF Discovering Software Process Measures Using Genetic Programming Discovering Software Process Measures Using Genetic Programming is a research project that is developing automated methods for finding algorithms that measure novel and useful attributes of software development processes using genetic programming. Dr. Taghi M. Khoshgoftaar is the Principal Investigator, Dr. Matthew P. Evett is co-Principal Investigator, and Dr. Edward B. Allen is Senior Research Associate. High quality software is essential for mission-critical systems. One cost-effective strategy is to target enhancement activities to those modules that are most likely to have faults. When the methods being developed by this project are applied to a software development project, the resulting software process metrics will be novel, specialized for the development organization, and useful for predicting software quality.