The goal of the research is to enable software engineers to find software development best practices from past empirical data. The increasing availability of software development project data, plus new machine learning techniques, make it possible for researchers to study the generalizability of results across projects using the concept of transfer learning. Using data from real software projects, the project will determine and validate best practices in three areas: predicting software development effort; isolating software detects; effective code inspection practices.
This research will deliver new data mining technologies in the form of transfer learning techniques and tools that overcome current limitations in the state-of-the-art to provide accurate learning within and across projects. It will design new empirical studies, which apply transfer learning to empirical data collected from industrial software projects. It will build an on-line model analysis service, making the techniques and tools available to other researchers who are investigating validity of principles for best practice.
The broader impacts of the research will be to make empirical software engineering research results more transferable to practice, and to improve the research processes for the empirical software engineering community. By providing a means to test principles about software development, this work stands to transform empirical software engineering research and enable software managers to rely on scientifically obtained facts and conclusions rather than anecdotal evidence and one-off studies. Given the immense importance and cost of software in commercial and critical systems, the research has long-term economic impacts.
