Fuzzy set approach for automatic tagging in evolving software

Software tagging has been shown to be an efficient, lightweight social computing mechanism to improve different social and technical aspects of software development. Despite the importance of tags, there exists limited support for automatic tagging for software artifacts, especially during the evolutionary process of software development. We conducted an empirical study on IBM Jazz´s repository and found that there are several missing tags in artifacts and more precise tags are desirable. This paper introduces a novel, accurate, automatic tagging recommendation tool that is able to take into account users´ feedbacks on tags, and is very efficient in coping with software evolution. The core technique is an automatic tagging algorithm that is based on fuzzy set theory. Our empirical evaluation on the real-world IBM Jazz project shows the usefulness and accuracy of our approach and tool.