Barrett R. Bryant received his B. S. in computer science from the University of Arkansas at Little Rock in 1979 and his M. S. and Ph. D. in computer science from Northwestern University in 1980 and 1983, respectively. From 1983-2011, he was on the faculty of the University of Alabama at Birmingham. He has also held visiting appointments at a number of institutions, including Ibaraki University, Hitachi, Japan, the Naval Postgraduate School, Monterey, California, USA, and Tsinghua University, Beijing, China. He serves on the Steering Committee of SAC (ACM Symposium on Applied Computing), and is a member of EAPLS, and a senior member of ACM and IEEE. Further details are available at http://www.cse.unt.edu/~bryant.
Grammar inference (GI) is the process of learning a grammar from examples, either positive (i.e., the pattern should be recognized by the grammar) and/or negative (i.e., the pattern should not be recognized by the grammar). Domain-specific languages are small languages often designed by experts from a particular domain (e.g., automotive, aeronautical, etc.), instead of language engineers. Domain experts may not be able to implement their language but can write examples of the language they want to develop. Related to domain-specific languages is the notion of domain-specific models for software systems in a particular application domain. This talk will focus on two novel applications of grammar inference to software engineering, namely inference of domain-specific language (DSL) specifications from example DSL programs and recovery of meta models (i.e., model specifications) from software model instances which have evolved independently of the original meta model.