Rick Kazman

Bio

Rick Kazman is the Danny and Elsa Lui Distinguished Professor of Information Technology Management at the University of Hawaii.  His primary research interests are software architecture, design and analysis tools, and technical debt. Kazman has been involved in the creation of several highly influential methods and tools for architecture analysis, including the ATAM (Architecture Tradeoff Analysis Method) and the Titan and DV8 tools.  He is the author of over 250 publications, co-author of three patents and nine books, including Software Architecture in Practice, Technical Debt: How to Find It and Fix It, Designing Software Architectures: A Practical Approach, Evaluating Software Architectures: Methods and Case Studies, and Ultra-Large-Scale Systems: The Software Challenge of the Future.  His methods and tools have been adopted by many Fortune 1000 companies and his work has been cited over 30,000 times, according to Google Scholar. He is currently a member of the IEEE Computer Society’s Board of Governors, a member of the ICSE Steering Committee and chair of the SANER Steering Committee.

Kazman received a B.A. (English/Music) and M.Math (Computer Science) from the University of Waterloo, an M.A. (English) from York University, and a Ph.D. (Computational Linguistics) from Carnegie Mellon University.  How he ever became a software engineering researcher is anybody’s guess.  When not doing architecture things, Kazman may be found cycling, singing acapella music, gardening, playing the piano, or practicing Tae Kwon Do.

Contact: kazman@hawaii.edu

Presentations

Scaling up software architecture analysis

Abstract

This paper will show how architecture design and analysis techniques rest on a small number of foundational principles. We will show how those principles have been instantiated as a core set of techniques. These techniques, combined together, have resulted in several highly successful architecture analysis and design methods. Finally, we will show how these foundations, and the techniques that instantiate them, can be re-combined for new purposes addressing problems of ever-increasing scale, specifically: addressing the highly complex problems of analyzing software-intensive ecosystems.