Dr. Raula Gaikovina Kula（大阪大学 大学院情報科学研究科、特任助教）
Dr. Raula Gaikovina is a member of the Software Engineering Laboratory at the Graduate School of Information Science and Technology, Osaka University since Septemeber 2013, as a Research Assistant Professor (Specially Appointed Assitant Professor). He is currently working under the project `Collecting, Analyzing, and Evaluating Software Assets for Effective Reuse’, Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (SARF). He has several active collaborations with NAIST, University of Victoria, Canada and Vrije Universiteit Brussel, Belgium. In 2013, he graduated with a PhD from NAIST, as a MEXT Monbushokagakusho scholar focusing on Quantitative Micro Software Processes. Raula has interned with Toshiba Japan and has industrial experience in Papua New Guinea, Australia and the United States of America. With interests in software reuse, software clones, peer review and software visualization topics, he has published in software engineer!
ing avenues such as MSR, ICSME, SANER and VISSOFT. He is currently an active member of the international IEEE and ACM, IWESEP in Japan and PNG-IT Cluster and Pacific Islands Chapter of the Internet (PICISOC) Societies.
Modeling and Visualizing Popularity of Software Libraries through Mining Repository Universes.
Widespread usage of library repositories such as the Java Virtual Machine (JVM) Maven Repository and CRAN is a testament to the growing reuse of software in both open source and commercial projects alike. During software maintenance, studies have highlighted risks and perils system maintainers face when deciding to update/replace libraries, especially systems that are composed of complex dependencies. System maintainers need to consider such as compatibility, stability and co-existence with the current system environments. Using `wisdom of the crowd’ approach, we propose an abstract model for intelligent mining of repositories to gather valuable trends of popular or `lessons learned’ by similar systems. In my talk, I will discuss this model, as well the different novel visualizations and interactions to uncover patterns to assist system maintainers make a more informed decision when updating software libraries.