学生のTRAN, Hoa Nhatさんとセキュリティ・ネットワーク領域の青木教授らがAPSEC 2017でbest paper awardを受賞
APSEC (Asia-Pacific Software Engineering Conference)は、ソフトウェア工学に関する国際会議で、今回で24回目の開催となります。今年は、中国の南京で開催され、世界各国(43カ国)から188件の論文の投稿があり、それらの中から51件が採録されました。論文賞に関しては、査読におけるスコアの上位10件の論文を対象に、プログラム委員による投票が実施され、5件に絞られました。そして、会議における発表を踏まえて、3件に賞が授与されました。
Domain-Specific Language Facilitates Scheduling in Model Checking
Nhat-Hoa Tran、Yuki Chiba 、青木 利晃
A concurrent system consists of multiple processes that are run simultaneously. The execution orders of these processes are defined by a scheduler. In model checking techniques, the scheduling policy is closely related to a search algorithm that explores all of system states. To ensure the correctness of the system, the scheduling policy needs to be taken into account during the verification. Current approaches, which use fixed strategies, are only capable of limited kinds of policies and are difficult to extend to handle the variations of the schedulers. To address these problems, we propose a method using a domain-specific language (DSL) for the succinct specification of different scheduling policies. Necessary artifacts are automatically generated from the specification of the policy to analyze the system. We also propose a search algorithm for exploring the system states. Based on this method, we develop a tool to verify the system with different scheduling policies. Our experiments show that we could serve the variations of the schedulers easily and verify systems accurately.
I am very honored and grateful to be selected as a recipient of the APSEC Best Paper Award. First of all, I would like to express my greatest gratitude to my supervisor, Professor Toshiaki Aoki, who advised me during my research. His support creates a great environment to encourage me both in studying and in social life. I am also grateful to Dr. Yuki Chiba and all lab members for their comments and discussions. Last but not least, I would like to thank my family members for their infinite love, which is the biggest encouragement for my study.