鷲崎弘宜, “SQuBOK v3にみる不確実なDX時代の確実な品質技術に向けて – AI＆機械学習、アジャイル&DevOps&オープンソース開発を中心に -“, ソフトウェア品質シンポジウム2019, 東京, 2019年9月12日

https://www.slideshare.net/hironoriwashizaki/squbok-v3dx-aidevops

ETロボコン2019 東京地区大会、1日目を多くのご参加を得て盛況に開催中。研究室の4年生チームは、惜しくもパーフェクトは逃しましたが2走とも完走し難所も攻略、良い取り組みでした。

Daisuke Saito, Hironori Washizaki, Yoshiaki Fukazawa, Tetsuya Yoshida, Isamu Kaneko and Hirotaka Kamo, “Learning Effects in Programming Learning Using Python and Raspberry Pi: Case Study with Elementary School Students,” IEEE International Conferene on Engineering, Teaching and Education (TALE 2019), Yogyakarta, Indonesia, 10-13 December 2019 (CORE Rank C)

Kyawt San Kyawt, Hironori Washizaki, Yoshiaki Fukazawa, Kiyoshi Honda, Masahiro Taga and Akira Matsuzaki, “Deep Cross-Project Software Reliability Growth Model,” The 30th IEEE International Symposium on Software Reliability Engineering (ISSRE 2019)(CORE Rank A), Industry Track, Berlin, Germany, Oct 28 – 31, 2019

Previous studies have suggested that software reliability growth models (SRGMs) for cross-project predictions are more practical for ongoing development projects. Several software reliability growth models (SRGMs) have been proposed based on various factors to measure the reliability and are helpful to indicate the number of remaining defects before release. Software industries want to predict the number of bugs and monitor the situation of projects for new or ongoing development projects. However, the available data is limited for projects in the initial development phases. In this situation, applying SRGMs may incorrectly predict the future number of bugs. This paper proposes a new SRGM method using the features of previous projects to predict the number of bugs for ongoing development projects. Through a case study, we identify similar projects for a target project by k-means clustering and form new training datasets. The Recurrent Neural Network based deep long shortterm memory model is built over the obtained new dataset for prediction model. According to experiment results, the prediction by the proposed deep cross-project (DC) SRGM performs better than traditional SRGMs and deep SRGM for ongoing projects.

小学生が身近な話題のクイズやゲーム作りを通し、分解・一般化・抽象化・モデル化・シミュレーション・論理的推論（演繹、帰納、仮説形成）・アルゴリズムというプログラミング的思考を分かりやすく楽しく学ぶ書籍です。小学校の先生や保護者、教育関係者の方々にも、プログラミング的思考を教育するうえでお手に取っていただければ幸いです。ぜひご覧ください！

鷲崎　弘宜 (著), 齋藤　大輔 (著), 坂本　一憲 (著), Scratchでたのしく学ぶプログラミング的思考, マイナビ出版, 2019/9/24

Atsuo Hazeyama, Hikaru Miyahara, Takafumi Tanaka, Hironori Washizaki, Haruhiko Kaiya, Takao Okubo and Nobukazu Yoshioka, “A System for Seamlessly Supporting from Security Requirements Analysis to Security Design using a Software Security Knowledge Base,” 6th International Workshop on Evolving Security & Privacy Requirements Engineering (ESPRE) at RE 2019, Monday 23rd September 2019, Jeju Island, Korea.

Naoto Wada, Yuki Noyori, Hironori Washizaki, Yoshiaki Fukazawa, Hideyuki Kanuka, Hiroki Ohbayashi, “The Proposal of Model Transformation Support Method Based on Model Editing Operation History,” 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE 2019), Oct 15-18, Osaka, Japan

• 鷲崎 弘宜, 産学合同によるセキュリティパターン研究: 場づくりと今後に向けたチャレンジ, SIGSE 産学連携フォーラム, 東京・品川, 2019年7月1日
• 鷲崎弘宜, SQuaRE に基づくソフトウェア品質評価枠組みと品質実態調査, SIGSE 産学連携フォーラム, 東京・品川, 2019年7月1日

8/29 ソフトウェアエンジニアリングシンポジウム SES2019 にてワークショップ「ビジネスと社会のためのソフトウェア工学に向けて」小林さん・谷口さんとご一緒に設置 SE4BS Prj成果をご紹介しつつDX時代のソフトウェア工学を考える参加型の機会ですのでぜひご参加ください。
2ページ以内のポジションペーパー投稿の場合は8/2締め切り、投稿なしの場合は8/9申し込み締め切りです。

ワークショップ

Joined #SNPD2019 and #AAI2019 #EAIS2019 in Toyama to present our achievements on Model checking, and organise special session on GQM+Strategies with Shintani-san. Shuji presented paper on linking GQM+Strategies and SysML.