カテゴリー別アーカイブ: 未分類

鷲崎教授がNEDO TSC Foresightフォーラムにて「複雑なIoTソフトウェアを効率よく開発運用保守するために必要なトレーサビリの確保に向けて」と題し招待講演


鷲崎弘宜, “複雑なIoTソフトウェアを効率よく開発運用保守するために必要なトレーサビリの確保に向けて”, NEDO TSC Foresight フォーラム, 東京, 招待講演, 2017年7月14日

Prof. Washizaki gave a talk titled “Combinations of Personal Characteristic Types and Learning Effectiveness of Teams” at COMPSAC 2017.


Hironori Washizaki, Yusuke Sunaga, Masashi Shuto, Katsuhiko Kakehi, Yoshiaki Fukazawa, Shoso Yamato, Masashi Okubo, Bastian Tenbergen, “Combinations of Personal Characteristic Types and Learning Effectiveness of Teams,” 41st IEEE Computer Society Signature Conference on Computers, Software, and Applications (COMPSAC 2017), J1/C2 – Journal First, Conference Second Scheme, Torino, Turin, Italy, July 4-8, 2017.

鷲崎教授がSSR平成28年度成果報告会にて「クラウドを含む複雑なネットワークシステムのためのパターンを中心としたセキュリティ&プライバシ知識の扱い」報告


鷲崎弘宜, “クラウドを含む複雑なネットワークシステムのためのパターンを中心としたセキュリティ&プライバシ知識の扱い”, SSR平成28年度成果報告会 , 東京・国立情報学研究所, 2017年6月27日

Comparison of Text-Based and Visual-Based Programming Input Methods for First-Time Learners, accepted at Journal of Information Technology Education: Research (ESCI Indexed)


Daisuke Saito, Hironori Washizaki, Yoshiaki Fukazawa, “Comparison of Text-Based and Visual-Based Programming Input Methods for First-Time Learners,” Journal of Information Technology Education: Research, 2017. (ESCI Indexed)(to appear)

Aim/Purpose: When learning to program, both text-based and visual-based input methods are common. However, it is unclear which method is more appropriate for first-time learners (first learners).

Background: The differences in the learning effect between text-based and visual-based input methods for first learners are compared the using a questionnaire and problems to assess first learners’ understanding of programming. In addition, we study the benefits and feasibility of both methods.

Methodology: In this research, we used the sandbox game Minecraft and the extended function ComputerCraftEdu (CCEdu). CCEdu provides a Lua programming environments for the two (text and visual) methods inside Minecraft. We conducted a lecture course on both methods for first learners in Japan ranging in age from 6 to about 15 years old. The lecture taught the basics and concepts of programming. Furthermore, we implemented a questionnaire about the attitude of programming before and after the lecture.

Contribution: This research is more than a comparison between the visual method and the text method. It compares visual input and text input methods in the same environment. It clearly shows the difference between the programming learning effects of visual input and text input for first learners. In addition, it shows the more suitable input method for introductory education of first learners in programming learning.

Findings: The following results are revealed: (1) The visual input method induces a larger change in attitude toward programming. (2) The number of operations and input quantity influence both groups. (3) The overall results suggest that a visual input is advantageous in a programming implementation environment for first learners.

Impact on Society: A visual input method is better suited for first learners as it improves the attitude toward programming.

Future Research: In the future, we plan to collect and analyze additional data as well as elucidate the correlation between attitudes and understanding of programming.

Prof. Washizaki gave introduction on Status and Contributions from Asia at IEEE CS Professional Educational Activities Board meeting


Hironori Washizaki, “Status and Possible Contributions: Asia Region,” IEEE Computer Society Professional Educational Activities Board Meeting, 2017 June 14, Phoenix, USA.

Knowledge Description Model for Bodies of Knowledge in Software Engineering Context, accepted at CISTI 2017


Pablo Alejandro Quezada-Sarmiento, Hironori Washizaki, Juan Garbajosa and Liliana Enciso, “Knowledge Description Model for Bodies of Knowledge in Software Engineering Context,” 12th Iberian Conference on Information Systems and Technologies (CISTI 2017), 21 to 24 of June 2017, Lisboa, Portugal.

Bodies of Knowledge (BOK) contains the relevant knowledge for a discipline. BOK must embody the consensus reached by the community for which this BOK will be of application. This consensus is a prerequisite for the adoption of the BOK by the community. In this paper, we utilize a combinations of Software Engineering Body of Knowledge (SWEBOK), models representation, and design science methodology in order to describe the software engineering knowledge context(SEC). SWEBOK serves as backbone taxonomy, while models representation provides a context of representation. In the process of develop of this paper science design methodology was used to provide fundamental knowledge in software engineering (SE).

鷲崎教授が学会誌・情報処理に寄稿「アジリティを追求したソフトウェア開発」


鷲崎弘宜、“9.未来に向かって:アジリティを追求したソフトウェア開発”, 情報処理, Vol.58, No.8, 2017.

ソフトウェア、社会、人々が密接に関わり、不確実性を増しつつある今日、市場や顧客の反応を素早く得て、要求や環境の変化に適応可能な俊敏さ(アジリティ、Agility)がソフトウェア開発に必要である。本稿では、アジリティを追及する「アジャイルソフトウェア開発」(Agile Software Development)について、よくある誤解も含めて様々な捉え方や起源、再定義の動きを紹介する。そのうえで研究との関係として、アジャイル開発に対する研究(Research for Agility)と、研究をアジャイルに進める取組み(Agility for Research)の両面を取り上げて、最後に将来を展望する。

RISE調査研究「ソフトウェア製品品質実態定量化および総合的品質評価枠組みの確立」報告セミナー 6月2日に実施


早稲田大学グローバルソフトウェアエンジニアリング研究所が、一般社団法人コンピュータソフトウェア協会(CSAJ)との協力のもと、独立行政法人情報処理推進機構からのRISE委託研究「測定評価と分析を通じたソフトウェア製品品質の実態定量化および総合的品質評価枠組みの確立」の成果を取りまとめました。その成果公開を記念して、成果を詳しくご紹介するセミナーを6月2日に開催し、100名ほどの多くのご参加をいただき盛況理に終了しました。ご参加ご協力有難うございました。早稲田大学グローバルソフトウェアエンジニアリング研究所では引き続き、CSAJほかと連携してソフトウェア製品品質実態調査を進めてまいりますので、ぜひこの取組みへのご参画をよろしくお願いします。

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Pitfalls and Countermeasures in Software Quality Measurements and Evaluations, accepted at Advances in Computers (Elsevier)


Hironori Washizaki, “Pitfalls and Countermeasures in Software Quality Measurements and Evaluations,” Advances in Computers, Vol. 106, Elsevier, 2017. (DBLP indexded)(to appear)

This chapter discusses common pitfalls and their countermeasures in software quality measurements and evaluations based on research and practical achievements. The pitfalls include negative Hawthorne effects, organization misalignment, uncertain future, and self-certified quality. Corresponding countermeasures include goal-oriented multidimensional measurements, alignment visualization and exhaustive identification of rationales, prediction incorporating uncertainty and machine-learning based measurement improvement, and standard/pattern-based evaluation.