KINDAI UNIVERSITY


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TSUNODA Masateru

Profile

FacultyDepartment of Informatics / Graduate School of Science and Engineering Research
PositionAssociate Professor
Degree
Commentator Guidehttps://www.kindai.ac.jp/meikan/469-tsunoda-masateru.html
URL
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Last Updated :2020/04/03

Research Activities

Research Areas

  • Informatics, Software

Published Papers

  • Probing Software Engineering Beliefs about System Testing Defects: Analyzing Data for Future Directions, Akito Monden, Masateru Tsunoda, Mike Barker, Kenichi Matsumoto, IEEE IT Professional, IEEE IT Professional, 19(2), 58 - 64, Apr. 2017 , Refereed
  • Relationship between Code Reading Speed and Programmers' Age, Yukasa Murakami, Masateru Tsunoda, Masahide Nakamura, International Workshop on Empirical Software Engineering in Practice (IWESEP 2016), International Workshop on Empirical Software Engineering in Practice (IWESEP 2016), Poster Session - Poster Session, Mar. 2016 , Refereed
  • Using Propensity Score for Empirical Analysis of Software Development, 角田 雅照, 天嵜 聡介, ソフトウェアエンジニアリングシンポジウム論文集, ソフトウェアエンジニアリングシンポジウム論文集, 2014, 202 - 203, Aug. 2014 , Refereed
  • Software Development Productivity of Japanese Enterprise Applications, Masateru Tsunoda, Akito Monden, Hiroshi Yadohisa, Nahomi Kikuchi, Ken-ichi Matsumoto, Information Technology and Management, Vol.10, No.4, pp.193-205, December 2009. [FILE], Information Technology and Management, Vol.10, No.4, pp.193-205, December 2009. [FILE], 10(4), 193 - 205, Jan. 2009 , Refereed

Conference Activities & Talks

  • Analyzing the Decision Criteria of Software Developers Based on Prospect Theory, Kanako Kina, Masateru Tsunoda, Hideaki Hata, Haruaki Tamada, Hiroshi Igaki, International Conference on Software Analysis, Evolution, and Reengineering (SANER 2016),   2016 03
  • Revisiting Software Development Effort Estimation Based on Early Phase Development Activities, Masateru Tsunoda, Koji Toda, Kyohei Fushida, Yasutaka Kamei, Meiyappan Nagappan, Naoyasu Ubayashi, Working Conference on Mining Software Repositories (MSR 2013),   2013 05
  • How to Treat Timing Information for Software Effort Estimation?, Masateru Tsunoda, Sousuke Amasaki, Chris Lokan, International Conference on Software and Systems Process (ICSSP 2013),,   2013 05

Misc

  • ソフトウェア開発者の年齢がプログラム理解速度に及ぼす影響の分析, Yukasa Murakami, Masateru Tsunoda, Masahide Nakamura, 情報処理学会研究報告, ソフトウェア工学研究会, 2016-SE-191, 1, 1, 6,   2016 03 , http://www27.cs.kobe-u.ac.jp/achieve/data/pdf/
  • 時空間情報と動作を組み合わせた認証方法, Masateru Tsunoda, Kyohei Fushida, Kohei Mitsui, Yasutaka Kamei, Masahide Nakamura, Keita Gotoh, Kenichi Matsumoto, 情報処理学会研究報告, 数理モデル化と問題解決研究会, 2010-MPS-77, 27, 1, 6,   2010 03 , http://www27.cs.kobe-u.ac.jp/achieve/data/pdf/1091.pdf
  • 位置と速度を利用した移動体向け認証方式の提案, Masateru TSUNODA, Kyohei FUSHIDA, Kohei MITSUI, Yasutaka KAMEI, Keita GOTO, Masahide NAKAMURA, Ken-ichi MATSUMOTO, 電子情報通信学会技術報告, モバイルマルチメディア通信研究専門委員会, MoMuC2006-55, 11, 16,   2006 11 , http://www27.cs.kobe-u.ac.jp/achieve/data/pdf/178.pdf
  • A Recommendation Method of Useful Software Components for Ongoing Project, KAMEI Yasutaka, TSUNODA Masateru, KAKIMOTO Takeshi, OHSUGI Naoki, MONDEN Akito, MATSUMOTO Ken-ichi, Technical report of IEICE. SS, 106, 16, 25, 30,   2006 04 , http://ci.nii.ac.jp/naid/110004718949
    Summary:Many software components have been provided by development platform vendors, for achieving efficient development of high quality software; however, some practitioners cannot find useful components because number of the provided components is too large. For solving this problem, we propose a method based on collaborative filtering for recommending useful components to each ongoing project. In the proposed method, at first, some past projects similar to given ongoing project are retrieved by calculating similarity with number of common components used in the ongoing project and each past proj...
  • Software Technology Recommendation Based on Collaborative Filtering, AKINAGA Tomohiro, OHSUGI Naoki, KAKIMOTO Takeshi, TSUNODA Masateru, MONDEN Akito, MATSUMOTO Kenichi, Technical report of IEICE. SS, 105, 128, 7, 13,   2005 06 16 , http://ci.nii.ac.jp/naid/10016575649
    Summary:In recent years, much software development technology is proposed. It is difficult for the software engineer to be master of all these technologies. So it is necessary to select the technology that should acquire it beforehand. Then, we propose a system recommending the software exploitation technology that seems to be useful for engineer by using Collaborative Filtering. In the proposal method, first of all, the interest to each development technology is first investigated to each engineer. And, the engineer to whom the tendency to the interest is similar is discovered based on cooperated ...
  • A Java Class File Recommender System Based on Collaborative Filtering, KAKIMOTO Takeshi, TSUNODA Masateru, OHSUGI Naoki, MONDEN Akito, MATSUMOTO Ken'ichi, IEICE technical report. Dependable computing, 104, 346, 29, 34,   2004 10 , http://ci.nii.ac.jp/naid/110003204298
    Summary:Today, most software development platforms provide various software components. However, some software developers are not aware of useful components because extremely large amount of components are provided. This paper propose a system recommending the developers some Java class files by using Collaborative Filtering. Once a developer venters a Java class file which has been developed in ongoing project, the proposed system investigates used Java classes in the entered class file. Next, the system finds some similar class files from already completed class files which made in the past proje...

Awards & Honors

  •   2012 10 , International Workshop on Empirical Software Engineering in Practice (IWESEP 2012), Best Paper Award

Research Grants & Projects

  • Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Evaluation framework considering users for software development support methods
  • Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Exploratory Research, Furinkazan: Understanding Behaviors in Software Development based on Game Theoretical Modeling and Empirical Studies, This study aims to understand the various characteristics of software developers and their behavioral patterns. We have addressed this challenge from the following four aspects. 1) A survey study based on behavioral economics to clarify the characteristics of developers for risk management. 2) Data mining on open source software projects to identify patterns of developers’ behaviors. 3) Data mining on closed software development especially to find the patterns of novice developers’ behaviors. 4) Game theoretical modeling and analysis for actual data.
  • Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Planning support for efficient software maintenance and operation, The goal of the research is to support making the plan of software maintenance and system operation. To support that, we clarified the followings, (1) benchmark of software maintenance based on working time, (2) benchmark of information system operation based on working time and unit cost. Also, to support building prediction models of maintenance efficiency and operation efficiency, we clarified the followings, (1) influence of outliers on software development project prediction, (2) advantages of Tobit model on software development project prediction. Using the benchmarks and analysis results, we can make appropriate maintenance plan and operation plan, and it will enhance efficiency of software maintenance and system operation.
  • Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B), Software Development Project Prediction Framework, To achieve high accurate prediction in a software development project, software development project prediction framework and its element technologies was studied. The framework consists of(1) peculiar data point(outlier) deletion,(2) stratification,(3) selecting appropriate variable for prediction, and(4) selecting appropriate prediction model for a dataset, and each element technology is applied in the numerical order. In the research period, each technology was invented and the effects were examined.