Takada, Shingo

写真a

Affiliation

Faculty of Science and Technology, Department of Information and Computer Science (Yagami)

Position

Professor

Related Websites

External Links
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Career 【 Display / hide 】

  • 1992.09
    -
    1993.06

    慶應義塾大学（理工学研究科日本IBM寄付講座） ,嘱託助手

  • 1995.04
    -
    1999.03

    奈良先端科学技術大学院大学（情報科学研究科） ,助手

  • 1999.04
    -
    2006.03

    慶應義塾大学（理工学部） ,専任講師

  • 2006.04
    -
    2015.03

    慶應義塾大学（理工学部），助教授（准教授）

  • 2015.04
    -
    Present

    慶應義塾大学（理工学部），教授

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Academic Background 【 Display / hide 】

  • 1990.03

    Keio University, Faculty of Science and Engineering, 電気工学科

    University, Graduated

  • 1992.03

    Keio University, Graduate School, Division of Science and Engineeri, 計算機科学専攻

    Graduate School, Completed, Master's course

  • 1995.03

    Keio University, Graduate School, Division of Science and Engineeri, 計算機科学専攻

    Graduate School, Completed, Doctoral course

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Academic Degrees 【 Display / hide 】

  • 工学　, Keio University, 1995.03

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Research Areas 【 Display / hide 】

  • Informatics / Software (ソフトウエア)

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Research Keywords 【 Display / hide 】

  • Software Engineering

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Books 【 Display / hide 】

  • Coverage-Guided Fairness Testing

    Perez Morales D., Kitamura T., Takada S., Studies in Computational Intelligence, 2021

    　View Summary

    Software testing is a crucial task. Unlike conventional software, AI software that uses decision-making algorithms or classifiers needs to be tested for discrimination or bias. Such bias can cause discrimination towards certain individuals based on their protected attributes, such as race, gender or nationality. It is a major concern to have discrimination as an unintended behavior. Previous work tested for discrimination randomly, which has resulted in variations in the results for each test execution. These varying results indicate that, for each test execution, there is discrimination that is not found. Even though it is nearly impossible to find all discrimination unless we check all possible combinations in the system, it is important to detect as much discrimination as possible. We thus propose Coverage-Guided Fairness Testing (CGFT). CGFT leverages combinatorial testing to generate an evenly-distributed test suite. We evaluated CGFT with two different datasets, creating three models with each. The results show an improvement in the number of unfairness found using CGFT compared to previous work.

  • 情報学基礎

    TAKADA SHINGO, 共立出版, 2013.03

    Scope: 1章，4章，7章

  • グローバル化するITSと国際標準

    TAKADA SHINGO, 森北出版, 2013.01

    Scope: 324-338

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Papers 【 Display / hide 】

  • Semantic Matching Based Test Case Reuse for Android Applications

    Ngoc Bao Nguyen, Shingo Takada

    Eighth International Workshop on Validation, Analysis, and Evolution of Software Tests (VST 2025)    174 - 181 2025.03

    Research paper (international conference proceedings), Joint Work, Last author, Accepted

  • Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling in Review Classification Using LLMs

    Yejian Zhang, Shingo Takada

    2025 11th International Conference on Computing and Artificial Intelligence (ICCAI 2025)  2025.03

    Research paper (international conference proceedings), Joint Work, Last author, Accepted

  • 静的解析ツールが示す優先度は開発者の役に立つのか？

    名倉正剛, 尾原秀登, 高田眞吾, 末次健太郎, 中川岳, 浅原明広

    電子情報通信学会論文誌 J107-D ( 2 ) 77 - 81 2024.02

    Research paper (scientific journal), Accepted

    　View Summary

    コーディング規約違反検出のための静的解析ツールでは，違反に対して警告すべき優先度が設定されていることがある．本研究では，多数のOSSプロジェクトを解析することによって，違反に対する優先度が実際に開発者にとって有益であるかどうかを明らかにする．

  • RSUTT: Robust Search Using T-Way Testing

    Matsukawa C., Takada S.

    Communications in Computer and Information Science 2178 CCIS   35 - 50 2024

    ISSN  18650929

    　View Summary

    Recent years have seen an increase of decision-making software based on Machine Learning (ML). Multiple cases have been reported where such software are discriminatory based on attributes such as race and gender. Thus, ML-based decision making software need to be tested for discrimination, or fairness testing. One popular approach to fairness testing is to find discriminatory data items by first conducting a global search, and then searching locally near the found discriminatory data items. Aequitas, CGFT, and KOSEI are three examples taking this approach. However, there are issues in terms of stability and efficiency. We thus propose an approach called Robust Search Using T-way Testing (RSUTT), which is based on CGFT for global search and KOSEI for local search. Experiments showed that RSUTT performs more efficiently compared to Aequitas, CGFT, and KOSEI.

  • Software defect prediction based on JavaBERT and CNN-BiLSTM

    Kun Cheng, Shingo Takada

    Proc. of 11th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2023) 3612   51 - 59 2023.12

    Research paper (international conference proceedings), Joint Work, Last author, Accepted,  ISSN  16130073

    　View Summary

    Software defects can lead to severe issues in software systems, such as software errors, security vulnerabilities, and decreased software performance. Early prediction of software defects can prevent these problems, reduce development costs, and enhance system reliability. However, existing methods often focus on manually crafted code features and overlook the rich semantic and contextual information in program code. In this paper, we propose a novel approach that integrates JavaBERT-based embeddings with a CNN-BiLSTM model for software defect prediction. Our model considers code context and captures code patterns and dependencies throughout the code, thereby improving prediction performance. We incorporate Optuna to find optimal hyperparameters. We conducted experiments on the PROMISE dataset, which demonstrated that our approach outperforms baseline models, particularly in leveraging code semantics to enhance defect prediction performance.

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Papers, etc., Registered in KOARA 【 Display / hide 】

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Reviews, Commentaries, etc. 【 Display / hide 】

  • CC2020 プロジェクトと 情報系カリキュラムについて

    高田眞吾

    情報処理 (情報処理学会)  61 ( 11 ) 1119 - 1119 2020.11

    Article, review, commentary, editorial, etc. (scientific journal)

  • Introduction to the Special Issue on Foundations of Software Engineering

    Monden A., Morisaki S., Ohira M., Aman H., Sawada A., Sugiyama Y., Takada S., Hanakawa N., Washizaki H.

    Computer Software (Computer Software)  37 ( 4 )  2020.10

    ISSN  02896540

  • 省略された代名詞の解釈 - 工学系 -

    高田眞吾，土居範久

    日本語学 14 ( 4 ) 19 - 26 1995.04

    Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media), Joint Work

    　View Summary

    省略された代名詞の解釈に関する過去の研究を概観し，それから具体的な研究例としてセンターリストモデルという枠組みを取り上げる．

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Presentations 【 Display / hide 】

  • 公平性テストにおける差別データの多様性の有効性に関する実証研究

    船本和希, 北村崇師, 高田 眞吾

    知能ソフトウェア工学研究会 (KBSE), 

    2025.03
    , 

    Oral presentation (general)

  • カリキュラム標準の国際動向とJ27

    高田眞吾

    情報処理学会第87回全国大会 (大阪) , 

    2025.03
    , 

    Oral presentation (invited, special), 情報処理学会

    　View Summary

    情報系の教育には，計算機科学やソフトウェア工学など複数の領域がある．国際的にはそれぞれの領域でカリキュラム標準を作成しており，本会のJ27は日本国内向けにカリキュラム標準を作成する．本講演では，カリキュラム標準の国際動向について，そしてそれとJ27の関係について述べる．

  • 保護属性を用いずに訓練された機械学習モデルの公平性テスト手法

    久間大誠, 北村崇師, 高田 眞吾

    第66回プログラミングシンポジウム, 

    2025.01
    , 

    Oral presentation (general)

  • 差別データの多様性が再学習に与える影響に関する調査実験と一考察

    船本和希, 北村崇師, 高田 眞吾

    第66回プログラミングシンポジウム, 

    2025.01
    , 

    Oral presentation (general)

  • Software Testing and Artificial Intelligence: The Road Ahead

    Shingo Takada

    36th International Conference on Software Engineering & Knowledge Engineering (SEKE 2024), 

    2024.10
    , 

    Oral presentation (keynote)

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • 機械学習に基づいたソフトウェアテストにおけるカバレッジ向上に関する研究

    2022.04
    -
    2026.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator

  • コンテキスト情報に基づいたモバイルアプリケーションのテストケース生成に関する研究

    2015.04
    -
    2019.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator

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Courses Taught 【 Display / hide 】

  • SOFTWARE ENGINEERING: DEVELOPMENT AND TESTING

    2025

  • RECITATION IN INFORMATION AND COMPUTER SCIENCE

    2025

  • PROGRAMMING METHODOLOGIES

    2025

  • PROGRAMMING 2 B

    2025

  • PROGRAMMING 2 A

    2025

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Memberships in Academic Societies 【 Display / hide 】

  • 情報処理学会 ソフトウェア工学研究会, 

    2006.05
    -
    Present

  • 情報システム学会, 

    2005
    -
    Present

  • 電子情報通信学会, 

    1998
    -
    Present

  • ACM (Association for Computing Machinery), 

    1997
    -
    Present

  • 情報処理学会, 

    1996
    -
    Present

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Committee Experiences 【 Display / hide 】

  • 2025.03
    -
    2025.12

    Program Committee Member, 32nd Asia-Pacific Software Engineering Conference (APSEC 2025)

  • 2025.03
    -
    2025.09

    Program Committee Member, 18th International Conference on the Quality of Information and Communications Technology (QUATIC 2025)

  • 2025.01
    -
    2025.11

    編集委員, コンピュータソフトウェア誌 FOSE特集号 2024

  • 2024.06
    -
    2025.04

    Program Committee Member, 20th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2025)

  • 2024.04
    -
    2024.12

    Program Committee Member, 31st Asia-Pacific Software Engineering Conference (APSEC 2024)

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