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 】

  • 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 】

  • 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．

  • 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)    51 - 59 2023.12

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

    　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.

  • Review Classification Based on Machine Learning: Classifying Game User Reviews

    Zhang Yejian, Shingo Takada

    IEEE Access (IEEE)  11   142447 - 142463 2023.12

    Research paper (scientific journal), Joint Work, Last author, Accepted

    　View Summary

    With the development of the game industry, the maturity of online game sales platforms, and the increasing complexity of game software itself, game companies need to analyze massive amounts of user reviews to understand the hidden defects of the game and the direction of future iterations. Manually reading game reviews is a labor-intensive and time-consuming task, as the number of reviews can go up to several thousand per day. Automatically classifying these game reviews will help alleviate this issue, but traditional classifiers will need a large number of labeled instances for training. In this paper, we propose and implement an approach that combines transfer learning in natural language processing (BERT), unsupervised learning, and active learning to classify game reviews using only a small amount of labeled instances. We found that our approach obtains 88.8% classification accuracy with only 100 labeled training instances. Our implementation can be extended to handle different types of new games by using a small amount of extra labeled instances and manual work.

  • Applying Reinforcement Learning for Automated Testing of Mobile Application Focusing on State Definition, Reward, and Learning Method

    Keita Murase, Shingo Takada

    Proc. of 35th International Conference on Software Engineering & Knowledge Engineering    64 - 69 2023.07

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

    　View Summary

    There have been various studies on the automation of mobile app testing. Typical methods for automated testing of mobile apps are based on random search and on building state transition models. But there are problems in terms of the efficiency of search and accuracy of model building. This paper focuses on applying reinforcement learning to testing of mobile apps, especially issues such as explosion of the number of states, fixed rewards for transitions, and difficulty in convergence of learning. We focus on state definition, reward function, and a learning method to solve these problems. Specifically, we define states using discrete values of UI (User Interface) information on the screen, def ine adynamic reward function, and perform periodic learning by using the transition history. The proposed method is implemented and evaluated. Evaluation results show that our proposed approach shows 1.21 times higher coverage than an existing tool using reinforcement learning.

  • Applying Symbolic Execution to Semantic Code Clone Detection

    Kazusa Takemoto, Shingo Takada

    Proc. of 35th International Conference on Software Engineering & Knowledge Engineering    118 - 122 2023.07

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

    　View Summary

    Many approaches have been proposed to detect code clones, which are basically similar code fragments. Most approaches are based on textual similarity. These approaches cannot detect semantic code clones, which are clones that have the same functionality but implemented with different syntax. Two functions can be considered to have the same functionality, when the output is the same given the same input. In order to appropriately generate inputs, we propose applying symbolic execution to semantic code clone detection. These functions are executed to obtain outputs, which are compared to determine if function pairs are clones. Our approach also does not limit output to return values; we also handle arrays and pointers as output, as the execution of the function may cause changes in their values. Furthermore, we classify types to enable cases where the types of inputs and/or outputs are not exactly the same. We evaluate our approach on SemanticCloneBench.

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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 】

  • 機械学習モデルの公平性向上に関する一考察

    松川 知聖，高田 眞吾

    第30回ソフトウェア工学の基礎ワークショップ (FOSE2023), 

    2023.11
    , 

    Poster presentation

  • Reusing Test Cases Across Smartphone Applications with Similar Functionalities

    Nguyen Bao Ngoc，Shingo Takada

    第30回ソフトウェア工学の基礎ワークショップ (FOSE2023), 

    2023.11
    , 

    Poster presentation

  • LLMを用いたWebアプリケーション用のテストコード生成に関する一考察

    伊藤 陸斗，高田 眞吾

    第30回ソフトウェア工学の基礎ワークショップ (FOSE2023), 

    2023.11
    , 

    Poster presentation

  • 記号実行を利用したセマンティックコードクローンの検出に関する一考察

    武元憲将, 高田眞吾

    第28回ソフトウェア工学の基礎ワークショップ (FOSE2021), 

    2021.11
    , 

    Poster presentation

  • モバイルゲームのテスト自動化における強化学習の利用に関する一考察

    村瀬渓太, 高田眞吾

    第28回ソフトウェア工学の基礎ワークショップ (FOSE2021), 

    2021.11
    , 

    Poster presentation

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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
    -
    2025.03

    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

    2024

  • RECITATION IN INFORMATION AND COMPUTER SCIENCE

    2024

  • PROGRAMMING METHODOLOGIES

    2024

  • PROGRAMMING 2 B

    2024

  • PROGRAMMING 2 A

    2024

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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 】

  • 2024.04
    -
    2024.12

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

  • 2024.02
    -
    2025.04

    編集委員, 情報処理学会論文誌「ソフトウェア工学」特集号 2024

  • 2024.02
    -
    2024.07

    Program Committee Member, 19th International Conference on Software Technologies (ICSOFT 2024)

  • 2024.01
    -
    2024.11

    編集委員, コンピュータソフトウェア誌 FOSE特集号 2023

  • 2024.01
    -
    2024.09

    Program Committee Member, 17th International Conference on the Quality of Information and Communications Technology (QUATIC 2024)

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