KEIO RESEARCHERS INFORMATION SYSTEM

Career 【 Display / hide 】

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

Academic Background 【 Display / hide 】

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

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

• 工学　, Keio University, 1995.03

Research Areas 【 Display / hide 】

Research Areas 【 Display / hide 】

• Informatics / Software (ソフトウエア)

Research Keywords 【 Display / hide 】

Research Keywords 【 Display / hide 】

• Software Engineering

Books 【 Display / hide 】

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.

  • Access to Document (DOI)

• 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

Papers 【 Display / hide 】

Papers 【 Display / hide 】

• Individual Fairness Testing in Fairness through Unawareness

  Taisei Kuma , Takashi Kitamura , Shingo Takada

  Proc. 2026 IEEE International Conference on Software Testing, Verification and Validation (ICST)    543 - 554 2026.05

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

  　View Summary

  Individual fairness testing evaluates a machine learning (ML) classifier by estimating its individual fairness ratio (IFr) and has become an important approach for assessing ML classifiers. However, existing studies on ML fairness testing focus exclusively on the Fairness Through Awareness (FTA) setting, where protected attributes are explicitly used. In this study, we introduce the first framework for individual fairness testing under Fairness Through Unawareness (FTU), where protected attributes are not explicitly available. The proposed framework, called IFTU_NAWARE, evaluates a classifier by estimating its IFr in the FTU setting. It extends individual fairness testing approaches developed for FTA by incorporating techniques, including protected attribute inference with confidence control, a generalized notion of individual similarity, and statistically guaranteed termination criteria. Our experiments show that IFTU_NAWARE can compute IFr estimates with statistical guarantees and demonstrate the effectiveness of both the generalized similarity notion and confidence-based protected attribute inference. These results highlight both the feasibility and the limitations of assessing individual fairness under FTU and provide a foundation for future methodological advances.

• Automatic GUI Testing of Android Applications Based on Multi-Agent Reinforcement Learning

  Kazuki Dodo, Shingo Takada

  Proc. 2026 IEEE International Conference on Software Testing, Verification and Validation Workshops (ASTA2026)    37 - 40 2026.05

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

  　View Summary

  Recent efforts in GUI testing of mobile applications have seen the use of reinforcement learning. Such work have focused on single agents, but they have had issues such as repeating the same paths, resulting in it being difficult to increase coverage. On the other hand, the reinforcement learning research field has seen the use of multiple agents.We propose a multi-agent reinforcement learning based approach to Android testing called MultiQDroid. MultiQDroid has multiple agents exploring the state space, each with a different exploration strategy. Agents can access memory containing their own explored states, as well as a shared memory that contains states that have been explored by all agents. The reward function is defined so that new states are given higher reward. Preliminary evaluation shows that MultiQDroid achieve higher coverage compared to a single agent approach.

• 欠陥予測を考慮したモバイルアプリケーションの自動 GUI テスト

  赤坂 虎大朗，高田 眞吾

  第32回ソフトウェア工学の基礎ワークショップ (FOSE2025)  2025.11

  Last author, Accepted

• Generating test code for web applications using LLM

  Rikuto Ito, Shingo Takada

  Proceedings of the 18th International Conference on the Quality of Information and Communications Technology (QUATIC2025)  2025.09

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

  　View Summary

  Testing web applications is challenging due to their complex user interfaces and dynamic behaviors. Achieving comprehensive state exploration requires generating appropriate input values that satisfy validation constraints. Existing approaches using random input generation or model-based techniques often struggle with form validation and contextual input requirements. This paper proposes a novel approach leveraging Large Language Models (LLMs) to understand web application context and generate meaningful inputs for effective state exploration. Our method extracts UI elements and contextual information from HTML, converts them into structured prompts for LLMs, and generates executable Selenium WebDriver code with appropriate inputs. The system then executes this code and continues exploration by detecting state transitions using normalized Levenshtein distance. Evaluation on multiple open-source web applications shows up to 30% higher code coverage and increased detection of client-side errors, confirming the effectiveness of our approach to LLM-assisted testing for web applications.

• 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

  　View Summary

  Graphical User Interface (GUI) testing is crucial for ensuring that Android applications deliver a positive, consistent, and functional user experience. Nonetheless, most automated GUI testing techniques aim at maximizing code coverage or finding as many crashes as possible, not focusing much on the functional features of the app. Recently, test reuse based on semantic similarities between applications with similar functionalities has been utilized to reduce the cost of GUI testing. However, they still face many obstacles, such as limited text semantic information and ineffective semantic matching rules, resulting in generating inaccurate test cases. In this paper, we propose SMAPDroid, a framework for reusing test cases between Android applications with similar features. Our evaluation results show that SMAPDroid performs better than prior work in terms of precision and recall, and reduced human effort by 91% compared to writing test scripts from scratch.

  • Access to Document (DOI)

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

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

• Generating test cases for mobile applications based on context information

  Takada, Shingo

  科学研究費補助金研究成果報告書 2018

• An environment for dynamically reconfiguring service-oriented systems

  Takada, Shingo

  科学研究費補助金研究成果報告書 2012

Reviews, Commentaries, etc. 【 Display / hide 】

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

  • Access to Document (DOI)

• 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．

Presentations 【 Display / hide 】

Presentations 【 Display / hide 】

• LLM によるライブラリを利用したコードの生成に関する一考察

  赤坂 虎大朗，高田 眞吾

  [Domestic presentation]  第32回ソフトウェア工学の基礎ワークショップ (FOSE2025), 

  2025.11

  , 

  Poster presentation

• 公平性テストにおける差別データの妥当性が再学習に与える影響に関する一考察

  船本和希, 北村崇師, 高田 眞吾

  [Domestic presentation]  第32回ソフトウェア工学の基礎ワークショップ (FOSE2025), 

  2025.11

  , 

  Poster presentation

• フォルトローカリゼーションにおける静的手法と動的手法の一考察

  磯野 雅志，高田 眞吾

  [Domestic presentation]  第32回ソフトウェア工学の基礎ワークショップ (FOSE2025), 

  2025.11

  , 

  Poster presentation

• GAN と拡散モデルを用いたテストケース生成に関する一考察

  塗 遠シン，高田 眞吾

  [Domestic presentation]  第32回ソフトウェア工学の基礎ワークショップ (FOSE2025), 

  2025.11

  , 

  Poster presentation

• 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

  [Domestic presentation]  知能ソフトウェア工学研究会 (KBSE), 

  2025.03

  , 

  Oral presentation (general)

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

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

• モバイルアプリケーションのテストにおけるテキスト入力欄の入力値の自動生成

  2026.04

  -

  2029.03

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

• 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

Courses Taught 【 Display / hide 】

Courses Taught 【 Display / hide 】

• GRADUATE RESEARCH ON INFORMATICS, MANAGEMENT, AND HUMAN SCIENCES A2

  2026

• PROGRAMMING 2 A

  2026

• JINKAN-KOSAI PROJECT 1

  2026

• RECITATION IN INFORMATION AND COMPUTER SCIENCE

  2026

• GRADUATE RESEARCH ON SCIENCE FOR OPEN AND ENVIRONMENTAL SYSTEMS 1

  2026

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Memberships in Academic Societies 【 Display / hide 】

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 】

Committee Experiences 【 Display / hide 】

• 2026.04

  -

  2026.12

  Program Committee Member, 33rd Asia-Pacific Software Engineering Conference (APSEC 2026)

• 2026.03

  -

  2026.09

  Program Committee Member, 19th International Conference on the Quality of Information and Communications Technology (QUATIC 2026)

• 2026.03

  -

  2026.09

  Program Committee Member, ソフトウェアエンジニアリングシンポジウム2026

• 2026.02

  -

  2026.07

  Program Committee Member, COMPSAC 2026 -- Symposium on Software Engineering Technologies & Applications

• 2026.01

  -

  2026.11

  編集委員, コンピュータソフトウェア誌 FOSE特集号 2025

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