9 citations · 10 across the 17 of their papers we have counts for
19 papers
Do Coding Agents Follow AGENTS.md? The Effect of Policy Wording on Instruction Compliance
Rikuto Tsuchida, Youmei Fan, Kazumasa Shimari +2
Coding agents do not always follow the policies in an Agent Context File (ACF). We inserted one policy, in four styles, into the ACFs of 86 benchmark tasks across four harness-mode…
An Exploratory Study of Dependabot Cooldown Adoption in Open-Source GitHub Projects
Hidetake Tanaka, Rikuto Tsuchida, Kazumasa Shimari +2
Automated dependency updates can rapidly propagate malicious package releases before maintainers and the broader community have enough time to detect them. In July 2025, GitHub mad…
Practical Implementation Report on Introducing Spec-Driven Development Using AI Agents in Software Development PBL
Hidetake Tanaka, Hiroshi Igaki, Kazumasa Shimari +2
In recent years, autonomous AI agents such as GitHub Copilot and Claude Code have been rapidly gaining popularity. This study reports on the practical implementation of Spec-Driven…
Predicting Struggling Students in CS1 Programming Using Keystroke-Level Editing Features
Yasuyo Kofune, Kazumasa Shimari, Kenichi Matsumoto
This paper investigates the feasibility of early detection of struggling students during CS1 programming exercises using keystroke-level logs. Some students fail to reach a correct…
Repository and Issue Characteristics of Early GenAI Adopters on GitHub
Rikuto Tsuchida, Youmei Fan, Kazumasa Shimari +3
Generative artificial intelligence (GenAI) has introduced new forms of AI assistance into software development, but less is known about the repositories and issues of early adopter…
How Do Developers Use Migration Guides? A Case Study of Log4j
Takahiro Monno, Kazumasa Shimari, Tetsuya Kanda +2
Migration guides are a form of software documentation that helps developers address breaking changes introduced in library version updates. Prior studies have examined documents su…
A Longitudinal Analysis of Good First Issue Practices and Newcomer Pull Requests in Popular OSS Projects
Hirotatsu Hoshikawa, Hidetake Tanaka, Kazumasa Shimari +2
Open-source software (OSS) projects rely on effective newcomer onboarding to sustain their communities. OSS projects widely adopt "good first issue" (GFI) labels to highlight begin…
How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses
Kan Watanabe, Rikuto Tsuchida, Takahiro Monno +5
The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their…
Forecasting Developer Environments with GenAI: A Research Perspective
Raula Gaikovina Kula, Christoph Treude, Xing Hu +28
Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ab…
Who Writes the Docs in SE 3.0? Agent vs. Human Documentation Pull Requests
Kazuma Yamasaki, Joseph Ayobami Joshua, Tasha Settewong +3
As software engineering moves toward SE3.0, AI agents are increasingly used to carry out development tasks and contribute changes to software projects. It is therefore important to…
Understanding the Characteristics of LLM-Generated Property-Based Tests in Exploring Edge Cases
Hidetake Tanaka, Haruto Tanaka, Kazumasa Shimari +1
As Large Language Models (LLMs) increasingly generate code in software development, ensuring the quality of LLM-generated code has become important. Traditional testing approaches…
Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis
Nirai Hayakawa, Kazumasa Shimari, Kazuma Yamasaki +3
Recently, research on predicting match outcomes in esports has been actively conducted, but much of it is based on match log data and statistical information. This research targets…
eye2vec: Learning Distributed Representations of Eye Movement for Program Comprehension Analysis
Haruhiko Yoshioka, Kazumasa Shimari, Hidetake Uwano +1
This paper presents eye2vec, an infrastructure for analyzing software developers' eye movements while reading source code. In common eye-tracking studies in program comprehension,…
Uncovering Intention through LLM-Driven Code Snippet Description Generation
Yusuf Sulistyo Nugroho, Farah Danisha Salam, Brittany Reid +3
Documenting code snippets is essential to pinpoint key areas where both developers and users should pay attention. Examples include usage examples and other Application Programming…
Using LLMs for Security Advisory Investigations: How Far Are We?
Bayu Fedra Abdullah, Yusuf Sulistyo Nugroho, Brittany Reid +3
Large Language Models (LLMs) are increasingly used in software security, but their trustworthiness in generating accurate vulnerability advisories remains uncertain. This study inv…
Mining for Lags in Updating Critical Security Threats: A Case Study of Log4j Library
Hidetake Tanaka, Kazuma Yamasaki, Momoka Hirose +5
The Log4j-Core vulnerability, known as Log4Shell, exposed significant challenges to dependency management in software ecosystems. When a critical vulnerability is disclosed, it is…
Do Developers Depend on Deprecated Library Versions? A Mining Study of Log4j
Haruhiko Yoshioka, Sila Lertbanjongngam, Masayuki Inaba +5
Log4j has become a widely adopted logging library for Java programs due to its long history and high reliability. Its widespread use is notable not only because of its maturity but…
How Maintainable is Proficient Code? A Case Study of Three PyPI Libraries
Indira Febriyanti, Youmei Fan, Kazumasa Shimari +2
Python is very popular because it can be used for a wider audience of developers, data scientists, machine learning experts and so on. Like other programming languages, there are b…
Nigerian Software Engineer or American Data Scientist? GitHub Profile Recruitment Bias in Large Language Models
Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula +3
Large Language Models (LLMs) have taken the world by storm, demonstrating their ability not only to automate tedious tasks, but also to show some degree of proficiency in completin…