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Rikuto Tsuchida

Publications and source records attributed to Rikuto Tsuchida.

6 recordsLinked to original sources
arXiv2026

Repository and Issue Characteristics of Early GenAI Adopters on GitHub

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 adopters of GenAI tools. We study the repository and issue characteristics of early adopters who state interest in GenAI on their public profiles on GitHub by analyzing 531 early adopters, 100 of their repositories with AI-tool configuration files (ACFs), 100 matched control repositories without ACFs, and 370 issues from the two repository groups. Repositories with ACFs show modest distributional differences in selected README structural characteristics. They contain longer README files with more code blocks and include a license more often, while repositories without ACFs contain more external URLs. The category distributions of the two repository groups show weak evidence of a difference. Once GenAI relatedness is held fixed, we detect no difference in issue Type or Outcome between the two repository groups. Our data, collected from May to August 2025, record how early adopters developed software in Software Engineering 2.0, the stage in which developers use both AI assistants and autonomous AI agents. When these agents become the norm, later studies can compare their data with ours.

cs.SE↗
arXiv2026

An Exploratory Study of Dependabot Cooldown Adoption in Open-Source GitHub Projects

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 made Dependabot cooldown generally available as a defense against software supply chain attacks. However, the effects of its early adoption remain unknown. In this exploratory study, we empirically examine how popular open-source GitHub repositories adopt and configure the feature and investigate their motivations. We find that security concerns motivated 83 of 92 adoption events with known motivations. Security linter warnings triggered 43 of 75 security-only adoptions. Among 251 ecosystems within repositories that retained cooldown, 97.2% set a general delay. Of these, 64.3% used seven days, while use of each update type setting was below 10%. Early adopters therefore favor simple default delays over fine-grained controls. These findings suggest that tools could provide robust defaults reflecting ecosystem support and reserve fine-grained controls for dependencies with clear update priorities.

cs.SE↗
arXiv2026

How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses

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 pull request description characteristics, and how human reviewers respond to them, remains underexplored. In this study, we conduct an empirical analysis of pull requests created by five AI coding agents using the AIDev dataset. We analyze agent differences in pull request description characteristics, including structural features, and examine human reviewer response in terms of review activity, response timing, sentiment, and merge outcomes. We find that AI coding agents exhibit distinct PR description styles, which are associated with differences in reviewer engagement, response time, and merge outcomes. We observe notable variation across agents in both reviewer interaction metrics and merge rates. These findings highlight the role of pull request presentation and reviewer interaction dynamics in human-AI collaborative software development.

cs.AI↗
arXiv2025

Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis

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 the FPS game VALORANT, which requires complex strategies, and aims to build a round outcome prediction model by analyzing minimap information in match footage. Specifically, based on the video recognition model TimeSformer, we attempt to improve prediction accuracy by incorporating detailed tactical features extracted from minimap information, such as character position information and other in-game events. This paper reports preliminary results showing that a model trained on a dataset augmented with such tactical event labels achieved approximately 81% prediction accuracy, especially from the middle phases of a round onward, significantly outperforming a model trained on a dataset with the minimap information itself. This suggests that leveraging tactical features from match footage is highly effective for predicting round outcomes in VALORANT.

cs.CV↗
arXiv2025

Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation

The purpose of this paper is to examine whether large language models (LLMs) can understand what is good and evil with respect to judging good/evil reputation of celebrities. Specifically, we first apply a large language model (namely, ChatGPT) to the task of collecting sentences that mention the target celebrity from articles about celebrities on Web pages. Next, the collected sentences are categorized based on their contents by ChatGPT, where ChatGPT assigns a category name to each of those categories. Those assigned category names are referred to as "aspects" of each celebrity. Then, by applying the framework of retrieval augmented generation (RAG), we show that the large language model is quite effective in the task of judging good/evil reputation of aspects and descriptions of each celebrity. Finally, also in terms of proving the advantages of the proposed method over existing services incorporating RAG functions, we show that the proposed method of judging good/evil of aspects/descriptions of each celebrity significantly outperform an existing service incorporating RAG functions.

cs.CL↗