most citedRound Outcome Prediction in VALORANT Using Tactical Features from Video Analysis

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2026

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…

cs.AI2026

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…

cs.SE2026

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…

cs.CV2025★ 1 cited

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…

cs.SE2025

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…