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20202026
most citedGenerative AI for Requirements Engineering: A Systematic Literature Review

38 citations · 40 across the 10 of their papers we have counts for

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11 papers · 1 filter

cs.SE2026

A Study on the Impact of Natural Language Differences in Prompts on Automatic Code Generation Using LLMs

Haruka Tokumasu, Masanari Kondo, Alexander Serebrenik +5

Large Language Models (LLMs) have demonstrated remarkable performance in automatic code generation tasks, thereby encouraging new research in this area. Although numerous studies h…

cs.SE2026

RepTran: Search-Based Repair of Transformer Models

Yuta Ishimoto, Paolo Arcaini, Fuyuki Ishikawa +3

To ensure the overall quality of AI-enabled software, not only traditional software components but also AI components need to be tested and repaired. Among AI components, Transform…

cs.SE2025

An empirical study on declined proposals: why are these proposals declined?

Masanari Kondo, Mahmoud Alfadel, Shane McIntosh +2

Design-level decisions in open-source software (OSS) projects are often made through structured mechanisms such as proposals, which require substantial community discussion and rev…

cs.SE2025

Evaluating Mutation-based Fault Localization for Quantum Programs

Yuta Ishimoto, Masanari Kondo, Naoyasu Ubayashi +4

Quantum computers leverage the principles of quantum mechanics to execute operations. They require quantum programs that define operations on quantum bits (qubits), the fundamental…

cs.SE2024

Myth: The loss of core developers is a critical issue for OSS communities

Olivier Nourry, Masanari Kondo, Shinobu Saito +3

Throughout their lifetime, open-source software systems will naturally attract new contributors and lose existing contributors. Not all OSS contributors are equal, however, as some…

cs.SE2024★ 38 cited

Generative AI for Requirements Engineering: A Systematic Literature Review

Haowei Cheng, Jati H. Husen, Yijun Lu +4

Introduction: Requirements engineering faces challenges due to the handling of increasingly complex software systems. These challenges can be addressed using generative AI. Given t…

cs.SE2024

An Empirical Study of Token-based Micro Commits

Masanari Kondo, Daniel M. German, Yasutaka Kamei +2

In software development, developers frequently apply maintenance activities to the source code that change a few lines by a single commit. A good understanding of the characteristi…

cs.SE2024

OSS Myths and Facts

Yukako Iimura, Masanari Kondo, Kazushi Tomoto +3

We have selected six myths about the OSS community and have tested whether they are true or not. The purpose of this report is to identify the lessons that can be learned from the…

cs.SE2024★ 1 cited

Exploring the Effect of Multiple Natural Languages on Code Suggestion Using GitHub Copilot

Kei Koyanagi, Dong Wang, Kotaro Noguchi +4

GitHub Copilot is an AI-enabled tool that automates program synthesis. It has gained significant attention since its launch in 2021. Recent studies have extensively examined Copilo…

cs.SE2023

When Conversations Turn Into Work: A Taxonomy of Converted Discussions and Issues in GitHub

Dong Wang, Masanari Kondo, Yasutaka Kamei +2

Popular and large contemporary open-source projects now embrace a diverse set of documentation for communication channels. Examples include contribution guidelines (i.e., commit me…

cs.SE2020★ 1 cited

A Quantitative Study of Security Bug Fixes of GitHub Repositories

Daito Nakano, Mingyang Yin, Ryosuke Sato +3

Software is prone to bugs and failures. Security bugs are those that expose or share privileged information and access in violation of the software's requirements. Given the seriou…