6 citations · 6 across the 11 of their papers we have counts for
11 papers
How Developers Discuss Generative AI: A Longitudinal Study of the Visual Studio Code Community
Panida Rumriankit, Akito Monden, Hiroki Inayoshi +4
Generative AI tools such as GitHub Copilot, ChatGPT, and coding agents have rapidly become part of everyday software development, yet little is known about how mainstream open sour…
Detection of LLM-assisted Code Plagiarism Using k-gram Software Birthmarks
Nikolay Fedorov, Akito Monden, Hiroki Inayoshi +2
Large language models (LLMs) have significantly lowered the technical barrier to software plagiarism. By transforming existing source code while preserving its functionality, moder…
Project-wise Comparison of Software Birthmarks Using Weighted Partial Similarity
Nikolay Fedorov, Akito Monden, Hiroki Inayoshi +2
Software birthmarks provide a robust approach to detecting code plagiarism even under substantial modifications, while distinguishing independently developed software. Existing sim…
Personalization of Code Readability Evaluation Based on LLM Using Collaborative Filtering
Buntaro Hiraki, Kensei Hamamoto, Ami Kimura +5
Code readability is an important indicator of software maintenance as it can significantly impact maintenance efforts. Recently, LLM (large language models) have been utilized for…
On Applying Bandit Algorithm to Fault Localization Techniques
Masato Nakao, Kensei Hamamoto, Masateru Tsunoda +5
Developers must select a high-performance fault localization (FL) technique from available ones. A conventional approach is to try to select only one FL technique that is expected…
An Empirical Study of the Impact of Test Strategies on Online Optimization for Ensemble-Learning Defect Prediction
Kensei Hamamoto, Masateru Tsunoda, Amjed Tahir +5
Ensemble learning methods have been used to enhance the reliability of defect prediction models. However, there is an inconclusive stability of a single method attaining the highes…
Building Defect Prediction Models by Online Learning Considering Defect Overlooking
Nikolay Fedorov, Yuta Yamasaki, Masateru Tsunoda +5
Building defect prediction models based on online learning can enhance prediction accuracy. It continuously rebuilds a new prediction model, when a new data point is added. However…
The Impact of Defect (Re) Prediction on Software Testing
Yukasa Murakami, Yuta Yamasaki, Masateru Tsunoda +5
Cross-project defect prediction (CPDP) aims to use data from external projects as historical data may not be available from the same project. In CPDP, deciding on a particular hist…
Selecting Source Code Generation Tools Based on Bandit Algorithms
Ryoto Shima, Masateru Tsunoda, Yukasa Murakami +5
Background: Recently, code generation tools such as ChatGPT have drawn attention to their performance. Generally, a prior analysis of their performance is needed to select new code…
Software Defect Prediction by Online Learning Considering Defect Overlooking
Yuta Yamasaki, Nikolay Fedorov, Masateru Tsunoda +5
Building defect prediction models based on online learning can enhance prediction accuracy. It continuously rebuilds a new prediction model when adding a new data point. However, p…
From Academia to Software Development: Publication Citations in Source Code Comments
Akira Inokuchi, Yusuf Sulistyo Nugroho, Supatsara Wattanakriengkrai +5
Academic publications have been evaluated in terms of their impact on research communities based on many metrics, such as the number of citations. On the other hand, the impact of…