7 papers
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…
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…