Hiroki Inayoshi

h-index4
2papers
26citations

2 Papers

3.9SEJun 24Code
Project-wise Comparison of Software Birthmarks Using Weighted Partial Similarity

Nikolay Fedorov, Akito Monden, Hiroki Inayoshi et al.

Software birthmarks provide a robust approach to detecting code plagiarism even under substantial modifications, while distinguishing independently developed software. Existing similarity measures are typically applied at the module level (e.g., source or class files). However, in practice, software reuse often occurs at the project level, where only a subset of modules may be reused. This setting introduces two key challenges: (1) partial reuse, where reused modules constitute only a small fraction of the project, and (2) incidental similarity from small modules, which can lead to false positives. In this paper, we establish a framework for project-wise birthmark comparison based on a symmetric aggregation of module-level similarities. On top of this framework, we propose two complementary mechanisms to address the above challenges. First, we introduce a weighting scheme that assigns higher importance to larger modules, reducing the influence of noisy matches from small modules. Second, we propose a partial similarity method that focuses on the top fraction of highly similar module pairs, enabling robust detection of partial reuse. We evaluate the proposed approach on 35 open-source Java projects across ten categories, where different versions of the same project are treated as reuse cases. The dataset and experimental artifacts are made publicly available to support reproducibility. Performance is assessed using two complementary properties of software birthmarks, resilience and credibility, combined via their harmonic mean. The results show that the proposed method consistently outperforms existing approaches, achieving robust and stable detection of partial code reuse at the project level.

4.2SEJul 3
Detection of LLM-assisted Code Plagiarism Using k-gram Software Birthmarks

Nikolay Fedorov, Akito Monden, Hiroki Inayoshi et al.

Large language models (LLMs) have significantly lowered the technical barrier to software plagiarism. By transforming existing source code while preserving its functionality, modern LLMs can generate semantically identical program that may evade traditional plagiarism detection techniques. Among such attacks, code paraphrasing modifies the syntax and structure of a program while preserving its behavior. This paper investigates whether software birthmarks can detect such LLM-assisted plagiarism. As a starting point, we employ k-gram software birthmarks based on unique k-grams of Java opcodes, with k ranging from 1 to 6. We employ three contemporary LLMs: ChatGPT-5.1-Codex-Mini, DeepSeek-V4-Flash, and Claude-Haiku-4.5. The dataset consists of individually compilable source files extracted from actively maintained BSD-2-Clause licensed Java projects. We further compare five similarity measures for birthmark matching: cosine similarity, Dice index, Jaccard coefficient, Simpson index, and edit-distance-based similarity. The results demonstrate that k-gram software birthmarks remain effective for detecting LLM-assisted plagiarism. Among the evaluated models, ChatGPT-5.1-Codex-Mini generated the most difficult-to-detect clones. Furthermore, the findings confirm the higher performance of coding-oriented models for plagiarism task.