arXiv subjects

Nikolay Fedorov

Publications and source records attributed to Nikolay Fedorov.

4 recordsLinked to original sources

arXiv2026

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

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.

cs.SE↗

arXiv2026

Project-wise Comparison of Software Birthmarks Using Weighted Partial Similarity

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.

cs.SE↗

arXiv2024

Building Defect Prediction Models by Online Learning Considering Defect Overlooking

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, a module predicted as "non-defective" can result in fewer test cases for such modules. Thus, a defective module can be overlooked during testing. The erroneous test results are used as learning data by online learning, which could negatively affect prediction accuracy. To suppress the negative influence, we propose to apply a method that fixes the prediction as positive during the initial stage of online learning. Additionally, we improved the method to consider the probability of the overlooking. In our experiment, we demonstrate this negative influence on prediction accuracy, and the effectiveness of our approach. The results show that our approach did not negatively affect AUC but significantly improved recall.

cs.SE↗

arXiv2023

Software Defect Prediction by Online Learning Considering Defect Overlooking

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, predicting a module as "non-defective" (i.e., negative prediction) can result in fewer test cases for such modules. Therefore, defects can be overlooked during testing, even when the module is defective. The erroneous test results are used as learning data by online learning, which could negatively affect prediction accuracy. In our experiment, we demonstrate this negative influence on prediction accuracy.

cs.SE↗