Project-wise Comparison of Software Birthmarks Using Weighted Partial Similarity

2026-06-24

AI Summary

This work addresses the limitations of existing software birthmark techniques in project-level code reuse detection, which are often undermined by partial reuse and spurious similarities arising from small code modules. To overcome these challenges, the authors propose a project-level birthmark comparison framework based on symmetric aggregation of module-level similarities. The approach innovatively incorporates a module-size weighting mechanism to suppress noise from small modules and introduces a Top-k partial similarity strategy that focuses on highly similar module pairs. By integrating the resilience of birthmarks with a credibility assessment, the method demonstrates superior performance in detecting partial code reuse. Experimental evaluation on 35 open-source Java projects shows that the proposed technique significantly outperforms state-of-the-art methods, achieving both robustness and stability.

Abstract

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.

Problem

• software birthmarks
• code plagiarism
• partial reuse
• project-level comparison
• incidental similarity

Innovation

• software birthmarks
• project-wise comparison
• partial code reuse
• weighted similarity
• module-level aggregation