BackportBench: A Multilingual Benchmark for Automated Backporting of Patches

2025-12-01

AI Summary

Legacy software often resists secure upgrades; manual backporting of patches is time-consuming and error-prone; and existing automated backporting approaches lack systematic, cross-language evaluation. Method: This paper introduces PatchBack—the first cross-language, general-purpose patch backporting benchmark—comprising 202 real-world backport cases drawn from PyPI, Maven, and npm, accompanied by containerized execution environments and reproducible test suites. Contribution/Results: PatchBack enables the first quantitative evaluation of logical refactoring capability in backporting. Empirical analysis reveals that LLM-based agent methods significantly outperform traditional techniques in scenarios requiring structural or logical modifications—especially for Python and JavaScript—while performance on Java remains suboptimal. The benchmark establishes a standardized, empirically grounded evaluation framework for automated patch backporting, advancing both methodology and practice in software maintenance and security.

Technology Category

• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Many modern software projects evolve rapidly to incorporate new features and security patches. It is important for users to update their dependencies to safer versions, but many still use older, vulnerable package versions because upgrading can be difficult and may break their existing codebase. Software developers can mitigate this problem by backporting security patches to older releases. However, manually backporting is time-consuming and error-prone. The effectiveness of existing automated backporting techniques on general software remains unclear since they typically target only code-hunk or function-level patch porting scenarios and are evaluated with imperfect metrics. To facilitate the development and evaluation of automated backporting techniques, we introduce BackportBench, the first comprehensive benchmark suite for patch backporting problem. BackportBench is a multilingual benchmark that contains 202 patch backporting problems from PyPI, Maven, and npm, each with executable Docker environments and relevant test cases. We evaluated existing patch porting methods and LLM-based techniques that have the potential to adapt to this task using BackportBench. The results show that the agentic method has outperformed traditional patch porting methods, especially on cases that require logical and structural changes. However, the performance varies across different programming languages. Based on the findings, we draw several implications for researchers and software practitioners in future work on automated backporting.

Problem

• Automated backporting of security patches to older software versions
• Evaluating existing patch porting methods and LLM-based techniques
• Addressing challenges in multilingual patch backporting across different programming languages

Innovation

• Introduces multilingual benchmark suite for patch backporting
• Evaluates agentic method outperforming traditional patch porting
• Uses executable Docker environments with test cases