[Paper] BackportBench: A Multilingual Benchmark for Automated Backporting of Patches

Source: arXiv - 2512.01396v1

Overview

The paper introduces BackportBench, the first large‑scale, multilingual benchmark designed to evaluate automated backporting of security and bug‑fix patches. By collecting 202 real‑world backporting tasks from Python (PyPI), Java (Maven), and JavaScript (npm) ecosystems—each packaged with a Docker environment and test suite—the authors provide a realistic playground for measuring how well current tools and large language models (LLMs) can port fixes to older, still‑in‑use library versions.

Key Contributions

• BackportBench dataset: 202 curated backporting problems spanning three major programming languages, complete with reproducible Docker images and test cases.
• Comprehensive evaluation protocol: Standardized metrics (pass/fail of test suites, semantic similarity, and manual correctness checks) that address shortcomings of prior, overly‑synthetic benchmarks.
• Empirical study: Systematic comparison of classic patch‑porting tools (e.g., Coccinelle‑style hunk transplant, function‑level adaptors) against modern LLM‑driven approaches, including zero‑shot, few‑shot, and agentic prompting strategies.
• Insights & guidelines: Identification of language‑specific challenges (e.g., dynamic typing in Python vs. static typing in Java) and practical recommendations for building more robust automated backporters.

Methodology

1. Data collection – The authors mined the issue trackers of popular PyPI, Maven, and npm packages, selecting patches that were explicitly backported by maintainers. Each case includes:
   • The original vulnerable/faulty commit (source version).
   • The target older release where the patch should be applied.
   • A Dockerfile reproducing the exact build and test environment.
2. Benchmark construction – For every case they generated a backporting problem consisting of:
   • The diff of the original patch.
   • The codebase of the older release.
   • A test suite that fails before backporting and should pass after a correct fix.
3. Tool selection – They evaluated:
   • Traditional rule‑based patch porters (e.g., PatchPort, Coccinelle).
   • LLM‑based methods: GPT‑4, Claude, and an agentic pipeline that iteratively edits code, runs tests, and refines the patch.
4. Metrics – Success is measured by:
   • Test‑suite pass rate (primary functional correctness).
   • Semantic similarity between generated and human‑written backports.
   • Manual inspection for subtle logical errors not caught by tests.

Results & Findings

• Language variance: Java backports saw the highest success (≈ 82 % with the agentic method), while Python lagged (≈ 73 %) due to its dynamic nature and reliance on runtime introspection.
• Logical vs. structural changes: Agentic prompting excelled when the fix required adding new helper functions or re‑architecting call‑graphs, scenarios where pure diff‑matching fails.
• Error patterns: Remaining failures often involved subtle side‑effects (e.g., changed exception types) that the test suite did not cover, highlighting the need for richer validation.

Practical Implications

• For DevOps & security teams: BackportBench can be integrated into CI pipelines to automatically evaluate candidate backports before they are merged into legacy branches, reducing manual effort and exposure time.
• Tool developers: The benchmark offers a concrete, reproducible testbed for training and fine‑tuning LLMs or building hybrid systems that combine static analysis with LLM reasoning.
• Package maintainers: By exposing the typical failure modes of current automation, maintainers can prioritize documentation (e.g., clear API deprecation notes) that makes automated backporting more tractable.
• LLM vendors: The agentic approach demonstrates that “think‑test‑revise” loops dramatically improve reliability, suggesting that future APIs should expose native test execution hooks for code‑generation agents.

Limitations & Future Work

• Scope of languages: The benchmark currently covers only Python, Java, and JavaScript; extending to compiled languages like C/C++ or Rust could surface different challenges (e.g., binary compatibility).
• Test‑suite completeness: Success is measured against existing tests, which may miss hidden bugs; augmenting benchmarks with mutation testing or property‑based tests would provide a stricter correctness signal.
• Scalability of agentic pipelines: Iterative test‑run cycles are computationally expensive; future research should explore more efficient verification strategies (e.g., static type‑checking, symbolic execution) to speed up large‑scale backporting.
• Human‑in‑the‑loop evaluation: While manual inspection was performed on a sample, a larger user study would better quantify developer trust and adoption barriers.

BackportBench opens the door for systematic, language‑aware research on automated patch backporting—an area that directly impacts software supply‑chain security and the day‑to‑day workflow of developers maintaining legacy systems.

Authors

• Zhiqing Zhong
• Jiaming Huang
• Pinjia He

Paper Information

• arXiv ID: 2512.01396v1
• Categories: cs.SE, cs.CL, cs.CR
• Published: December 1, 2025
• PDF: Download PDF