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Source note2 min read

PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks

Source

arXiv

arxiv.org/abs/2607.28587v1

Read the source

Source

Plain-Language Brief

Researchers have developed a system called PAIChecker to check for misalignment between Pull Requests (PRs) and issues in software development benchmarks used to evaluate the issue resolution capability of large language models (LLMs). The study found that 13.6% of SWE-bench Verified instances exhibit misalignment across five patterns in eleven fine-grained scenarios. PAIChecker uses a three-phase design for more accurate and generalizable detection and achieves the best performance among four LLM backbones.

What the Document Says

  • SWE-bench-like benchmarks are used for evaluating LLMs' issue resolution capability.
  • Each PR is paired with its linked issue by extracting references from the PR description.
  • The issue description serves as the problem statement, and the PR patch serves as the test oracle.
  • Due to repository complexity, PR-Issue pairings are often misaligned in practice.
  • Researchers found 13.6% misalignment across five patterns in eleven fine-grained scenarios of SWE-bench Verified instances.
  • PAIChecker is a multi-agent system for checking PR-Issue misalignment in SWE-bench-like benchmarks.
  • PAIChecker uses a three-phase design: pattern identification, cross-agent label synthesis, and code-level validation.
  • PAIchecker achieves the best performance across four LLM backbones on SWE-Gym and SWE-bench Multilingual, reaching up to 92.12% and 91.67% binary accuracy, respectively.

Key Concepts Mentioned

  • SWE-bench-like benchmarks
  • Pull Requests (PRs)
  • Issues
  • Large Language Models (LLMs)
  • PAIChecker
  • misalignment
  • pattern identification
  • cross-agent label synthesis
  • code-level validation
  • binary accuracy

Claims Made by the Document

  • 13.6% of SWE-bench Verified instances exhibit misalignment.
  • PAIChecker achieves the best performance across four LLM backbones.

Evidence Used

  • 13.6% is reported in the text from the study.
  • The experimental results on SWE-Gym and SWE-bench Multilingual show the performance of PAIchecker.

Methods / Approach

  • three-phase design: pattern identification, cross-agent label synthesis, and code-level validation

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

  • What are the five patterns of misalignment mentioned in the study?
  • How was the performance of PAIChecker evaluated besides binary accuracy?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The study focuses on SWE-bench Verified instances, but it's unknown whether the findings apply to other types of benchmarks.
  • It's not stated in the source how the four LLM backbones were chosen or compared.

Raw Abstract / Excerpt

SWE-bench-like benchmarks are widely used for evaluating LLM's issue resolution capability. They typically follow a common construction pipeline: each PR (Pull Request) is paired with its linked issue by extracting issue references from the PR description; the issue description is used as the problem statement, and the PR patch serves as the test oracle. However, due to the inherent complexity of developing and maintaining large repositories, such PR-Issue pairings are often misaligned in practice. In this work, we systematically study SWE-bench Verified instances, finding that 13.6% exhibit misalignment across five patterns in eleven fine-grained scenarios. To enable reliable and scalable construction of those benchmarks in the future, we propose PAIChecker, a multi-agent system for checking PR-Issue misalignment in SWE-bench-like benchmarks. Specifically, PAIChecker adopts a three-phase design that combines specific pattern identification, cross-agent label synthesis, and code-level validation, thereby enabling more accurate, generalizable, and progressively verified detection. Experiments on SWE-Gym and SWE-bench Multilingual show that PAIchecker achieves the best performance across all four LLM backbones, reaching up to 92.12% and 91.67% binary accuracy, respectively.