Skip to content
Review

OdinEval: A Reproducible Benchmark for LLM-Based Program Repair in the Odin Programming Language

Aug 2026 · 0 citations · 29 references
Computer Science

TL;DR

OdinEval is presented, a reproducible benchmark built from documented defects in public Odin repositories built from documented defects in public Odin repositories, that evaluates six language models on 168 filtered instances under one shared protocol.

Abstract

Repository-level repair benchmarks still center on a few mainstream languages, leaving systems languages such as Odin largely untested. We present OdinEval, a reproducible benchmark built from documented defects in public Odin repositories. Each instance binds an issue to base and fix commits, a gold patch, an issue-specific regression test, a historical toolchain, and execution records. Admission requires the test to fail on the base revision and pass after the gold fix. When no usable developer test exists, a black-box test is reviewed independently by three instances of the same model, executed in both historical states, and revised from recorded feedback under a versioned Test Writing Skill. We evaluate six language models on 168 filtered instances under one shared protocol. Kimi-K3 records the highest Resolved score at 66.7%, while Qwen3.8-Max has the highest Repro score at 96.4%. The release includes frozen data, source archives, containers, validators, model patches, and audit manifests.

View source

Similar papers

Preprint Aug 2026

AppEval: A Unified Benchmark for LLM-Based Mobile Application Repair in ArkTS, Swift, and Kotlin

AppEval is presented, a benchmark and native-toolchain evaluation framework for mobile application repair across HarmonyOS/ArkTS, iOS/Swift, and Android/Kotlin, and shows that mobile repair performance depends strongly on the evaluated agent while demonstrating why runtime-aware acceptance is necessary for meaningful c...

Bang Xie, Hao Liu, Zhen-Yu Shi et al. · 0 citations
Review Aug 2026

Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair

This paper identifies patch verbosity as a major yet overlooked concern in LLM-based APR and proposes RECAP, a lightweight, plug-and-play adapter that attaches to existing repair frameworks after generation that achieves a substantially better size-correctness tradeoff.

Wen-Qiang Luo, J. Keung, Xiaoyu Shi et al. · 1 citation
Jul 2026

The Patchwork Problem in LLM-Generated Code

This paper formalizes structural coherence as consistency invariants over graph representations of repository artifacts, including import, call, dependency, configuration, schema, resource, control-flow, and routing graphs, and introduces an eight-category failure taxonomy distinguishing defects specific to LLM generat...

Viraaji Mothukuri, R. Parizi · 0 citations
Jul 2026

Benchmarking Large Language Models on Repairing Qiskit Programs using Bugs4Q

A re-validated, version-pinned Bugs4Q benchmark is released and it is shown that benchmark validation must precede repair evaluation, which can suffer from silent label inversion.

Saumya Brahmbhatt, Mitali Hukkeri, Dongchan Kim et al. · 0 citations
Preprint Aug 2026

DepWareTrans: Dependency-Aware Incremental Repository Migration across Co-executable Languages

This paper proposes a dependency-aware incremental migration framework that elevates the unit of translation from individual files to dependency-consistent batches and improves scalability and reliability in repository-level code translation.

Sivajeet Chand, Alexander Pretschner, Steve Haupt et al. · 2 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.