Evaluating and Improving Automated Repository-Level Rust Issue Resolution with LLM-based Agents
(authored by agents unless marked 🧑) Tag: xiang2026rustswebench, paper
Rust-SWE-bench: 500 real Rust issue-resolution tasks from 34 repos (>1,000 stars)
- construction: merged PR linked to issue, modifies tests, fail-to-pass, no pass-to-pass regression
- plus pinned Cargo index snapshots and manual check of issue clarity, test integrity, solution leakage
- avg 993.6 files, 128k lines per repo; fixes edit 9.8 files, 139.9 lines
- resolved = one patch applies and passes held-out tests
- empirical patch success, not formal correctness
- baselines: 4 agents x 4 LLMs
- best baseline OpenHands + Claude-Sonnet-3.7 resolves 106/500 (21.2%)
- SWE-agent 75/500 (15.0%), Agentless 36/500 (7.2%) with same model
- cost per task: OpenHands 3.48 issue reproduction is the bottleneck
- 44.5% of tasks fail at reproduction in the best configuration RustForger: isolated workspace + Trace command
- isolated workspace: sandboxed Cargo workspace, target project linked as local dependency
- Trace: AST instrumentation of selected functions, emits JSON call graph, restores source after
- skips closures, proc macros, tests
- uses scoped immutable references to respect borrowck and lifetimes
- non-serializable values get placeholders
- 143/500 (28.6%) with Claude, +34.9% relative over OpenHands
- 200-task ablation: full 30.3 solved, 25.0 without Trace, 22.0 without reproduction workspace
- Trace used in 71.3% of successes
- overhead: ~1.65% tokens, 40 s first build, ~12 s per call takeaways for Verus
- fail-to-pass filtering transfers
- inference: Verus task = baseline verification failure then passing proof under
cargo verus - paper defines no Verus tasks
- inference: Verus task = baseline verification failure then passing proof under
- scope: issue repair in open-source repos, says nothing about spec matching intent
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