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The paper investigates how AI code agents perform when the surrounding code is rewritten in a semantically equivalent way. Using a random variant sampler that applies control‑flow rewrites, dead‑code injection, and identifier renaming, the authors evaluate two agent scaffolds—mini‑SWE agent and OpenCode—backed by four frontier models across SWE‑bench datasets. Results show modest drops in resolve‑rate (up to 6.7 percentage points) with significant degradations in 6 of 16 configurations, and reveal that robustness varies across models and scaffolds, forming a jagged frontier.
arXiv:2507. 11059v3 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset.
E2E-SWE is a benchmark that tests large language models’ ability to create complete, functional software repositories from scratch. It includes 186 tasks across 11 programming languages, each requiring an agent to build an installable project based solely on a natural‑language specification and an empty workspace, while passing a hidden test suite. The benchmark was crafted by software engineers and LLMs, then refined through iterative verification by autonomous agents to ensure clarity and solvability.
arXiv:2608. 14863v1 Announce Type: cross Abstract: LLM-based coding agents have advanced rapidly on single-process SWE tasks, with frontier models now clustering in the high-70s on SWE-bench Verified.
arXiv:2610.09079v1 Announce Type: cross Abstract: Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete r...
arXiv:2607. 18161v1 Announce Type: cross Abstract: Coding agents are increasingly used to accelerate code generation in many downstream tasks, such as fixing bugs, building applications, and prototyping.