The evaluation of coding agents generally strives to be as realistic as possible. In our study, we collect 4,782 agent sessions of real software engineers in JetBrains IDEs, which we call Production S...
arXiv:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.
arXiv:2610.09633v1 Announce Type: new Abstract: The evaluation of coding agents generally strives to be as realistic as possible. In our study, we collect 4,782 agent sessions of real software engine...
SWE‑Duel proposes a self‑scaling benchmark for coding agents, where tasks and oracles are generated at evaluation time to prevent data leakage and adapt difficulty to participant performance. In the arena, two agents take turns as Red and Blue: Red proposes a feature, injects a subtle bug, and must repair it blind, while Blue reviews the pull request without knowing the bug and must pass all tests. A tournament of 11 model‑harness participants on 15 repositories across five languages showed GPT‑5.5/Codex CLI as the top solver and Claude Opus 4.8/mini‑swe‑agent as the top task author, with newer model versions producing harder tasks for older ones.
SWE‑Duel proposes a self‑scaling benchmark for coding agents, where tasks and oracles are generated at evaluation time to prevent data leakage and adapt difficulty to the agents’ capabilities. In the arena, two agents take turns modifying a real repository: the Red agent adds a feature, introduces a subtle bug, and the Blue agent must fix it without knowing the bug exists. A tournament of 11 model‑harness participants on 15 repositories in five languages showed GPT‑5.5 as the best solver and Claude Opus 4.8 as the best task author, with newer model versions producing harder tasks for older ones.
arXiv:2610.30047v1 Announce Type: cross Abstract: Coding agents are typically evaluated using one-shot, well-formulated prompts. Benchmarks such as SWE-bench use existing GitHub issues as input for a...
SWE-Journey is a new benchmark designed to evaluate coding assistants in more realistic settings, focusing on long-horizon tasks and multi-turn interactions. It introduces a weak-to-strong synthesis pipeline to create extended coding challenges and a user-simulation agent based on four real user personas to mimic authentic code-assistance dialogues. The benchmark reveals that while models succeed on over 75% of tests with software architects, they manage less than 25% with non-coders, highlighting a significant gap in current assistant capabilities.
arXiv:2605.29442v2 Announce Type: replace-cross Abstract: AI coding agents increasingly act directly within software environments, yet existing analyses of their failures rely on benchmark trajectori...
WatchPoint is a simulated‑user system that generates and runs diagnostic scripts against a live web application, producing structured observations to guide coding model retries. Unlike prior methods that rely on screenshots or non‑executable metrics, WatchPoint operates on Web‑Bench—a benchmark of 50 multi‑file web projects with 1,000 sequential tasks verified by deterministic end‑to‑end tests. It recovers 57.6% of diagnosed tasks, matching a human tester’s 54.5% recovery rate, and identifies when such simulated feedback is beneficial or should be withheld.
arXiv:2606. 19613v1 Announce Type: cross Abstract: We introduce StaminaBench, a benchmark that measures the stamina of coding agents: how many consecutive interaction turns (change requests) they can handle before failing.
arXiv:2604.20779v2 Announce Type: replace Abstract: AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful...
arXiv:2609.14992v1 Announce Type: new Abstract: Recently, the rapid development of large language models (LLMs) has reshaped software engineering by enabling autonomous code agents that plan, execute...