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New SWE-Journey benchmark evaluates coding assistants on realistic tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SWE-Journey, a new benchmark designed to more realistically evaluate coding assistants like Claude Code and Codex. This benchmark addresses limitations in existing evaluations by focusing on long-horizon tasks and multi-turn interactions, which are crucial for real-world software development. The system uses a weak-to-strong synthesis pipeline to create extended coding tasks and simulates realistic user interactions based on four distinct personas. Initial results indicate that while assistants perform well with software architects, they struggle significantly with non-coders, highlighting a gap in their ability to support users with less technical expertise. AI

IMPACT This benchmark could lead to more robust and user-friendly coding assistants by highlighting current limitations in handling complex, interactive tasks.

RANK_REASON The item is a research paper introducing a new benchmark for evaluating AI coding assistants. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SWE-Journey benchmark evaluates coding assistants on realistic tasks

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The item is a research paper introducing a new benchmark for evaluating AI coding assistants. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Xuebo Liu ·

    SWE-Journey: Towards More Realistic Evaluation of Coding Assistants through Long-Horizon, Multi-Turn Interaction

    Coding assistants such as Claude Code and Codex have become a major application of LLM agents, yet existing benchmarks remain far from real-world use, particularly in task horizon and interaction length. Code assistants require completing long chains of development work in contin…

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