arXiv AI
Is Agent Code Less Maintainable Than Human Code?
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Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality
arXiv:2607. 03691v2 Announce Type: replace-cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.
arXiv AI
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How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
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...
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Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering
arXiv:2606. 17799v1 Announce Type: cross Abstract: Coding agents have become a major mode of software engineering, but the benchmarks we use to compare them were designed in a pre-agent era: they collapse model, harness, and environment into a single end-to-end score, typically computed against one reference solution, with no component-level signal for iteration.
arXiv AI
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SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents
arXiv:2609.08149v1 Announce Type: new Abstract: SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analys...
arXiv Machine Learning
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Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments
arXiv:2607. 28591v1 Announce Type: cross Abstract: Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation.
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Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments
Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation. Each task must couple a realistic software state with a specification, development tools, and reliable verification.