arXiv AI By Andras L. Ferenczi, Mariya Bessonov, Allwyn Ponnudurai, Chandrakanth R. Mamillapalli, Krishna K. Maddula, Krishna K. Lingamneni

Certifying Repeatable Entry-Replay Witnesses for COBOL-to-Java Migration

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arXiv AI
1d ago

AgentTrustCI: Build-Time Policy, Grounding, and Trajectory Tests for Java LLM Agents

AgentTrustCI is a Java/JUnit 5 framework that turns application‑layer expectations—such as tool selection, grounding of answers, and adherence to workflows—into policy, grounding, and trajectory checks over recorded traces. It provides deterministic replay for repeatable build‑time tests and a judge interface for optional semantic evaluation. In tests across 55 synthetic cases from seven enterprise domains, the full pipeline correctly classified all injected faults and passing controls, while single‑layer configurations handled 35 cases, demonstrating the framework’s coverage and repeatability.

By Sachin Gupta
arXiv AI
Aug 25

SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?

The paper introduces SWE Refactor Bench, a benchmark of 20 whole‑repository migrations aimed at testing whether coding agents can autonomously perform long‑horizon stack migrations. It employs a three‑stage evaluation—Migration Audit, Behavioural Tests, and Agentic Verification—to assess both migration completeness and behavioural correctness, revealing that only 5.4 % of runs succeed across all stages. The study shows that agents struggle with migration tasks, especially language rewrites, and highlights the need for more robust coding‑agent capabilities.

By Deyao Hong, Yizhe Chi, Wenyi Li, Xiaoqiu Wang, Mingju Gao, Kaisen Yang, Bingxiang He, Youjie Zheng, Calvin Xiao, Qinhuai Na