arXiv AI

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

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
arXiv AI
Sep 30

From Dead Code and Static Requirements to Working Engines: Software Revival with Coding Agents

The paper introduces ReviveBench, a benchmark designed to evaluate coding agents’ ability to revive non‑running software and reconstruct industrial engines from open specifications. It comprises two families of tasks—revival (ten tasks addressing dependency issues, missing modules, legacy builds, and GPU models) and reconstruction (thirteen tasks covering numerical, geometric, hardware, and transactional systems). The benchmark uses hidden verifiers calibrated against native environments, engineering tools, or reference implementations, and the authors report that the strongest evaluated model passes all revival tasks and most reconstruction tasks, while also uncovering verifier defects that highlight measurement error in executable verification.

By Tianyu Liu, Dingyuan Dai, Yufan Du, Zhen Yang
arXiv AI
Oct 28

Beyond QASecClaw: Agent Scaling, Verifier Feedback, and Evidence Reliability in Software Assurance

The study investigates whether adding specialist language‑model contexts can enhance software assurance when deterministic tool evidence is already available. Five experiments were conducted using Qwen2.5‑Coder‑7B‑Instruct across Java warning controls, OWASP Benchmark, Defects4J test generation, SV‑COMP assertion verdicts, and misleading evidence packages. Results show that increasing context count does not improve unsafe clearance, that verifier feedback can raise correct verdicts but also incorrect ones, and that models reject most counterexamples while scope checks classify packages accurately, supporting deterministic evidence admission but highlighting limits in verdict aggregation and feedback interpretation.

By S M Abdulla Al Mamun, Akif Islam, Mohd. Ruhul Ameen
arXiv AI
Sep 18

Chronicle: Cut-Point Replay for Regression Testing of LLM Agents

Chronicle introduces a method called cut‑point replay to make regression testing of large language model (LLM) agents reproducible. It records an agent’s run at non‑deterministic boundaries as immutable envelopes and then replays selected boundaries while executing the rest live, enabling continuous‑integration tests that detect faulty code changes. Benchmarks show minimal overhead, perfect bit‑stability, and effective detection of unsafe actions in a mutation study.

By Tisha Chawla, Susheem Koul
arXiv AI
Sep 21

GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions

arXiv:2609.21562v1 Announce Type: cross Abstract: Coding agents can modify and test code across large software projects. Game development is a domain where agents must implement gameplay rules. A gam...

By Xinyu Che, Yunfei Ge, Shihao Li, Yanchen Liu, Hang Yan, Xinping Lei, Yanghai Wang, Zixuan Dong, Yifan Yao, Qianqian Xie, Letian Zhu, Jiaheng Liu
arXiv AI
Sep 7

The History Is the Detector: Executing CVE Patch History, End-to-End

The paper introduces BUGSTONE‑E2E, a framework that converts vulnerability history into executable detection rules and validates them. It mines reusable rules from fixing commits, organizes them by CWE and language, and applies a funnel‑shaped pipeline that starts with lightweight analysis and culminates in LLM‑guided inspection, runtime verification, and patch generation. Using 19,325 high‑severity CVEs, the system identified 2,710 fixing commits, created 1,033 detection rules across 56 CWE families, and produced runtime evidence for 644 findings in 14 programs.

By Qiushi Wu, Kevin Eykholt, Youngja Park, Xiaokui Shu, Dhilung Kirat, Douglas Lee Schales, Ian Molloy