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Xin Peng

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Book Open access Oct 2026

Verifier-in-the-Loop LLM Solving of Heap Constraints for Concolic Execution

Concolic execution systematically explores program paths by solving symbolic constraints, but in object-oriented software its practical effectiveness is often limited by heap constraints over objects and heap structure, such as nullness, aliasing, runtime types, and field reachability. These constraints form a systemat...

Shao-Ran Xia, Le-Yi Cheng, Cai-Hua Dong et al. · 0 citations
Book Open access Oct 2026

Enhancing Trace-Based Root Cause Analysis for Microservice Systems via Code Change Understanding

Microservice architecture has become the dominant design pattern for modern web applications. However, the dynamic structure, complex inter-service invocations, and continuous updates of microservices make root cause analysis (RCA) particularly challenging. Existing RCA methods typically rely on runtime monitoring data...

Min Zhang, Chen-Xi Zhang, Sen-Yu Xie et al. · 0 citations
Preprint Aug 2026

OpenHarmony Bench: Evaluating LLMs and Coding Agents on OpenHarmony App Development

We present OPENHARMONY BENCH, an app-level coding benchmark for evaluating LLM-based coding agents on OpenHarmony ArkTS applications. Unlike function-level benchmarks, it evaluates complete app-level changes: each task requires an agent to modify a buildable ArkTS project so that a requested behavior works end to end,...

Li Li, Han Hu, Tian-Jian Zhang et al. · 0 citations
Open access Oct 2026

Poirot: Automatic Root Cause Analysis of Safety Violations in ADS Simulation Testing via Hypothetical Reasoning

With the rapid development of autonomous driving systems (ADSs), it has become critical to ensure their operational safety, leading to the widespread adoption of simulation testing. While existing scenario-based simulation testing approaches have demonstrated effectiveness in detecting safety violations, they often fal...

You Lu, Ding-Ji Wang, Kun Zhang et al. · 0 citations
Preprint Aug 2026

DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents

Results show that DocsChisel improves the task success rate of LLM agents by 95.89% over the original tool documentation and by 75.15%, on average, over existing baselines, while incurring limited optimization time and token overhead.

You Lu, Kun Zhang, Bi-Huan Chen et al. · 0 citations
Preprint Aug 2026

FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows

Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures. However, constructing high-quality agentic workflows remains largely manual and requires substantial domain e...

Hao Shuo, Lu You, Bi-Huan Chen et al. · 0 citations

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