A Multi-Agent Benchmarking Framework for Evaluating the Performance of Large Language Models in Logic Programming

A Multi-Agent LLM Framework for Automated Software Testing

A multi-agent testing framework in which requirement analysis, test-case generation, sandboxed execution, and defect detection are delegated to four distinct agents, and in which detection rests on a composite rule combining the execution signal with the semantic judgment of a dedicated diagnostic agent.

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report

A comprehensive overview of the existing tools and frameworks for implementing MAS in software engineering and a set of lessons learned and challenges that can help researchers and practitioners to select a suitable MAS framework according to their needs are provided.

Metamorphic Testing of Multi-Agent LLM Systems: A Trace-Based Behavioral Oracle Framework

Multi-agent systems built on large language models (LLMs) are increasingly deployed for complex tasks requiring autonomous planning, tool use, and inter-agent coordination. However, the non-deterministic nature of LLM outputs and the emergent behavior arising from agent interactions render traditional test oracles ineffective, creating a critical gap in quality assurance for agentic AI. This work introduces MORPHAGENT, a framework designed to address the oracle problem in multi-agent LLM systems through trace-based behavioral analysis. Our contributions are threefold: (1) goal-preservation relations that verify consistent goal achievement under input perturbations, (2) coordination-consistency relations that validate inter-agent delegation and communication patterns under agent substitution and reordering, and (3) tool-use integrity relations that ensure semantic equivalence of tool invocation sequences under prompt paraphrasing. MorphAgent instruments agent execution to capture structured traces comprising planning steps, tool calls, message exchanges, and final outputs, then systematically applies metamorphic transformations and checks behavioral invariants without requiring ground-truth oracles. We evaluate the framework on four multi-agent benchmarks spanning code generation, research synthesis, customer service, and data analysis tasks, encompassing 2,840 source-followup execution pairs across three LLM backends. Results show that MORPHAGENT detects 82.0% of seeded behavioral faults, including 90.3% of coordination failures and 81.7% of goal-deviation faults, while maintaining a false positive rate of 6.1%. The framework uncovers 14 previously unreported behavioral anomalies in established multi-agent frameworks, demonstrating its practical utility for assuring agentic AI reliability. These results suggest that trace-based metamorphic testing can serve as a practical foundation for reliable validation of emerging agentic AI systems.

AI-Generated Code Is Not Reproducible (Yet): An Empirical Study of Execution Reliability in LLM-Based Coding Agents

AgentCodeReview: Implementation and Comprehensive Benchmark Evaluation of a Multi-Agent Framework for Explainable Code Review and Automated Bug Repair

The AgentCodeReview system is presented, a multi-agent system that is able to conduct explainable code review and automated bug repair by leveraging software engineering agents with different code review tasks and its utility and extensibility to the field of explainable AI in software quality assurance are demonstrated.

The Evaluation Context Protocol (ECP): A Portable Contract for AI Agent Evaluation

This paper proposes the Evaluation Context Protocol (ECP), an early-stage, vendor-neutral framework intended to act as a portable evaluation contract layer for agentic systems and describes an open-source reference implementation that includes adapters for LangChain, LlamaIndex, CrewAI, and PydanticAI.