A Hybrid Neuro-Symbolic BDI Multi-Agent Architecture for LLM-Based Unit Test Generation

AAMAS 2026 Workshop EMAS Submission56 Authors

Keywords: Multi-agent systems, LLM-powered software testing, Coverage-driven generation, JaCaMo, CArtAgO, JUnit, JaCoCo, Test oracle inference

TL;DR: An iterative multi-agent architecture using LLMs and JaCaMo to enhance Java unit test coverage through deterministic path targeting and execution feedback.

Abstract: Automating unit test generation with Large Language Models (LLMs) has shown great potential, yet standard generative approaches often struggle with systematic path exploration and the effective integration of deterministic execution feedback. This paper introduces a hybrid neuro-symbolic multi-agent architecture, developed within the JaCaMo framework, where agents follow a Belief–Desire–Intention (BDI) deliberation cycle to perform goal-directed test generation, that re-frames unit test generation as a goal-directed, agentic search process. By combining the creative reasoning of LLMs with the formal precision of symbolic analysis, the architecture utilizes a society of role-specialized agents and artifacts to transform coverage gaps into symbolic "Logic Hints". This creates an iterative neuro-symbolic feedback loop capable of resolving complex branch conditions and navigating deep logic paths that typically stagnate one-shot prompting methods. Evaluation across seven benchmarks shows that our architecture achieves a 100% success rate in reaching the targeted coverage threshold, consistently outperforming all baselines. Statistical tests (Friedman, Wilcoxon) confirm superior reliability and search efficiency with large effect sizes ($A_{12} > 0.8$), proving that structured agentic autonomy effectively bridges the gap between LLM reasoning and formal software testing requirements.

Paper Type: Regular paper

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Submission Number: 56