SeGa detects 29 of 60 real business-logic bugs by generating tests from requirement-document scenarios, 22-25 more than four LLM-based baselines.
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cs.SE 3years
2026 3representative citing papers
A multi-agent LLM-based framework extracts knowledge graphs from 50 real Ethernet switch manuals with 0.97-0.99 correctness to enable downstream test case specification generation.
Agent-generated tests mainly act as observational feedback channels and do not meaningfully improve issue resolution success in current LLM software engineering agents.
citing papers explorer
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Uncovering Business Logic Bugs via Semantics-Driven Unit Test Generation
SeGa detects 29 of 60 real business-logic bugs by generating tests from requirement-document scenarios, 22-25 more than four LLM-based baselines.
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Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems
A multi-agent LLM-based framework extracts knowledge graphs from 50 real Ethernet switch manuals with 0.97-0.99 correctness to enable downstream test case specification generation.
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Rethinking the Value of Agent-Generated Tests for LLM-Based Software Engineering Agents
Agent-generated tests mainly act as observational feedback channels and do not meaningfully improve issue resolution success in current LLM software engineering agents.