Automated Self-Testing as a Quality Gate: Evidence-Driven Release Management for LLM Applications
2026-03-18T08:51:52Z•457528060cf115d6c6bdf1af259272309f24684f29192e0ddf529a0eccf2fc5d
LLM applicationsLoosely-Structured SoftwareSATDSEMAGVIBEPASSVibeContractautomated testingbackbone algorithmscode generationcontract-driven QAdatasets mapengineering design datasetsevidence coverageevidence-based gatesfault-triggering test generationlatency monitoringmulti-agent systemsrelease governanceruntime evolutionsafety testingscientific software technical debt','code review','human-AI codeself-evolutionary agentssoftware quality assurancevariability modelsvibe coding
What happened
Collection of recent research on engineering, QA, and governance for LLM-driven and agentic software. Key contributions include: an automated self-testing quality-gate framework for LLM applications that uses evidence-based PROMOTE/HOLD/ROLLBACK decisions across five dimensions (task success, context preservation, P95 latency, safety pass rate, evidence coverage); Loosely-Structured Software (LSS) principles for managing runtime-generated entropy in multi-agent systems; VibeContract, a contract-based QA paradigm for vibe coding that decomposes intent into verifiable task-level contracts; SEMAG
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_se
- Record identifier
- 457528060cf115d6c6bdf1af259272309f24684f29192e0ddf529a0eccf2fc5d
- Enrichment time
- 2026-03-18T08:51:52Z
- AI-assisted enrichment
- Yes
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