STARWEST 2026 - AI in Testing
Wednesday, September 23
The Quality Nervous System
The Quality Nervous System is a biologically inspired network where AI agents and humans operate symbiotically in a single adaptive system. AI agents continuously explore, learn, and execute in real time across software at machine speed, while humans provide the judgment, strategy, and purpose to assure outcomes align with user and business goals. AI partnering fundamentally changes how software is built. Humans now collaborate with systems that generate code, tests, insights, and behavior at unprecedented speed and volume. Continuous real-time results flood teams faster than they can...
Thursday, September 24
User-centricity for AI-assisted Test Engineers
Traditional software testing is fundamentally deterministic: the same inputs must always produce the same outputs. Yet many teams introduce AI into their testing without first defining the problem the AI is meant to solve, leading to brute-force experimentation and unreliable results. Google’s 2025 DORA report highlights that user-centricity is a prerequisite for AI success and that AI is most effective when it is pointed at a clear problem. This session explores how that insight applies technically to testing. Before AI can be used as a testing tool, it must first be tested and understood...
AI Tool Face Off!
Feeling overwhelmed by the sheer volume of AI tools promising to revolutionize quality assurance? Cut through the noise in this practical session designed to help you and your team identify which AI assistants actually deliver value for software testing. Brian will dive into a side-by-side comparison of GitHub Copilot, Google Gemini, and Claude Code across essential daily QA workflows: generating test cases directly from pull request changes and specification documents, constructing comprehensive test plans for new features, and asking strategic testing questions—such as how to approach...
Agentic Quality at Scale—Orchestrating a QA Swarm for Swift Delivery
As delivery cycles compress, single AI agents are not enough. The next leap is a coordinated swarm of specialized QA agents, each owning a slice of the quality lifecycle (requirements, test generation, execution intelligence, defect triage, and release decisions). This session shows how to design an agent operating model that scales across teams, products, and pipelines without losing trust, traceability, or control. This session will introduce a practical blueprint for deploying multiple cooperating AI agents across the SDLC, with clear boundaries, KPIs, and governance that align to...
RAG Testing That Holds Up: Evaluating LLMs for Faithfulness, Boundaries, and Trust
PreviewMany teams are adopting RAG to constrain LLMs to internal documents, policies, and knowledge bases, but “using RAG” does not guarantee trustworthy behavior. In practice, models still hallucinate, blend outside knowledge, ignore source boundaries, and produce confident answers that are not supported by retrieved evidence. Traditional test approaches (happy-path assertions, correctness spot checks, performance metrics) often miss these failures because the output reads plausibly correct. Drawing from real evaluation work on document-constrained enterprise systems, this session...