Systems | Development | Analytics | API | Testing

AI is reshaping software testing, but real quality does not come from hype alone. This session explores where AI truly adds value in automation and where human testers remain essential. It focuses on building a practical partnership between AI and testers, where speed and scale are balanced with judgment, context, and responsibility.

Learn more: speedscale.com.

Learn more: speedscale.com.

As software systems grow more complex and business cycles demand faster releases, traditional approaches to quality engineering are no longer enough. AI brings a new dimension to the Software Development Life Cycle—augmenting decision-making, predicting risks, automating quality checks, and continuously assuring value delivery. Armed with GenAI and Agents we can reshape how we think about software quality. This discussion explores practical ways to embed AI across the SDLC to accelerate delivery, reduce risk, and achieve a step-change in quality outcomes.

Retrieval-Augmented Generation can sound convincing while still being wrong. This session focuses on moving beyond surface-level metrics and turning stochastic AI outputs into evidence-backed, verifiable results. It explores how to test the entire RAG pipeline, from ingestion and indexing to retrieval, grounding, and answerability, ensuring every response is traceable to the right source, policy, and user context.

Private AI is emerging as a strong alternative for teams that need the power of AI without compromising data privacy or compliance. This session explores how local LLMs can support software development and test automation when cloud-based tools are not an option. Using setups like Ollama with models such as Qwen 2.5 and integrations like the Continue plugin, the talk demonstrates how secure, on-device AI can accelerate tasks like BDD creation, automation scripting, and performance testing.