STARWEST 2026 - Developer
Customize your STARWEST 2026 experience with sessions for software developers.
Monday, September 21
Testing from the Inside: AI-Assisted Unit Testing Edition
NewWant to level up your testing and development skills while harnessing the power of AI? In today’s environments, shifting left is more important than ever to catch bugs early and accelerate delivery. Traditional software testing teaches you to think outside the box from a user’s perspective—but some of the best insights come from looking inside the box, analyzing the code itself, and applying AI to make testing faster and smarter. Join Tariq King as he walks you through the fundamentals of program-based testing, now enhanced with AI assistance. Learn how to apply techniques such as testing...
Test Automation: How to Start and Succeed
Many organizations invest a lot of effort in test automation at the system level but then have serious problems as their product matures and changes over time. As a leader, how can you ensure that your new automation efforts will get off to a good start? What can you do to ensure that your automation work provides continuing added value? Chris Loder will explain the critical issues you need to know to get a good start, and he will share his extensive experience in building great automation. He covers the most important management issues you should address for test automation success,...
Become an AI Power User
Impostering a bit in the AI-verse? Overwhelmed by daily AI announcements? Unsure you're using AI most effectively? Tiny bit of FOMO? Jeremiah has you covered! In this workshop, he'll help you become an AI Power User. Become a boss at your job, whatever your role or industry! He'll show you where AI shines and where you'll want to be careful, plus toss you lots of hands-on practice. In the time together, Jeremiah will help you pinpoint YOUR niche, build a custom AI assistant, and develop a comms strategy to show off your new skills. You'll walk out with cutting-edge knowledge, a...
Become Your Company's Quality Consultant
Companies of all sizes face difficulties in achieving higher quality standards within their organization. As quality engineering includes various roles and activities, it is also challenging to find the right people to analyze the current state from a wide perspective and provide the recommendations that will allow these organizations to mature their teams, improving their DevOps culture in the process. Join Péter Földházi as he illustrates the knowledge and skills necessary to become your company's quality consultant. Péter's goal is to involve engineers from beginner to advanced levels...
A Quality Engineering Introduction to AI and Machine Learning
Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...
Testing AI Systems That Refuse to Sit Still: Practical Evals, Red Teaming, and Oversight for AI Agents
NewModern AI systems don’t behave like traditional software. The same prompt can produce different outputs, models can drift without code changes, and AI agents may hallucinate, misuse tools, leak context, or confidently invent facts while appearing completely functional. In this hands-on tutorial, Jeremiah Marble will show attendees how to test and harden modern AI systems using practical, lightweight techniques teams can apply immediately. Participants will build tiny AI agents, intentionally break them through prompt injection, unsafe outputs, hallucinations, and memory drift, then create...
Cursor and Claude Code for Test Automation Engineers (Advanced)
NewYou have the basics of AI tools covered, now let’s push them to the limits. In this advanced, hands-on workshop, we’ll go beyond vibe-coding and explore how to use AI tooling as a strategic orchestration tool for test automation engineers. Suitable for QA Engineers, Test Automation Engineers, Frontend Developers, and DevOps Engineers; we’ll dive deep into advanced techniques like multi-agent reasoning for debugging, building robust end-to-end tests, maintaining long-context conversations without drift, and crafting reusable automation patterns. We’ll be building custom tooling and agents...
Building Apps and Tests Together with AI: Agentic Spec-Driven Development
NewWhat if you could turn ideas into working software with tests to prove it, using AI as your collaborator? This hands-on half-day tutorial introduces agentic spec-driven development, a practical approach for building web apps and test automation together with AI coding agents like Claude, Cursor, Copilot, or Codex. Designed for testers and anyone involved in software development, this session shows how to define a clear project context and rules. You will write feature specs as structured markdown with user stories, design decisions, and acceptance criteria. Then you will use AI to generate...
Test Smarter, Not Harder: How to Design Test Suites for Continuous Delivery
Imagine: as soon as any developed functionality is submitted into the code repository, it is automatically subjected to the appropriate battery of tests and then released straight into the wild. Setting up the pipeline to do just that has become commonplace, but most organizations hit the same stumbling block: just what IS the appropriate battery of tests? Automated build pipelines don't always lend themselves well to the traditional stages of testing. In this hands-on tutorial, Melissa will introduce testers to the key principles of test case and test suite design that apply to...
Tuesday, September 22
Agentic AI: From Rules to Reasoning
NewAI agents have existed for decades, but generative AI has fundamentally changed what agents can do and how they are designed and built. Come and explore the evolution of AI agents across two major waves. First, learn the foundations of Agentic AI through agents built using rules, heuristics, and traditional machine learning, examining where these approaches excel and why they struggle with complexity, ambiguity, and scale. Then dive into the second wave of agents powered by generative AI and multimodal large language models. These modern agents can reason, plan, use tools, and interact...
Testing on the Right: Lessons in Monitoring and Observability
Observability has exploded onto the software engineering zeitgeist over the last five years, and for a good reason. However, it suffers from being misunderstood and sometimes equated with a closely related subject—monitoring. This confusion is compounded by the fact that some of the existing tools and frameworks just adopted a lot of the observability terminology in just the letter of the word, not the intent. Not having a solid grasp on the basics of observability is becoming unacceptable in the world of effective software quality engineering. Kaushal Dalvi shares his experiences in the...
Wednesday, September 23
Telemetry at Scale: Lessons from Building Observability for Distributed Systems
Modern distributed systems fail in messy, non-obvious ways: a small latency spike in one microservice can cascade through queues, sidecars, gateways, and control planes, yet traditional logging and isolated dashboards rarely reveal the true root cause. In this talk, Sneha will share how Microsoft tackled this while building the telemetry and observability platform behind Azure Container Apps and the Aspire Dashboard, used across thousands of customer environments. They standardized on OpenTelemetry to unify traces, metrics, and logs across heterogeneous workloads, invested in consistent...
How Testers Can Break AI: Practical Techniques to Find Bias, Hallucinations, and Accessibility
As AI-powered features (especially generative AI) are rapidly integrated into modern software, testing teams face a critical challenge. Traditional testing approaches focus on correctness and performance but fail to uncover ethical risks such as bias, hallucinations, and accessibility regressions. In real projects, this has led to AI systems that technically “work” yet exclude users, generate misleading outputs, or erode trust. In this talk, Aditi addresses this gap by reframing AI quality as a testable concern and applying practical, tester-led techniques rather than data science-heavy...
Evaluating Agentic LLM Apps: Beyond Vibes
"It seems to work" isn't a deployment strategy. As AI agents move from demos to production, teams discover that traditional software testing falls apart — outputs are non-deterministic, "correct" is subjective, and yesterday's perfect prompt fails mysteriously today. This talk tackles the unique challenges of verifying agentic applications. Rushabh will explore why agent evaluation is fundamentally harder than traditional ML testing: multi-step reasoning chains, tool use side effects, and the compounding uncertainty problem. You'll learn practical approaches to building evaluation datasets...
Test-Driven Thinking in an AI-dominated World
AI code-generation delivers on speed, but teams working in medical, financial, transportation, and other high-risk domains face a dilemma: when the AI writes both code and tests, how do you know it hasn't hallucinated away a critical edge case? "Vibe coding" through iterative prompts leaves product advocates, testers, and developers uncertain whether all critical scenarios have been covered. Rob Myers shares practical approaches from teams using AI-augmented development with Test-Driven Development and Behavior-Driven Development. You'll see how to leverage a natural human strength—people...
Thursday, September 24
Testing AI Systems That Learn in Production: From Static Test Cases to Continuous Validation
As organizations increasingly deploy AI and machine learning systems into production, testing practices built for static, rule-based software are no longer sufficient. Unlike traditional applications, AI systems learn from data, change behavior over time, and are sensitive to data drift, bias, and feedback loops, making defects harder to detect with conventional test cases. This session presents a practical, experience-driven approach to testing AI systems across the full lifecycle, from model development to live deployment. Drawing on real-world implementations and applied research, the...