STARWEST 2026 - Performance Testing & Monitoring
Monday, September 21
Automation Framework Essentials
Automation is critical in today’s software delivery lifecycle, and yet many organizations struggle to keep their automation running. How can we mitigate difficulties and get consistent automation runs and results we can trust? The secret is implementing a solid automation framework, but that isn’t as easy as it seems. Chris Loder has built several automation frameworks over his career and has learned what works—and, more importantly, what doesn’t. This tutorial will cover what an automation framework is, the benefits of having one, and the keys to a successful framework, including...
Tuesday, September 22
Holistic Performance Testing for Modern Applications
With the advent of frameworks like Angular, React, and Vue, the landscape of application performance has changed significantly in terms of testing and measurement. Gone are the days of measuring response time as a single value based on back-end performance. In modern web and mobile applications, additional layers need to be peeled apart at the front end to truly understand its performance characteristics. Traditional approaches to performance testing are no longer sufficient to provide a delightfully responsive user experience. Join Kaushal Dalvi as he details new developments in the...
AI-Driven API Test and Automation
In this tutorial, you’ll learn how to use GenAI to implement and test a REST API from scratch. Using Cursor as your development environment, you’ll be guided through a hands-on experience that combines powerful tools like Mocha, Supertest, k6, and GitHub Actions to implement automated testing and continuous integration. Under the guidance of Julio de Lima, you’ll first dive into essential REST API architecture concepts to build a solid foundation. Then, with the support of GenAI, you’ll generate and refine your API, create functional test cases, and automate them to validate behavior and...
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...
Quality and Testing Measures and Metrics
To be most effective, leaders—including development and testing managers, ScrumMasters, product owners, and IT managers—need metrics to help direct their efforts and make informed recommendations about the software’s release readiness and associated risks. Because one important evaluation activity is to “measure” the quality of the software, the progress and results of both development and testing must be measured. Collecting, analyzing, and using metrics are complicated because developers and testers often are concerned that the metrics will be used against them. Join Jeff Pierce as he...
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...
From Local to Cloud: Scaling Your Load Tests with AWS (Without Blowing the Budget)
Many teams begin load testing on a local machine or inside their own network, but quickly hit limits with CPU, bandwidth, realism, and scale. This session addresses the challenge of moving from local load testing to cloud-based execution in a practical, cost-conscious way using AWS. The session will walk through how to spin up EC2 instances as load generators, create and manage SSH keys, transfer and run tests remotely, and collect results without needing deep cloud expertise. You’ll learn how to use Spot Fleets to reduce costs, structure your test setup for repeatability, and safely...
Thursday, September 24
Testing Event-Driven Systems Without Losing Your Sanity: Practical Patterns for AWS Serverless and Asynchronous Workflows
Event-driven architectures promise speed and scale, but they also introduce testing pain: eventual consistency, non-deterministic timing, duplicated events, and failures that only appear in production. In this talk, Parthiban will share a practical, field-tested approach he has used while leading distributed teams building regulated FinTech workloads on AWS serverless components such as Lambda, EventBridge, Step Functions, SQS, and API Gateway. He’ll start with the common failure patterns that make traditional end-to-end testing brittle, slow, and expensive. Next, he will walk through how...
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...
LLM-Powered Observability for Modern Cloud Systems: Telemetry Reasoning, Incident Triage, and Faster Root-Cause Analysis
Modern cloud systems generate overwhelming volumes of telemetry—metrics, logs, traces, and events—yet incident response still relies on manual correlation, tribal knowledge, and brittle rule-based alerts. This work presents an approach to LLM-powered observability that augments traditional monitoring with telemetry reasoning to accelerate incident triage and root-cause analysis. Prashanthi proposes a pipeline that structures heterogeneous signals into a unified incident context, enriches them with service topology, deployment metadata, and SLO/SLA objectives, and guides engineers with...