Performance Testing with AI | Jmeter | Lighthouse | K6

Install Apache Maven by downloading the latest 3.9.6 zip, unzip it, set the Maven home environment variable, add the bin location to PATH, and verify installation with mvn -v.

Install Visual Studio Code on Windows 11, import the repository, configure Java projects with Maven and the Java debugger, and enable extensions like GitHub Actions for continuous testing and integration.

install Node.js and npm by downloading from nodejs.org and running the prebuilt installer; verify with node -v and npm -v, then ensure PATH includes node and npm.

Test the performance of network, page load, printing, boot time, cryptography, web services, and encoding/decoding. Choose what to test based on project context and industry.

Avoid testing performance only when the product is stable; test continuously during development to catch issues early, signal problems in specific methods, and prevent team disruption.

Start performance testing with unknown user counts by referencing industry benchmarks like 2.5–3 seconds page load. Compare targets to top competitors and assess login and responsiveness using common sense.

Identify target app and hosting environment, select a test tool, and define test scenarios. Enable monitoring and debugging with Azure insights, and ensure infrastructure and test data support load testing.

Learn to measure response time in performance testing, targeting under 3 seconds for web pages and under 0.3 seconds for microservices, and break down round-trip time into connection and processing.

Measure cpu and ram during infrastructure performance tests and know when to stop; distinguish cpu-intensive from memory-intensive workloads and use 90% cpu or 95% memory thresholds.

Simulate user traffic with tools like k6 and loadrunner to test APIs, front-end, back-end, and databases, measuring response times and resource usage across loads.

Visualize the performance test pyramid guiding testing from low-level functions to APIs and end-to-end scenarios, including scalability considerations, with the bulk of tests close to development for speed and feedback.

Explore how load testing benchmarks system performance under a constant load, using ramp up, steady load, and ramp down to identify bottlenecks and optimize with 100 to 250 users.

Perform a short K6 load test from Grafana to simulate 1200 users over 30 seconds, capture http 200 checks, 80 ms average latency, and observe CPU and memory trends.

Perform stress testing by pushing beyond the steady state to reveal the system's limits, evaluate response time, memory, and CPU behavior, and validate graceful shutdown, throttling, and chaos engineering.

Endurance testing validates long-duration stability under sustained load. Spanning hours to days, it reveals memory leaks, RAM growth, disk usage, and capacity needs for monitoring, alerting, and CI/CD readiness.

Demonstrate automatic node scaling under varying loads to ensure capacity matches demand. See how performance testing reveals app scalability and downscaling behavior as load rises and falls.

Learn how spike testing measures a platform's ability to absorb sudden short-term load bursts and maintain scalable performance during peak demand.

Explore volume testing to measure how apps handle huge data, from petabyte-scale warehouses to millions of documents, videos, and complex searches, ensuring fast, reliable processing.

Use Google Lighthouse to analyze Yahoo's desktop page, revealing performance, accessibility, best practices, and SEO insights to improve load times and revenue.

Explore preconditions for self-adapting load scenarios, using given–when–then bdd to map user stories to acceptance tests, and set up production analytics and real-time monitoring to trace user journeys and engagement.

Combine acceptance testing with performance testing using BDD, tie in real user analytics, and let AI map behavior into prioritized given-when-then test cases for end-to-end performance.

Explore an on-prem performance testing setup with three Windows VMs on a VMware layer and container orchestration, contrasting cloud-hosted apps with a local load generator for isolation and cost insights.

Explore cloud-based load testing with SaaS providers, where you simulate traffic from multiple geographies on hosted infrastructure you don’t manage, paying only for used virtual users via a simple GUI.

Demonstrates cloud-based load testing with Grafana k6 by configuring virtual users and geographic distribution. Monitors Azure performance with HTTP 200 checks and low latency, most responses under 100 ms.

Explore k6.io for lightweight, JavaScript-based performance testing integrated into development, with cloud and local runs, 21 load zones, end-to-end testing, fault injection, and monitoring via Prometheus and Datadog.

Explore K6 browser testing as a browser-based, headless load testing tool, compatible with the Playwright API, for quick smoke tests and experiments in your performance testing workflow.

Run your first automated performance test with k6 by creating a test.js script, launching it with ten virtual users for 30 seconds, and reviewing results.

Assess the performance test report by reading data received, data sent, and average response time. Clarify iteration counts, virtual users, and percentiles (90th, 95th) to evaluate server performance.

Use AI to understand the results file from performance testing, asking GPT to explain each report item and export the results to CSV for analysis.

Explore how performance testing contrasts requests per second with virtual users, compares synchronous and asynchronous load, and shows how median and average reveal response-time distributions.

Interpret performance results visually by plotting load versus response time and filtering the csv for readability. Prefer Grafana Cloud reporting, or, if needed, use ChatGPT as a last resort.

Run your performance tests in the cloud from VS Code using k6 cloud, configure environment variables with your API key, and monitor results in Grafana Cloud.

Demonstrates running k6 tests from GitHub into Grafana Cloud, using a Grafana API token in GitHub secrets, and comparing CI/CD results with in-cloud performance dashboards.

Explore how Google Lighthouse measures loading performance from the user’s perspective across devices, and access it via pagespeed.web.dev or browser extensions.

Run a Google Lighthouse assessment to measure performance, accessibility, best practices, and SEO, loading the page multiple times to produce final results and check mobile-first behavior.

Explore how Google Lighthouse computes a performance score by weighting metrics such as first contentful paint, largest contentful paint, total blocking time, cumulative layout shift, and speed index.

Explore Google Lighthouse scoring and metrics such as first contentful paint, speed index, total blocking time, and largest contentful paint. Learn the threshold guidelines that influence performance and seo.

Learn to interpret a Google Lighthouse report by examining the tree map and coverage to identify unused JavaScript and speed up pages. Optimize for mobile, accessibility, and SEO.

Don't obsess over performance scores from tools like Google Lighthouse, PageSpeed, Yellow Labs, and GTmetrix; they vary. If the app feels fast, prioritize user experience and SEO over metrics.

Install Google Lighthouse locally, run headless tests from the command line, and generate HTML reports to compare desktop and mobile performance, accessibility, SEO, and best practices.

Integrate Google Lighthouse into GitHub Actions to automate performance validations within a ci cd workflow, configure a Lighthouse check in a yaml file, and publish reports.

Trace the end-to-end round trip from browser to databases to identify bottlenecks, aiming for a 3–4 second response, and test components step by step.

Learn how throttling affects performance testing by disabling or monitoring it to avoid false results, verify correct handling of denied requests, and compare test scenarios.

Explore how caching layers—from browser to database, content delivery networks, and memory—boost performance, and why testing with caching disabled first then gradually enabling layers provides realistic measurements.

Explore how sticky sessions affect performance in distributed clusters, explaining session replication, load balancers, and why disabling sticky sessions can improve testing outcomes.

Explore how database indexes transform slow disk lookups into fast RAM searches, reducing full table scans and boosting performance up to 20x with proper indexing on name and id.

Learn how logging levels from tracing to fatal affect disk space, CPU, and network during performance testing, and when to use minimal logs in production.