Masterclass Shift Left: Accelerated Software Testing

Install node.js and npm from nodejs.org using the prebuilt installer. Verify installations with node -v and npm -v, and adjust environment variables PATH if needed.

Shift left moves testing earlier in the development life cycle, from ideation to production, aligning with test driven development and acceptance test driven development to prevent defects and cut costs.

Explore shift left actions across the software development life cycle, using operation feedback and user insights to drive preventive testing, automation, and test driven development.

Adopt a bottom-up shift-left strategy by automating small, fast unit tests on the developer machine to enable rapid feedback, before expanding to more complex UI tests.

demonstrates test driven development in java with maven tests, adding a triangle area calculation (height times base divided by two), and using failing tests to guide correct implementation.

Discover shift right by testing in production to monitor real user behavior, validate ideas with AB testing, feature flags, dark launches, and blue/green deployments.

Explore how test automation shifts left to accelerate delivery pipelines, using the test pyramid, unit and integration tests, and practical data strategies in a GitHub-driven automation pipeline.

Run integration tests after unit testing to verify connectivity to RESTful APIs, databases, queues, and file systems, using mocks or real services, without validating business logic.

Learn how contract testing validates communication between clients and servers across RESTful and SOAP APIs and microservices. Explore API gateways, JSON and XML payloads, and schema validation.

Showcase the test pyramid in Java by comparing unit, API, and UI tests on a sum endpoint, highlighting fast feedback and flaky UI.

Measure branch time and PR open time to drive small, fast delivery and reduce merge conflicts. Use rebasing and proactive code reviews to shift quality checks left during development.

Explore continuous integration, delivery, and deployment pipelines, including unit and integration tests, code scanners, pull requests, builds, artifacts, infrastructure changes, and automated promotions to QA, UAT, and production.

See how a GitHub workflow uses the super linter to scan code quality on feature branches, blocking the pipeline when issues arise.

Demonstrates a GitHub pipeline blocking deployment on failing unit tests, using a jquery-based calculator test suite validating add, subtract, multiply, and divide, and skipping steps via yaml and environment variables.

Explore the continuous deployment pipeline, contrasting long lived environments with short lived ones, and learn how trunk, release branches, and automated testing enable faster deployments while managing costs.

Adopt a multi-pipeline design—nightly build, feature environment, CI, and promote production—that balances fast deployments with a final human check for quality and risk control.

Compare the cost and speed of API tests versus UI automated tests, highlighting that API tests are faster, cheaper to maintain, and reflect backend and frontend interactions.

Leverage the feature environment on a feature branch to enable on demand, ephemeral testing with automated builds and deployments, delivering fast feedback and shift left testing.

Explore the end-to-end DevOps and automated environment validation within a continuous deployment pipeline, from code on feature branches to production monitoring and feedback.

Explore how performance testing fits into quality engineering and non-functional testing, and how it assesses speed, responsiveness, scalability, and stability under varying load and environment variables.

Explore what shift left means for performance testing by moving non-functional checks into development, testing on feature branches and in CI before merging to master.

Learn how to integrate performance testing into CI/CD with GitHub actions during pull requests. Explore code scanning, coverage, and automated checks that stop on failures and trigger deployments.

Demonstrates performance validations at the ci level with GitHub actions, integrating linting, unit tests, dependency scans, code coverage, and JMeter performance tests into a full ci/cd pipeline.

Transform a feature file into a cucumber file with project context. Copy and adapt generated code for compatibility with your test system, noting that the code is not yet proven.

Leverage AI to understand unfamiliar code by analyzing YAML configurations and GitHub actions, revealing item meanings, workflows, and scheduling options, then modify content to fit needs.

Explore how to assess unit test coverage using JaCoCo, generate and regenerate test classes with AI, and identify edge cases like division by zero to improve code reliability.

Select a free performance testing tool with no prior knowledge, like Apache JMeter. Ensure it supports HTTP and JSON payloads, runs in Docker, and integrates with GitHub Actions.

Install and verify Apache JMeter, prepare a REST post request, and create a first performance test script (.jmx) to exercise the API.

Generate random test data for load testing by configuring a json request with a random variable (min 1, max 100) across 20 users, 20s ramp up and 10 iterations.

Run performance tests from the command line using JMeter or another tool, configure the tool path, start the application server, and generate results for integration into a pipeline.

Create a docker container image to run a JMeter test plan. Use the Apache JMeter image and a Dockerfile to run tests and export results locally or in the cloud.

Run the test in a pipeline by triggering a manual workflow, monitor the performance test with JMeter on a web app, and review results for download and analysis.