Case Study · QA Automation
A Playwright test automation framework wired into Jira, Zephyr and CI/CD
An enterprise software provider ships business-critical web apps to many customers, and manual regression testing was slowing every release. AMT built a reusable Playwright framework, linked every automated result to its Zephyr test case and Jira story, and ran the suite in CI/CD with quality gates before production.
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At a glance
- Client
- Enterprise software provider building and maintaining business-critical web applications for multiple customers
- What AMT built
- End-to-end QA automation framework with test management, defect traceability, CI/CD execution and dashboards
- Test types
- Functional, regression, smoke, end-to-end, API
- Tools
- Playwright, TypeScript, Jira, Zephyr, Jenkins or GitHub Actions, Allure
- Browsers
- Chromium, Firefox, WebKit
- Result
- Less manual testing, faster release validation, earlier defect detection, full traceability from requirement to defect
The short version
Releases were frequent, workflows were complex and regression testing was mostly manual. AMT built a Playwright framework using the Page Object Model, data-driven tests and parallel execution. Every automated run reports into Zephyr, every failure links to a Jira story with screenshots and trace files, and smoke tests run automatically after each deployment. Quality gates stop failing builds before they reach production.
The problem
- Manual testing dependency. Repetitive regression testing took a lot of effort and slowed releases.
- Growing coverage. Every enhancement added scenarios to validate.
- Inconsistent execution. Manual runs varied and missed defects.
- Weak traceability. It was hard to connect requirements, test cases, results and defects.
- Late defect detection. Issues showed up late, when they cost more to fix.
- Release bottlenecks. Testing became the constraint on shipping.
What AMT built
Playwright automation framework
- Functional and end-to-end tests for the web applications
- Cross-browser runs on Chromium, Firefox and WebKit
- Reusable scripts using the Page Object Model (POM)
- Data-driven tests that cover many scenarios from configurable datasets
- Automated smoke and regression suites
- Parallel execution to cut run time
- Screenshots, trace files and logs captured automatically for debugging
Jira and Zephyr test management
- One repository for manual and automated test cases
- Test cases mapped to Jira user stories and requirements
- Suites organised by module and release
- Execution status, coverage and defect history tracked in one place
- Playwright results attached to the matching Zephyr test cases
- Clear view of passed, failed and pending scenarios
Development, QA and project management now work from the same picture of quality.
Continuous testing in CI/CD
- Smoke tests run automatically after each deployment
- Regression runs as part of release validation
- Scheduled runs across configured environments
- Reports generated and published automatically
- Quality gates catch failures before production
Defect reporting and traceability
- Detailed results and failure logs
- Screenshots and trace files for every failed scenario
- Defects linked to Jira stories and test cases
- Historical execution results kept
- Visibility into defect resolution and regression checks
QA dashboards
| Dashboard shows | Why it matters |
|---|---|
| Execution summaries | Fast read on each run |
| Pass and fail status per case | Where to look first |
| Regression coverage | What is protected and what is not |
| Defect status | What is blocking release |
| Release-wise progress | Readiness for each release |
| History and trends | Whether quality is improving |
Ongoing maintenance
The framework evolves with the product. AMT updates scripts as the application changes, extends regression coverage for new features, investigates intermittent failures, and tunes the suite for faster feedback.
Results
- Less manual effort on repetitive regression and functional testing
- Faster release validation through parallel runs and CI/CD
- Repeatable coverage of critical workflows
- Defects caught earlier in the development cycle
- Traceability from requirement to test case to result to defect
- Shared reporting across development, QA and project teams
- A framework that scales with the product and release frequency
Technology stack
| Component | Technology |
|---|---|
| Test automation | Playwright |
| Language | TypeScript / JavaScript |
| Test architecture | Page Object Model (POM) |
| Test case management | Zephyr |
| Project and defect management | Jira |
| CI/CD | Jenkins / GitHub Actions |
| API testing | Playwright API testing / Postman |
| Reporting | Playwright HTML Reports / Allure |
| Version control | Git |
| Execution environment | Docker / CI runners |
Before you build something similar
- What runs on every deploy? Start with a short smoke suite that runs after each deployment. Expand from there.
- Can you trace a failure to a requirement? If test results do not link to stories, nobody knows what a red build actually breaks.
- Who fixes flaky tests? Assign it. A suite people stop trusting gets ignored.
- What stays manual? Exploratory and usability testing still need people. Automate the repetitive, stable and high-risk paths.
FAQ
Can Playwright integrate with Jira and Zephyr?
Yes. In this framework Playwright results are linked to their Zephyr test cases, and defects are linked to the related Jira stories, with screenshots and trace files attached.
Which browsers does Playwright test?
Chromium, Firefox and WebKit, which is Safari's engine.
How does test automation fit into CI/CD?
Smoke tests run automatically after each deployment, regression runs during release validation, and quality gates stop failing builds before production.
What is the Page Object Model?
A design pattern that keeps page structure and test logic separate, so one UI change means one update instead of fixing every test.