This guide examines the role of automation in end-to-end QA testing, with a particular focus on the impact of AI-centric adoption. It serves as a practical roadmap to help you develop a strategy for transitioning from the traditional way to AI-driven Quality Assurance.

Additionally, we will discuss the terminology used to represent a full-scale integration with GitLab, closely examining Playwright and QA.tech solutions; each one suitable at different stages of the CI/CD evolution.

By the end of this guide, you will be able to meticulously plan each milestone with a clear definition of the action points needed to move toward a resilient model.

What Is the Evolution of QA?

The present testing landscape in the software industry, can be divided into three main categories:

1. Traditional QA

Considered a gatekeeper in the Waterfall Model for controlling the quality of each software release. It translates to detecting faultiness at the end of the cycle since testing is allocated after the development stage by design.

2. QA Automation CI/CD

This method is characterized by the beginning of Shift-Left Testing, where team collaboration is encouraged to improve the product’s quality. The impact is due to strategic placement of test checkpoints throughout the many stages of the pipeline, based on script-based task automation.

3. AI-Powered QA

The latest stage of QA evolution chain, places machine learning (ML) and large language model (LLM) at the epicenter of the product management’s roadmap letting AI to take over responsibility of both decision-making and coding tasks. The impact is a speed boost favoring faster deliveries, reduction in critical issues and false positives while improving the accuracy of the testing process.

Let’s take a closer look at each step.

Traditional QA

In the traditional way of testing software, the process of spotting potential problems while tracking the outcomes could only rely on a series of manual instructions executed by the responsible QA tester.

QA plays a role by the end of the SDLC, way after both the design and codebase foundations have been established. This monolithic approach not only challenges an eventual model overhaul, with drastic consequences on productivity metrics, but also condemns a lengthier timeline until the delivery date.

Occasionally scripted instructions can be seen, however manually executed by a QA member. Albeit the slight reduction in time not having to redo the same task repetitively, it is a proven ground that ad hoc interactions facing major manual execution of tests are the norm.

QA Automation CI/CD

Now jumping into more modern times, we see the adoption of DevOps practices more and more common, applying an Agile philosophy behind the technical decisions. A CI pipeline constitutes the backbone of the release iteration phasing the build, test and deployment of applications altogether in an endless loop of production.

With QA automation CI/CD, comes another concept called continuous testing (CT) – the conception of strategically placing test cases in selected checkpoints. The main goal is to contribute to an early detection of problems.

This CT flows automatically waits on a code merge in order to initiate a new pipeline run, supervising the quality of the code and ensuring the application works as intended. All contributors to the codebase receive immediate feedback, assisting them in debugging plus, addressing issues earlier before the release.

Playwright is a versatile end-to-end testing tool with a unified API for cross-browser testing. We will use it to demonstrate how to streamline test cases into GitLab CI while maintaining a realistic scenario.

The versatility of Playwright can be compared to a Swiss Army knife in the realm of pocket knives. Such a metaphor highlights its ability to adapt to various testing contexts.

Playwright Testing in GitLab CI

Learn how to generate a test case from scratch, review a report, and integrate it into GitLab’s pipeline following this layout.

Build a Test Case

Ensure that you have Node.js and Playwright installed. Then, generate code using the *codegen* feature.

npx playwright codegen

Use “record” and “pick locator” to edit the script.

Locating elements of a page with the help of the ‘pick locator’ tool

After scripting the test case, run the test in headless mode.

npx playwright test example.spec.ts

A report will be provided at the end of the run, offering a visual and more detailed output from each defined test object.

npx playwright show-report

Up to this point, you have successfully generated a test case and produced an HTML-based report.

Integration with GitLab CI

Upon building a test case, it is time to integrate it as a job in your GitLab repository’s pipeline.

Edit the .gitlab-ci.yml file under the root directory of your repository.

Introduce the following variables to unroll the automation sequence and enable artifact collection.

stages:
  - test

playwright_tests:
  stage: test
  image: mcr.microsoft.com/playwright:v1.53.2-noble
  variables:
    CI: "true"
  script:
    - npm ci
    - npx playwright install --with-deps
    - npx playwright test
  artifacts:
    when: always
    paths:
      - playwright-report/
    expire_in: 1 week
  • Commit and submit the file to the remote repository. It will automatically initiate a new pipeline run, which you may follow the progress until it concludes.
  • Artifacts will be collected and made available in the Artifacts section inside Build drilldown.

Job artifacts displayed through GitLab UI

These steps are briefly outlined in this guide to help you in creating a roadmap for the ideal CI integration. Please refer to the official documentation for an in-depth exploration of the topic.

Limitations of QA Automation CI/CD

Research shows that ever-increasing complexity in maintaining reliable source code, combined with pressure to meet delivery timelines and challenges like upskilling and smart scaling, present significant obstacles to overcome in this model.

Here are some variables that impose a constraint to a sustainable long-term plan:

  1. Multiple toolchain integration
  2. Test flakiness
  3. Infrastructure maintenance
  4. False positives
  5. Balancing manual with automated testing
  6. Learning curve
  7. Compatibility with legacy systems
  8. High costs of manual test creation and maintenance

In summary, it can be stated that variables such as false positives, maintenance, and limited adaptability are barriers to progress.

AI-Powered QA

Let’s be clear – merging a mesh of automation tools with diverse language frames into the same product roadmap, is no easy task to accomplish.

As cloud hosting rents increase, strategic planning is paramount to keep unnecessary risks at bay. We are looking at crucial elements such as version mismatching or limited support, backporting and patching, maintaining a tenant can lead to a serious impact that can void your commitment with deadlines.

To deliver quality software while diminishing risk factors, you may ask, “What is the best approach to tackle these issues?”. The answer lies in planning your next move towards AI-driven lifecycle CI/CD to foster product innovation.

Introducing QA.tech – A Leader in Transformation

QA.tech’s autonomous testing solution enables next-level CI/CD with a comprehensive set of features, including discovery through machine learning, generative code for applications, and a predictable simulation nature.

The QA.tech key signature model is defined by an AI autonomous testing model, which involves a blend of AI development and human oversight. By reducing human intervention, it offloads the most strenuous of workloads into the algorithm’s capability to explore, plan, execute, and evaluate test scores.

Onboarding is fairly easy thanks to the smart wizard that quickly initiates test cases. Setting up the account and landing the initial project requires just a few clicks of interacting with the UI.

How to Onboard Your CI Workflow with QA.tech

The onboarding process starts with opening a new project and creating the initial set of test cases. Follow these instructions to ensure a successful onboarding:

  • Navigate to Test Cases link located on the left side pane, and Add Test Case.
  • A pop-up window will reveal three options:
    • Choose an automatically generated test by the AI discovery mechanism.
    • Create more tests utilizing the web crawler.
    • Create your own tailor-made test by describing the goal and the expected output in return.

Simple but consistent, creating a test case

Interacting with the test case can be done relying on the instruction set or adding more stages to the case with the help of generative AI.

What to Expect?

By adopting an autonomous approach to QA testing, teams can increase their focus on user experience-related matters, particularly those concerning potential entanglements within the UI flow, promoting a coherent navigation experience.

Embracing AI-driven autonomous testing promotes enhanced feedback loops within shorter periods. The smartly generated suggestions mark a clear departure from static messages embedded in the code, a trademark from the script-based techniques applied in a common DevOps streamline. These reports will indicate which areas can be improved, offering concise explanations with the expected outcomes.

Grouping test cases into a single scenario acts like a filter of information, aggregating related tests into a singular module ready to be scrutinized by the AI algorithm.

What are the Main Features of QA.tech?

QA.tech’s web testing tool demonstrates unique traits that are beneficial to your Left-Shift testing plan. These are the fundamental traits from QA.tech autonomous tester:

  • An array of CI/CD integrations, including GitLab CI. This offers a straightforward path to edit the .gitlab-ci.yml file. You are required to place the QA_TECH_API_TOKEN API token into the CICD secret variables from the GitLab project to enable authentication.
  • Autonomous discovery reveals key aspects to be tested in the web application. By scanning and simulating real-user interactions with the application, it can generate a comprehensive test collection right from the start
  • AI’s contribution by automatically generate test cases strongly pursuits a brief testing scenario without overly extensive planning. This is achieved by probing the web application and determine which scenarios are the most suitable.
  • Documentation is covered by gathering results, such as screenshots, videos, network and console logs. This process contributes to an automated version of structured documentation.
  • Bug detection is amplified by the AI’s capacity to rapidly change and adapt. Moreover, code revision and user interaction simulation are key attributes for identifying major flaws.

Reviewing a test result

What to Keep in Mind When Adopting a New Model?

Transformation brings a set of new challenges to the present business model. Plan strategically and move decisively, accumulating experience, documenting outcomes from previous lessons learned along the way.

Here’s what to be mindful of:

  • Identify incoming variables Approach change with awareness, recognizing that novelty introduces new factors into the equation. Allow events to unfold and monitor them closely. Feedback loops are essential for coordinating a common solution to implement.
  • Supervise and monitor The QA team has the final say when revising the provided content. Practices should adhere to compliance principles. Maintain an open-minded approach, also fostering creativity.
  • Shift in the mindset Managerial decisions should be particularly sensitive to allow transformation to be captivating, promoting synergies between teams throughout the impacted areas of business. These decision-making factors should promote quality and inspire trustworthiness.

Machine learning intends to alleviate workloads from human hands, opening a door to more creative and cooperative ways to work.

Final Thoughts

The path for perfecting your product can be smooth and thoughtful in order to achieve excellence.

Generating additional cases will ease the scalability of your project and record historical data to support machine learning workflows. Streamlining AI-driven processes into the CI/CD pipeline will favor the release lifecycle without sacrificing quality.

Get ready for lowering costs and be rewarded with an increase in end-user satisfaction, plus a reduction in human inaccuracies.

Unlock the potential of your project by partnering with the best option in the market today!