Quality assurance is a key part in any software development project. In agile-based development, which is highly iterative, the role of QA becomes even more crucial. Today’s article will focus on creating and scaling an agile QA process.
You will see many examples and suggested tools that enable QA automation and scaling so that you can select the right one for your QA needs. We will also discuss the challenges in scaling the QA process and how to combat them.
Let’s start with understanding the role of QA in the agile world.
The Role of Testing and QA in Agile Development
In an agile-based workflow, QA processes are integrated with all the stages of the software development lifecycle (SDLC) instead of just testing at the end of the development. Agile makes product quality more important bug fixing. Quality is everyone’s responsibility and testing occurs throughout development. Agile-based QA aims to prevent issues instead of fixing them. Frequent communication within cross-functional teams is also an important aspect of the QA process in Agile.
Some key techniques used in agile-based QA include continuous testing, test-driven development (TDD), acceptance test-driven development and automated testing.
The Importance of Early and Continuous Testing
Agile software development promotes early and continuous testing. It helps find bugs early in development, saving time and improving program quality. Continuous testing speeds up feedback in the CI/CD workflow as well.
One of the techniques for early and continuous testing is exploratory testing. It is a risk-based, critical-thinking testing technique that lets testers use their knowledge of risks, implementation details and client needs. Knowing these things early in testing helps developers and QA engineers uncover issues quickly and thoroughly.
Agile testing also uses continuous integration. It involves frequent code integration into a shared repository. Automated builds and tests verify the integrations to find faults early in the development cycle and keep software ready for release all the time.
Building a Scalable Testing Strategy
Creating an agile QA process starts with the right foundation. Let’s explore your potential testing strategy starting with the right tools and frameworks.
Evaluating and Selecting Appropriate Test Automation Tools and Frameworks
Successful test automation requires the selection of the right tools and frameworks. Your application’s technological stack, team capabilities, types of testing (unit, integration, functional, performance, etc.) and budget determine the tools to use.
For example, for end-to-end testing of a JavaScript-based web application, you may use Cypress or WebDriverIO. PyTest and Selenium WebDriver are good options for unit and UI testing Python apps. Use JMeter or Gatling for performance testing.
Developing Automated Test Scripts and Test Suites
After choosing your tools and frameworks, start creating automated tests. Test scripts inform the tool what to do, what to use and what to expect.
For example, with Selenium WebDriver, you may develop a test script that opens a web browser, navigates to your application, logs in with a test user, navigates to a specified page, fills out a form, submits it and checks for the anticipated confirmation message.
Establishing a Robust Test Automation Infrastructure
A reliable test automation infrastructure is essential for accurate results. This includes test systems or virtual machines, software and tools and the process for executing these tests and handling their results.
A continuous integration (CI) server like Jenkins or a cloud-based service like Travis CI or GitHub actions can automatically execute your tests every time code is committed to your repository. Sauce Labs or BrowserStack can perform your tests on several browser and OS combinations.
The below diagram shows the exact steps of test automation.
Ensuring the Availability of Suitable Test Environments
Software testing must simulate the production environment to catch issues before releasing the application. Production hardware, software and networks must be as close to the staging/test environment as possible. Here are two key points to achieve this:
- Set up a development, QA, staging and production test environment approach instead of just a development and production environment.
- Incorporate version control, automated build and deployment to ensure environment consistency. GitHub and GitHub actions are some examples.
- Synchronize the production data with the staging environment on a regular basis.
Managing test data and ensuring data privacy and security
Test data management entails creating, managing and supplying production-like data sets without exposing sensitive data. Let’s discuss some techniques below.
- Data anonymization can hide personal data while preserving its structure and relationships. I use IBM Infosphere Optim and Oracle Data Masking for this.
- I create reusable test data sets that can be shared across different teams.
- Incorporate data generation tools because performance and load testing require large data sets. Some example tools are Mockaroo and GenRocket.
- Use data lifecycle management to remove or archive test data if not needed.
Incorporating Techniques for Test Environment and Data Provisioning
Efficient provisioning of test environments and data speeds up testing and reduces setup and maintenance time. Some techniques in this regard are the following:
- Use containerization, as containers can quickly create and destroy portable, lightweight,
- and consistent test environments. Docker and Kubernetes are some common examples.
- Use Terraform or another IaC tool to automate test environment development and configuration.
- Automate development and testing with CI/CD pipelines. Jenkins, GitHub actions, and CircleCI are examples in this regard.
Collaboration and Communication
Facilitating close collaboration between development, DevOps and quality assurance teams is essential. JIRA, Trello or GitLab are good options here.
Establishing effective communication channels and practices
The team must establish effective communication channels, such as daily standups and sprint planning meetings. Utilize collaboration platforms such as Slack, Microsoft Teams or Discord for daily stand-ups and problem-solving. For documentation, use Confluence or Google Docs.
Emphasizing the importance of shared understanding and knowledge sharing
In modern software development, cross-functional teams are very common. It is inevitable for teams to convene regular knowledge-sharing sessions, or “brown bag sessions”, and ensure the team’s access to project documentation. Confluence for project documentation, GitHub for code and technical details, Google Docs for collaboration and an internal Wiki for knowledge sharing are frequently used tools in this regard.
Agile Testing Practices
Now that we’ve laid a foundation, let’s look at the elements that allow. youto build an agile QA process.
Test-Driven Development (TDD)
You cannot force developers to write tests first. Instead, you should make them realize the benefits of TDD.
First, writing your tests gives you a plan for your code. It also lets you test your code right away. That’s test-first development. Some developers might think that TDD will increase the overall project length. While this may be true, its benefits outweigh its disadvantages. The end result is a robust and bug-free application.
According to this survey, 92% of the developers participating in the research believed TDD produced higher quality products, while 79% believed it promoted simpler design.
Continuous Integration (CI)
Continuous Integration ensures code quality by automating the process of creating, testing and validating the codebase as different developers push their code. The process of CI ensures that new changes integrate with the existing code early and frequently so any breaking code changes can be caught and fixed immediately instead of doing it at the sprint end. Some important features of CI are the following:
Automated builds: After repository updates, CI procedures build the code automatically. This quickly detects code integration issues, reducing code conflict bugs. If two developers change the same code, the CI system will quickly build and test the combined code, revealing conflicts.
Automated testing: CI pipelines test the codebase to ensure new changes don’t disrupt the functionality or cause regressions. Developers can easily fix mistakes, enhancing code quality and reliability. Every time you change code, Jenkins or TravisCI can run unit, integration and performance tests.
Fast feedback: CI gives developers immediate feedback. Instead of waiting for a development cycle, developers may fix issues immediately. It not only reduces technical debt but also simplifies code maintenance.
Code review and collaboration: CI fosters code review and teamwork. Integrating code updates allows team members to review and enhance each other’s work. Collaboration enhances code quality, ownership and advancement.
Enforcing coding standards and best practices: CI tools enforce code standards. JavaScript ESLint and Python Flake8 check code for style, mistakes and maintainability. CI enforces these standards, keeping your codebase clean, uniform and clear.
Continuous Testing (CT)
Continuous Testing (CT) is essential to modern software development practices, specifically those following the DevOps model. Constant testing helps identify the issues early in development, reducing the cost of fixing bugs.
Let’s break down the process of implementing continuous testing practices with automated pipelines:
- Solid Base of Test Cases: Develop a comprehensive set of reliable test cases covering your application’s functionality.
- Choose the Right Tools: Select appropriate tools for continuous testing, like Jenkins, GitLab CI/CD or Travis CI.
- Set up the Pipeline: Configure your tool to monitor your code repository, automate builds and execute your test suite upon each build.
- Integrate testing in the CI/CD Pipeline: Make sure your pipeline triggers a series of tests on every code commit and subsequent build.
- Handle Test Failures: Ensure the pipeline notifies the developers about failed builds, providing enough information for troubleshooting.
- Review and Refine: Regularly assess and improve your testing pipeline to match your evolving codebase.
Timely Feedback
By enforcing a “Red-Green-Refactor” cycle, Test-Driven Development (TDD) emphasizes early feedback and rapid bug detection. Developers create a failing test (“Red”) for a new feature or repair, then write just enough code to pass that test (“Green”), and then refine the code without changing its behavior (“Refactor”). This loop ensures that problems are identified and addressed promptly, making their resolution simpler and faster. TDD is facilitated by tools such as JUnit, NUnit, Jest, Jenkins and Travis CI that orchestrate testing and provide continuous feedback. Consequently, TDD provides immediate insights into the health of the code and expedites problem resolution.
Scaling the Testing Process
The iterative nature of Agile development typically results in a greater demand for testing. Test automation is one approach to solving this issue. In automated testing, software tools and frameworks are used to repeatedly conduct system tests. Unit tests, integration tests and system tests can all be automated with the help of frameworks like Selenium, JUnit and TestNG. Since Agile teams work in short sprints and make rapid changes to the codebase, automated tests are essential since they can be done much faster and more frequently than manual tests.
The world quality report 2020 specifies the benefits realized from test automation in its comprehensive report. Here is a glimpse from this report showing the enormous benefits of test automation.
Parallel Testing and Distributed Testing
We can investigate approaches like parallel testing and distributed testing to speed up the testing process even more. Parallel testing is a method of speeding up the testing process by executing many tests simultaneously. This can speed up the execution of lengthy test suites that would otherwise have to be executed one at a time. I also use distributed testing, in which tests are executed simultaneously across numerous computers.
Scaling Test Environments, Resources and Infrastructure
The testing infrastructure, resources and environments must evolve as new tests are developed. Methods like Infrastructure as Code (IaC) can be used to handle this expansion efficiently. In IaC, infrastructure is handled like software applications are: provided and maintained via code. As a result, test environments may be set up and broken down in a uniform, repeatable fashion.
IaC can be implemented with the help of tools like Terraform, Ansible, and AWS CloudFormation. Scaling your testing infrastructure can be considerably aided by the ability of these technologies to automatically create, alter and delete environments. This article explains in detail how testers can achieve automation excellence by using IaC.
Common Challenges in Testing and QA Processes
Scaling testing and QA presents many challenges. These include test data management, test automation, communication and collaboration.
- Test Data Management: Your product will grow and require increasingly sophisticated datasets. This data will be hard to maintain, so you will need to make use of synthetic data and data masking techniques. Tools like Delphix and Tonic can develop, manage and anonymize test data, assuring quality and security.
- Test Automation: Setting up an automated testing pipeline is difficult, yet test automation can boost testing efficiency. Tools like Selenium, Junit, Jest, and Cypress can do unit, integration and end-to-end tests. Automation should supplement manual testing and not rely solely on either manual testing or automation testing. I cannot automate all scenarios.
- Communication and Collaboration: As the team grows, communication becomes harder but more crucial. Agile teams should use tools like JIRA, Slack and MS Teams to measure progress and foster frequent communication between cross-functional team members. I maintain documentation to encourage knowledge sharing.
Best Practices and Practical Tips for Success
Here are some of the techniques to scale your QA process:
- Adopt CI/CD: Continuous Integration and Continuous Deployment (CI/CD) practices enable the regular integration and rapid deployment of changes. Jenkins, CircleCI and Travis CI are examples of CI/CD pipeline support tools.
- Implement IaC: Infrastructure as Code (IaC) automates the configuration and administration of your test environments. Utilizing IaC tools such as Ansible, Terraform or Chef makes replicating environments, resolving issues and managing versions simpler.
- Adopt Shift-Left Testing: This entails integrating testing early in the development cycle to identify bugs when they are simpler and less expensive to fix. This is an essential Agile and DevOps strategy.
- Consistent Retrospectives: Continuous refinement is the foundation of Agile. Convene retrospective meetings frequently to identify what is working and what is not. Utilize these observations to modify and enhance your processes.
- Prioritize Testability: When designing and implementing new features, always consider how the testing will happen for this feature/bug. This may involve decomposing features into smaller, testable components or implementing TDD practices.
Conclusion
Scaling agile testing is challenging, but a well-planned strategy and sound practices make it possible and beneficial. Understanding agile testing concepts, developing a scalable testing strategy, integrating agile practices such as test-driven development (TDD) and continuous integration (CI) and effectively managing rising testing requirements enable organizations to construct a robust agile testing framework. However, each business encounters unique obstacles. The best practices and ideas presented in this article serve as a guide, but the success of agile testing depends on adapting them to an organization’s objectives and circumstances. With high-quality software, satisfied customers and an efficient agile team, the arduous journey is worthwhile.
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