Regression Test Suite Maintenance In Scalable Test Environments

 

Regression Test Suite Maintenance In Scalable Test Environments

Maintaining a robust and effective regression test suite is both crucial and challenging, particularly in scalable test environments. This article delves into the insight that can streamline the process and boost efficiency. A pivotal aspect of this is cross-browser testing, a procedure that assesses the compatibility of an application across different web browsers to ensure consistent functionality and user experience. This step is not just important but also time-consuming and complex due to the vast number of browser-device-OS combinations in the world.

Regression Test Suite Maintenance In Scalable Test Environments

Luckily, innovative platforms like LambdaTest are stepping in to transform scenarios. With its cloud-based digital experience testing platform, LambdaTest allows for seamless testing across 3000+ test  environments, minimizing the hassle of maintaining your regression test suite and proving itself an essential tool in any software developer's arsenal. With this, let’s understand regression test suite maintenance in more detail.

What Is Regression Test Suite?

A regression test suite is an assembly of test scenarios focusing on the numerous essential functionalities of the software. Existing functional tests, unit tests, integration tests, and previously executed test cases usually serve as the foundation for building regression suites.

Ways To Maintain Regression Test Suite In Scalable Test Environments

Maintaining a regression test suite in scalable test environments requires a proactive and systematic approach to ensure its effectiveness and efficiency. Here are some essential ways to achieve this:

Rethink Your Test Automation Strategy

A significant reason for challenges in maintaining regression testing is the tendency of our tests to execute too many actions and verifications simultaneously, often navigating user flows within our application that may not be necessary. These kinds of tests, often referred to as end-to-end (e2e) tests, are inherently hard to maintain, slow, and prone to breaking. They often represent a significant time sink in regression maintenance.

The first step in developing any regression suite should always involve reducing the number of e2e tests and increasing the number of functional tests. Functional tests (also known as feature tests) are essentially the opposite of e2e tests. They allow us to confirm that a particular feature or implementation in the application is working as expected. Given that an application consists of hundreds of small implementations, testing each of them separately always ensures coverage, maintainability, and reliable results regarding the application's state.

Consider this example - Scenario: User downloads a bill

●      User logs into the application

●      User creates a new bill

●      User opens the created bill

●      User selects the download option

●      Then the bill is downloaded

This test is a typical example of a 'flaky test.' The goal here is to verify the 'Download' functionality of the application, with an invoice acting as a precondition. However, is it necessary to create a new invoice to download it? What if the invoice creation implementation fails? In such a scenario, not only would a potential 'Create Invoice' test fail, but this test would fail as well because the invoice creation, which is not the aim of this test but a precondition, failed. It's crucial to minimize these unnecessary preconditions as much as possible.

Therefore, the previous example could be modified like this - Scenario: User downloads a bill

●      User logs into the application

●      User opens bill 'X1023'

●      User selects the download option

●      Then the bill is downloaded

In this assumed scenario, it's still necessary to open the invoice to download it. So, we have to do this before choosing the download option. However, in this case, the invoice is already present in the system where we're running the test, thereby minimizing the chance of a precondition causing our test to fail.

Dedicated Test Environment And Dataset For Testing Process

It's crucial to underscore the immense benefits that come from running regression tests in a specialized environment coupled with a targeted dataset. These components offer considerable advantages:

Specialized Environment:

a. Consistency in test execution within a controlled setting.

b. A separate environment allows for code isolation and validation of application behaviour, ensuring no external factors or activities impact the outcomes.

c. It enables a near-perfect emulation of a production environment for testing, which assures the authenticity of the results obtained.

Dataset:

a. Command over the data utilized in each test execution.

b. Utilizing a dataset prevents tests from having to generate data for validation, which diminishes the likelihood of result alteration due to an error during test data creation.

These components establish the foundational groundwork upon which any regression tests should be conducted in scalable test environment conditions. Furthermore, for ease, both the environment and the dataset can be implemented through automated tasks in services such as Jenkins or Gitlab. This allows for a 'clean' state before each execution of the regression tests.

Leverage API Calls To Enhance Your Tests

It's often overlooked, but integrating API calls can significantly enhance UI tests. At times, these are outright avoided under the premise of adhering to user-experience faithful UI tests. While UI tests should indeed mimic actual user interactions as closely as possible, attention needs to be paid to preconditions, as they can introduce unexpected errors that may compromise our test validation. Also, it's likely that other tests in our regression suite already validate what would be the preconditions in further tests.

Incorporating API calls into our UI tests not only expedites test execution but also doesn't alter the application's behaviour. Behind every form interacting with the Backend, an endpoint call is made by passing specific information to it. We can directly utilize the same endpoint by transmitting the information we would usually send through the form. Consider this scenario: To verify a user's account in the system, the account has to be new, meaning we can't use a previously stored user account.

Scenario: User verifies a newly created account

●      User opens the sign-up form

●      User submits the sign-up form

●      User logs into the application with the newly created user

●      User verifies the account

●      Then the user sees a verification completed message

To create a new account, the user accesses the registration form, fills in all the required fields, and submits the information to generate the new user. Knowing what information we send via the endpoint, we can redefine the test as follows:

Scenario: User verifies a created account - Given a random account is created with the following data:

●      | field     | value                     |

●      | firstname | John                       |

●      | lastname  | Doe                        |

●      | email     | test+random_number@company.com |

●      | password  | Test123456                 |

●      User logs into the application with the created user

●      User verifies the account

●      Then the user sees a verification completed message

The Given step endpoint will call the same one used in the registration form, and the information used is identical to what would be transmitted via the form. After this step, we get the generated email which the user would use to access the system. Although implementing this API call in the automation framework might initially appear complex, the benefits of leveraging the application's API for UI testing far outweigh the initial framework preparation investment. Furthermore, it also enables backend testing in the same framework, a substantial advantage for ensuring the system API functions correctly.

Engage With Developers For A Uniform Locator Strategy: IDs Over XPath

Among various methods to select UI elements of an application, IDs and XPath are the most prominent. IDs serve as unique identifiers for the application's UI elements. When accurately defined, IDs are unchanging names, easily accessible and utilized by primary software testing tools such as Selenium. However, they demand explicit definition and maintenance from the development team.

XPath (XML Path Language), on the other hand, is a language that enables the creation of expressions to traverse and process an XML document. The application's DOM tree elements are constructed as an XML document, and tools like Selenium let us select the application's elements using this structure. This process doesn't require the explicit involvement of the development team, allowing swift test automation. Yet, using XPath comes with its challenges.

For instance, minor changes in the UI could alter the Absolute XPath Expression of a UI element, causing it to fail. In short, XPath locators can easily "break" with any alteration in your application's structure, whether that involves changing components, adding new elements like checkboxes, or modifying form text on the app's 'front.' Each change may lead to test failure and increase maintenance time for adjusting the selectors.

In contrast, the IDs introduced by the development team make the selectors far more robust and resilient, unaffected by any layout changes, be it editing form elements or adding new ones. Undeniably, effective communication between QA and development teams is crucial for creating reliable and robust tests. Therefore, we should request the development team to define identifiers (IDs) for all elements we interact with in our tests.

Leverage Metrics And Feedback To Refine Your Regression Testing

It's vital to use metrics and feedback to evaluate and enhance our regression testing procedures and their results. Metrics such as defect density, defect detection rate, test coverage, test execution time, and test automation rate can be operated to calculate our regression test suite's usefulness and efficiency. Stakeholders, customers, and user feedback also help identify areas where improvement and satisfaction are needed in our regression testing. In fact, tools like dashboards, reports, and analytics become very helpful to visualize and express metrics plus feedback with others.

Remember, your regression test suite is not some fixed entity. It's dynamic and continuously evolving. It's required to regularly review and revise your regression test suite to align with the targeted software system's current state and requirements. Techniques like test suite evaluation, test case maintenance, and test suite optimization can be used to pinpoint and remove outdated, duplicative, or ineffective test cases and to add or modify test cases to adjust new or modified features or functionalities.

Automation In Your Regression Test Suite

Automation is a compulsory component for maintaining a factual regression test suite. It can help enhance the consistency and reliability of our test results while saving us a significant amount of time, effort, and resources. Automation tools and frameworks, like TestNG, Cucumber, Selenium, or Robot Framework, can be used to develop and execute our all regression test scripts. Additionally, we can go for continuous integration and continuous delivery (CI/CD) tools, like GitLab, Jenkins, or Azure DevOps, to embody our regression testing into our software development lifecycle. This lets our tests run automatically whenever some sort of changes or deployments are made in the code.

Incorporate Manual Efforts

Subsequently, develop a set of tests for the fundamental functionality throughout the application. These often involve workflows that most automated tools struggle to handle due to the intricate and integrated interactions involved. While this basic functional regression test suite incorporates workflows that may not be critical to the application's function, they are typically frequently used workflows by end users.

Manual regression tests might encompass exploratory tests surrounding the test case that automation wouldn't cover. Other manual regression tests could cover end-to-end or system workflows that are lengthy and complex — these are typically not ideal for automation. Basic functional regression test suites assist in maintaining your code clean and generating positive responses from customers towards your application. Plus help significantly in scaling test environments.

To Wrap Up

As software systems evolve, precisely the same way the corresponding test suites should, too. So that they can accurately, efficiently, and effectively evaluate the performance and functionality of the system. Adopting the techniques discussed above can help you achieve your goals.

Furthermore, the usage of machine learning and AI can also enhance test suite maintenance. Because it's not only about catching bugs and downsizing errors; it's about continuously enhancing the quality of software products, meeting user expectations, and contributing thoroughly to the success of businesses. Remember, a well-maintained regression test suite is the cornerstone of a powerful, scalable, and successful test environment.