Introducing automation test scripts at the app development phase offers indisputable advantages in today’s dynamic environment. By adopting test automation, organizations can optimize their workflows, produce high-quality applications quickly and efficiently. Swift identification of codebase regressions allows for prompt bug resolution, enabling developers to prioritize valuable tasks over firefighting. Additionally, it instills a confidence in teams to deliver exciting new features to customers consistently. However, the effectiveness of these advantages depends on the diligent maintenance of the test suite.
Test automation maintenance is an essential responsibility to ensure sustained success. Tests must be meticulously adjusted to align with changes in the application’s landscape as it evolves through codebase modifications and system updates. Whether this involves crafting new test scripts or modifying existing ones, test automation maintenance is critical for validating new features, preventing recurring bugs, and maintaining a smooth workflow. Neglecting this crucial aspect is equivalent to hindering progress, both in the present and future.
Types Of Maintenance Testing
There are two types of maintenance testing that every tester must perform when working on maintaining tests:
Confirmation Maintenance Testing
This form of maintenance mobile app testing verifies the updated code. You must monitor every minor and major change in the code while performing this type of maintenance testing to guarantee there are no functionality or downtime concerns.
Regression Maintenance Testing
This kind follows the confirmation maintenance testing phase and focuses on testing the current functionality. It checks the entire software after the code update to ensure that the current functionality is not affected. It’s known as regression testing.
Ways to Setup and How to Maintain Automation Scripts
Clear Test Data Management Approach
One of the primary challenges encountered by test engineers revolves around managing test data in automated tests, particularly in end-to-end test automation. The volatility of data often leads to script failures. To mitigate this, it becomes crucial to refresh the database, update its status, and execute scripts each time changes are applied to the application. This fundamental approach is effective for a few test suites, applications, and environments.
An alternative strategy to prevent such issues is generating a new set of data with every script run. A well-defined test data management approach proves instrumental in sustaining the test automation suite and plays a pivotal role in achieving a tangible return on investment.
Regular health checks of automation suites
Automation suites can be affected by even minor changes if the test engineer is unaware of them. To pre-empt challenges during test execution, it is advisable to conduct regular health checks on the automation suites. Routine assessments help ensure that automation suites operate as expected, delivering optimal results.
Impact analysis of any new enhancement in the application
Impact analysis stands as a crucial element in responsible Requirements Management, offering a precise comprehension of the repercussions of a suggested change in an application and its potential effects. This understanding aids teams in making informed business decisions regarding the approval of pertinent changes.
The analysis scrutinizes the proposed change to identify modules requiring creation, modification, or rejection, as well as to estimate the effort needed to implement the change. While neglecting impact analysis does not alter the task’s magnitude, it is advisable to steer clear of unexpected scope expansions. In light of the analysis, QA should be strategically planned to update affected automated scripts within the respective sprints.
Automate at API level/ lesser UI automation
Automated UI tests continue to be the prevalent type of automation conducted by test engineers, favoured by most agile teams. Nevertheless, after a few months of initiating automation, teams frequently recognize that the maintenance cost of UI-level tests outweighs the benefits gained from automation. As a result, test engineers are recommended to pursue automation at the API level. Automating the application at the API level not only reduces the total cost of ownership (TCO) but also enhances the resilience of scripts by operating at the application code level.
How to update test data and environment for automation testing?
Ensuring the synchronization of test data and the mobile app testing environment with modifications in the production environment and the application under test is crucial for automation testing. Regular updates to test data and the environment should be conducted in accordance with the frequency and extent of releases, bug fixes, and feature updates. Employing version control, continuous integration/continuous delivery (CI/CD), and test data refresh are all effective strategies for achieving this.
Version control systems such as Git, SVN, or TFS enable tracking and management of changes in test data and environment files. CI/CD pipelines, exemplified by tools like Jenkins, Bamboo, or Azure DevOps, can automate the processes of building, testing, and deploying your application. Test data refresh involves the removal or archiving of outdated test data, which is then replaced with new or updated test data. The method chosen, whether through scripts, tools, or manual processes, depends on the scale and complexity of the test data.
How to optimize test data and environment for automation testing?
Enhancing the efficiency, effectiveness, and scalability of test data and environments for automation testing involves the process of optimization tailored to your testing objectives. Regular assessments should be conducted to scrutinize the usage, quality, and impact of test data and environments, aiming to identify any gaps, issues, or opportunities for improvement. Various techniques can be employed to optimize test data and environments, including data subsetting, data masking, data compression, environment cloning, and environment parallelization.
Data subsetting entails selecting only relevant or essential data while discarding unnecessary portions. Data masking involves replacing sensitive or confidential data with fictitious or anonymized equivalents. Data compression reduces the storage space or bandwidth required for test data by encoding or shrinking it. Environment cloning is the creation of multiple copies of a test environment through the replication or duplication of its configuration and settings. Environment parallelization allows for the simultaneous execution of multiple tests on different test environments by distributing or splitting the test workload.
Final Thoughts
Robust automation test scripts are built with long-term maintenance in consideration. Regardless of the effort invested in test automation, ongoing maintenance remains imperative as the application under test undergoes changes and advancements. While organizing and refining tests may at times seem like a time-consuming task, it consistently proves to be a worthwhile investment. Teams neglecting the importance of crafting maintainable tests initially may soon find themselves dedicating more time to fixing broken tests or contemplating the abandonment of the entire test suite.
Maintaining an automated test suite doesn’t have to be an excessively time-consuming endeavor. By incorporating good practices into your test automation strategy from the outset, you can ensure that your tests remain in excellent condition over time.
