Test automation is a powerful tool for improving the quality, speed, and coverage of software testing. However, it also comes with challenges, such as maintaining, debugging, and scaling the automated test scripts. How can you make your test automation reliable and avoid wasting time and resources on fixing broken tests? Here are some tips to help you achieve test automation reliability.
One of the first steps to make your test automation reliable is to use a consistent and clear naming convention for your test scripts, variables, functions, and data. This will help you and your team to easily identify, organize, and update the test cases, as well as avoid confusion and duplication. A good naming convention should follow the standards and best practices of your programming language, framework, and tool. It should also reflect the purpose, scope, and expected outcome of the test case.
If you start with API automation, your tests will automatically become more reliable. If this question is aimed at UI automation, carefully examine the flakiness reasons and decide whether it is worth fixing the causes. If so, fix them, if not, delete the tests. Constantly failing test suites will lead to less confidence and eventually complete ignorance of the results. Also apply practices that are common in application development: Code reviews, pair programming, TDD, clean code principles, etc.
Para deixar meus testes automatizados sempre confiáveis, eu fa?o quest?o de manter meus scripts atualizados, ajustando conforme minha aplica??o muda. Prefiro estruturas modulares e escolho identificadores fortes para os elementos na interface. Ah, e n?o esque?o de criar uma boa estratégia para lidar com imprevistos. Sempre rodo meus testes e integro tudo no processo contínuo de integra??o pra manter a consistência e confiabilidade.
Acredito que criando um algoritmo, mesclando os 6 passos mencionados, pois tem que haver por conven??o uma nomenclatura clara e objetiva entre as ferramentas de trabalho. Aplicando o modelo de objeto de página (POM): padr?o de design que ajuda a separar a lógica de teste dos elementos de interface do usuário. Tendo sempre uma revis?o e controle de versionamento das aplica??es. Obtendo um gerenciamento dos dados testados, tornando o teste confiável. Aplicando teste em ambientes diferentes para que n?o haja dúvida das rotinas e implementa??es feitas. Utilizando ferramentas que possam monitorar e alertar sobre falhas ou eficiência para os melhores resultados.
I've seen that consistent naming practices enhance code readability, making it easier for developers to comprehend, navigate and modify existing test scripts. Descriptive and meaningful names for test cases, variables, functions and files is crucial for enhancing code maintainability and overall project organization. It also promotes code consistency and reduces the likelihood of errors.
Naming conventions always play an important role in test automation. It's always good to have a standard for testing projects which keep you handy when doing scripting, using test repos, using test management tools and also in CI/CD pipelines. So, you don't have last minute surprises or errors.
The Page Object Model (POM) is a design pattern that helps you to separate the test logic from the user interface elements. By using POM, you can create reusable and maintainable objects that represent the web pages or components of your application. Each object contains the methods and properties that interact with the elements on that page or component. This way, you can reduce the code duplication, improve the readability, and isolate the changes in the user interface from the test logic.
In my experience , the POM can be optimized using descriptive and meaningful names. explicit waits instead of implicit waits to ensure reliable test results.Implementation of data-driven testing to reuse test data and reduce duplication. Using assertions to validate test results and confirm expected behavior. Using logging and debugging tools to diagnose issues and troubleshoot test failures. Managing test code using version control for tracking changes, collaboration, and rollback capabilities and automating the testing process using continuous integration (CI) and continuous delivery (CD) pipelines to streamline the development cycle and minimize manual effort.
You can use POM design pattern also for API testing or any feature tests. For me, we need to cover the main idea of this design pattern. You can create objects for your feature as you did in pages. You can think this approach is modeling the test framework with the features. It could be your model based testing also. This pattern always serves the maintainability of the test code.
The POM allows you to reuse your selectors across all your testing suites by abstracting them, otherwise you would have to "hardcode" every selector in all your automation test cases. How annoying would be that? ;D
Except POM pattern, I would add, you can definitely use such patterns as Page Factory and Driver Factory. They can improve reliability, diversity and scalability of your automation. It always depends on needs of your project, but you can definitely implement these patterns at least in your pet projects and then you can get lots of useful tips and implement these things in your future or current projects.
Implementing the Page Object Model (POM) architecture helped streamline our test automation. By encapsulating web page interactions into dedicated classes, we reduced code duplication and maintenance efforts. This modular approach enhanced test scalability and readability.
Code reviews and version control are essential practices for any software development project, and they are also important for test automation reliability. Code reviews allow you to check the quality, consistency, and functionality of your test scripts, as well as to identify and fix any errors, bugs, or vulnerabilities. Version control helps you to track, manage, and synchronize the changes in your test scripts, as well as to collaborate with your team and resolve any conflicts. You can use tools like Git, GitHub, or Bitbucket to implement code reviews and version control for your test automation.
Usually, code reviews and version control tools should be added to CI/CD of your project. It's crucial to use these things, just because it will increase the speed of deployment and quality of the code and autotests, overall it will improve signficantly by implementing these things.
During a recent code review, a team member identified potential improvements in our automation scripts. This collaborative process not only enhanced code quality but also provided a valuable learning opportunity for the entire team. Integrating version control ensured a systematic approach to tracking changes and rolling back if necessary.
In current times when expectation is to do 100% of test automation. Multiple testers work at the same time, so it's always useful. Also, it is useful whenever you want a previous version to execute or see. reviews give you other testers perspective and makes you to follow coding/scripting/testing guidelines. In a project as the project deliverable codes are released, in a similar way test cases and test scripts and automation framework can be released parallelly with project releases/sprint releases. also, its helpful when executing using CI/CD pipelines, for commit smoke or release regression.
Test data management is the process of creating, storing, and managing the data that you use for your test automation. Test data can include input values, expected results, configuration settings, or any other information that your test scripts need to run. Test data management helps you to make your test automation reliable by ensuring that you have consistent, accurate, and relevant data for your test cases. You can use tools like Faker, Test Data Generator, or SQL Server Data Tools to create and manage your test data.
If possible, make it very easy to create clean systems from scratch and seed them with a defined set of baseline data, then you don't have to think about "how do I clean up" ever again. Then have your tests scripts create the the data they need to demonstrate what they should demonstrate. That isn't always that easy, so there a bunch of strategies and workarounds you can use, very context dependent though.
Effective test data management significantly improved our test coverage. Creating a centralized repository for test data, with dynamic data generation where applicable, minimized dependencies on external sources. This approach enhanced test reliability and eliminated data-related bottlenecks.
Running tests in parallel and on different environments can help you to make your test automation reliable by increasing the test coverage, reducing the test execution time, and detecting any compatibility or performance issues. Running tests in parallel means that you can execute multiple test cases or test suites at the same time, using different threads, processes, or machines. Running tests on different environments means that you can test your application on various browsers, operating systems, devices, or network conditions. You can use tools like Selenium Grid, TestNG, or Jenkins to run your tests in parallel and on different environments.
Running tests concurrently in parallel environments uncovered issues related to parallel execution dependencies. Resolving these challenges not only improved the efficiency of our test suite but also increased our confidence in the reliability of the automation, ensuring it can adapt to various deployment scenarios
Through my experience as Test engineer I worked on a project which need to be tested on multiple operating systems such as windows 10, Mac Os X, Linux (Ubuntu, fedora...). I have figured out that each operating system is different from the other, for example when I run the test on Windows it works smoothly without any problem. However when I run the same test on Mac or Linux I found some bugs normal and sometimes critical. In the same point sometimes I found a bug on the same Os with different versions for example ( MacOs Monterey / MacOs ventura) and so on. The main point of this description is to always verify your app releases on different environments and Os , browser version, mobile versions....
Ter ambientes segregados para a execu??o dos testes é uma maneira ideal de poder simular o comportamento das aplica??es em diferentes circunstancias, podendo identificar problemas e/ou limita??es de forma eficaz e poder aplicar os ajustes necessários !
The last step to make your test automation reliable is to monitor and report the test results. This will help you to evaluate the effectiveness, efficiency, and quality of your test automation, as well as to identify and resolve any issues, failures, or gaps. You should use tools that can provide you with clear, detailed, and actionable reports that show the test status, duration, errors, screenshots, logs, and metrics. You should also use tools that can alert you and your team of any test failures or anomalies, and integrate with your project management or bug tracking systems. Some of the tools that you can use for test monitoring and reporting are TestRail, Allure, or Katalon TestOps.
For monitoring and report, you can use Continuous Integration tools and show all the results in live. -Reports should contains necessary information or referral links about the problem. (Reproduction steps and used data) -Reports should be understandable for each stakeholder - Your reports should not create any noise for the stakeholders. If they receive many messages or emails about the test reports, they will ignore it. - Traceability and accessibility show be easy for your previous reports.
An essential aspect is to continually enhance your automation coverage by addressing identified gaps, including: Production Issues: Identify issues that were missed in the automation suite but surfaced in the production environment. User Behaviour Changes: Keep abreast of any alterations in user behaviour or patterns and incorporate these changes into your automation. Regression Misses: Integrate issues identified by functional/UAT teams during regression testing into your automation coverage. Product Analytics Utilisation: Leverage product analytics to stay informed about user interactions and update your automation coverage accordingly. Periodically reviewing and updating coverage on these factors makes Automation more reliable.
To ensure reliable Test Automation: ? Build robust, modular test cases. ? Use explicit waits for asynchronous actions. ? Employ logging and detailed reporting. ? Manage consistent test data. ? Integrate tests into CI/CD pipelines. ? Configure test environments accurately. ? Version control for test code. ? Regularly update tests with application changes. ? Implement parallel test execution. ?Foster collaboration between developers and testers.
Hi everyone here is my some input from my past experiences 1 Make a base page for for the common methods like selecting some specific conditions 2 Educate your team regarding base framework not only where they have to work also on the base of framework so that can do the modifications when needed 3 peer review its not specific to TL that they should review the code or PR but also the team should also do internally because everyone has its own experience and expertise by this way they can improve the code quality and also upgrade there self. Hope you all like my views Thanks
follow these best practices: Identify the most common user paths and write tests for them. This will help ensure that the most important functionality is tested. Start with simple tests that don’t require lots of code and support to get started with automation quickly. Use tools, libs, and frameworks (existing popular solutions) to speed up workflows and improve the efficiency of the QA process. Increase test coverage and eliminate the chance of human-prone errors. Once you have a few tests in place, you can start scaling up your automation testing with a test plan that ensures that you are testing all the important functionality. Future-proof your tests - use set-up, tear-down, and randomized test data.
Handle Flakiness: Identify and address flaky tests (tests that give inconsistent results). This might involve improving wait strategies, handling asynchronous operations, or fixing issues with test data.