You're facing constant software updates. How do you maintain the precision of your automation scripts?

My approach to automation involves several steps. First, I focus on writing code that functions correctly. Once that is achieved, I address edge cases to ensure comprehensive coverage. The next step is to conduct rigorous testing to identify and fix any bugs. Finally, I optimize the code by adding logging, enhancing modularity, and refining performance.

Version controlling is required along with that need to maintain highly dynamic object repository so that we can quickly update the changes in element if there are any. Also need to make sure that is there any changes in workflow or not if yes then we can leverage AI for that to add some code into automation framework.

Continous testing is very important along with writing an easy to maintain repository for long run. If we have the right process it can be developed/modified along with the feature development itself. AI can be great help to write such tests.

To keep my automation scripts precise, I focus on minimizing dependencies on other software from the start. This approach makes it easier to maintain accuracy. For any essential software my scripts rely on, I stay informed about their update cycles. This allows me to regularly review and update my scripts, ensuring they remain compatible and reliable. Thorough testing after each update is key to keeping everything running smoothly.

Build maintainable scripts (clear code, object repository) and handle UI changes (multiple locators, wait times). Test often (regression tests) and adapt scripts (data-driven, parameters). Stay informed about updates (changelogs, communication).

No SDCD Honeywell acompanhei a troca de todas esta??es de controle e release, sistema operacional sempre uma vers?o anterior sem bugs.

Version control is a crucial practice in managing automation scripts and software development, providing a systematic approach to tracking changes, collaborating on code, and maintaining the integrity of your automation processes. At its core, version control involves using a system to record changes made to scripts over time. This allows you to track revisions, compare different versions, and revert to previous states if necessary. By maintaining a detailed history of changes, version control helps in managing updates and debugging issues more efficiently. When implementing version control, start by selecting a version control system (VCS) that fits your needs, such as Git, Subversion (SVN), or Mercurial.

The CI system should have integration with automated tests such that each change runs relevant tests and fails the build if any of the push blocking tests fail. This ensures the deployed change is good enough to pick for further testing and investigation.

Na atualiza??o de Script no que trabalhei exemplo Honeywell, n?o teve muita diferen?a do antigo para o novo. Conforme visto nos comentários dos colegas acima sempre mantendo uma programa??o bem dividida e simples, uma atualiza??o vai ser mais fácil.

Continuous testing is a crucial practice in modern software development and automation, focusing on the ongoing evaluation of software quality throughout the development lifecycle. It integrates testing into the continuous integration and continuous deployment (CI/CD) pipeline, ensuring that code changes are tested as they are made and deployed. The core of continuous testing is to automate testing processes, allowing for frequent and reliable validation of software. Automated tests are integrated into the CI/CD pipeline, so each code change triggers a series of tests. This approach helps identify defects and issues early in the development cycle, before they can affect the final product.

Embracing abstraction is a key principle in software development and automation that involves hiding complex implementation details behind simpler interfaces. This approach improves code manageability, enhances flexibility, and simplifies interactions with complex systems. Abstraction allows developers to create generalized interfaces that provide a simplified way to interact with underlying systems or components. By focusing on what a component does rather than how it does it, abstraction helps in managing complexity and reducing the cognitive load on developers. By defining clear and concise interfaces, developers can work on individual components independently without needing to understand the intricate details of other parts of system.

Comprehensive documentation ensures that processes, code, and systems are well-understood, easily managed, and effectively communicated among team members and stakeholders. Begin by creating detailed documentation for all aspects of your project. This includes design documents, architecture diagrams, API specifications, and user manuals. Each piece should provide a clear and thorough explanation of its subject, including purpose, functionality, and interactions with other components. Maintain thorough documentation for code and automation scripts. Document the purpose and functionality of each script, including how it integrates with other systems and any specific configurations or dependencies.

Leveraging AI for maintaining automation scripts is a game-changer in software development, especially in environments with frequent updates. By employing machine learning algorithms, developers can not only predict issues but also automate the adaptation of scripts, which minimizes downtime and enhances efficiency. This proactive approach not only saves time but also allows developers to focus on more strategic tasks, ultimately leading to more robust and scalable applications. Investing in AI capabilities can provide a significant competitive advantage in the fast-paced tech landscape.