Comparing Mobile and Web Automation: AI vs. Traditional Approaches
Welcome back to the second part of our blog series on Enhancing Testing and Automation with AI. In the previous blog, we delved into the fascinating world of automating APIs both with and without the assistance of artificial intelligence. Now, as we continue our journey into the realm of automation, we shift our focus to a new frontier: mobile and web automation. In this part, we will embark on a comprehensive exploration and comparison of data-driven automation, examining how AI can revolutionize the landscape, and how it stacks up against traditional methods in the realms of mobile and web automation. Join us as we unveil the transformative potential of AI in streamlining and enhancing your automation processes.
Web Automation
Traditional web automation faces numerous challenges that can hinder its efficiency and scalability. We explore how AI-powered tools, specifically GitHub Copilot and ChatGPT, can be game-changers in resolving these challenges in web automation.
The Challenges of traditional Web Automation
Traditional web automation, while effective in automating repetitive tasks, grapples with several hurdles that testers must overcome. Some of the key challenges include:
| Challenges in web Automation | AI-Powered solutions | |
|---|---|---|
| Script Maintenance | As web applications undergo frequent updates and changes, maintaining scripts written in programming languages like Java or Python becomes time-consuming and cumbersome | GitHub Copilot: Its code suggestion feature can automatically generate code snippets for repetitive tasks and updates, reducing the burden of manual script maintenance. |
| Handling Dynamic Elements | Web applications often feature dynamic elements such as pop-ups, alerts, and UI changes based on user interactions, posing difficulties for traditional automation scripts. | GitHub Copilot: Copilot can suggest dynamic element handling techniques and best practices based on contextual understanding, enabling automation scripts to adapt to UI changes effectively. |
| Data-Driven Testing | Traditional web automation may lack robust support for data-driven testing, making it challenging to execute test cases with different data sets and scenarios. | GitHub Copilot: It can assist in generating data-driven test cases by suggesting code for parameterization and iteration, making it easier to execute test scenarios with various data sets. |
| Cross-Browser and Cross-Platform Testing | Testing web applications across various browsers and platforms is essential to ensure compatibility. Traditional web automation may face difficulties in handling these diverse environments effectively. | GitHub Copilot: Copilot can provide cross-browser and cross-platform testing libraries or snippets, streamlining the process of writing test scripts compatible with different environments. |
| Complex User Interactions | Modern web applications involve complex user interactions, such as drag-and-drop and context-based actions. Scripting such interactions in traditional automation can be complex and error-prone. | GitHub Copilot: Its ability to comprehend complex user interactions allows it to suggest code for handling drag-and-drop, multi-select, and other intricate actions with greater accuracy. |
| Waiting for Elements to Load | Setting up appropriate wait times for elements to load can be challenging in traditional automation, leading to synchronisation issues and test failures. | GitHub Copilot: Copilot can suggest efficient wait mechanisms, reducing synchronisation issues and improving the stability of automation scripts. |
| Maintenance Overhead | Continuously updating automation scripts to match changes in the UI or functionality can become a significant overhead for testers. | GitHub Copilot: By providing automated code suggestions, Copilot minimises the effort required to update and maintain scripts when the application changes, significantly reducing maintenance overhead. |
| Limited Test Coverage | Traditional web automation might focus on specific test scenarios, leaving gaps in test coverage and risking critical issues going unnoticed. | ChatGPT: It can assist testers in brainstorming test scenarios and edge cases, leading to the creation of more comprehensive test suites that cover a wider range of scenarios. |
| Limited Reporting and Analysis | Traditional automation frameworks may lack robust reporting and analysis capabilities, hindering the identification of test patterns and trends. | ChatGPT: It can assist testers in brainstorming test scenarios and edge cases, leading to the creation of more comprehensive test suites that cover a wider range of scenarios. |
| Non-technical Testers | Testers with limited programming knowledge might find it difficult to create and maintain automation scripts using traditional methods, creating a dependency on technical resources. | GitHub Copilot: Copilot can empower non-technical testers to write automation scripts by offering code suggestions and guidance, thereby reducing the dependency on technical resources. |
Case study - Unravelling the Web Testing Enigma: AI Lights the Way
In this case study, we showcase how a leading Financial Services Company achieved remarkable improvements in web automation by adopting AI-powered tools. Discover how their framework and automated test cases transformed with and without AI integration, leading to unprecedented efficiency gains.
Steps followed:
We initiated a prompt to seek a web automation framework with strong community support, and ChatGPT recommended using Selenium with Java and the TestNG framework. Inquiring about an integrated reporting tool, ChatGPT suggested Allure. Additionally, it advised implementing the Page Object Model to manage various pages, functionalities, and web elements efficiently.
For enhanced efficiency, ChatGPT recommended using a singleton web driver to avoid multiple initializations. We also sought guidance on integrating Jenkins, and ChatGPT provided us with comprehensive steps to follow. The process of dockerization was simplified with the instructions provided by ChatGPT.
Our coding tasks were significantly simplified with the invaluable assistance of Copilot. It enabled us to effortlessly create Constant files, and env files, and automated most of the required validations, contributing to enhanced productivity. While some manual analysis was necessary for web elements, the combination of ChatGPT and Copilot facilitated the majority of other tasks.
In summary, ChatGPT and Copilot proved to be powerful allies in streamlining our web automation project, making coding and various processes remarkably easier.
Time Comparison Chart
This chart visually illustrates the time difference between two scenarios - one with AI and one without AI. By using bars to represent the time taken in each scenario, we easily compare and understand the significant efficiency gains achieved through AI integration.
Mobile Automation
Mobile automation refers to the process of automating the testing and execution of tasks on mobile devices such as smartphones and tablets. This type of automation is commonly used in software development and quality assurance to ensure the functionality and performance of mobile applications.
| Challenges in mobile Automation | AI-Powered solutions | |
|---|---|---|
| Device Fragmentation | The mobile market is diverse, with numerous devices running on various operating systems, screen sizes, and hardware configurations. Ensuring compatibility and consistent performance across all these devices can be challenging for mobile automation. | While Copilot can't directly address device fragmentation, it can speed up the coding process, allowing developers to focus on handling compatibility and testing on different devices. |
| UI Changes | Mobile app interfaces often undergo frequent updates and design changes. These alterations can break existing automation scripts, necessitating continuous maintenance and updates. | Copilot can help generate XPath or CSS selectors that are less likely to break when UI changes occur |
| Test data creation for different scenarios | It is difficult to create test data for every corner cases. There are chances that few corner cases can be missed. | You can leverage ChatGPT or Copilot to generate test data. For instance, use AI to create random usernames, email addresses, or text inputs for your test scenarios. This helps in data-driven testing. |
Case study - The AI Revolution in Mobile Automation: A Journey Unveiled
Customer : Financial Services Company, a leading provider of liquidity solutions for Employee Stock Ownership Plans (ESOPs).
Challenge : Create framework for mobile automation and automate mobile test cases with and without AI.
Steps followed:
The following steps were followed in our case study:
1. Framework Selection: The team, proficient in using WebdriverIO, opted to stick with the same framework for consistency. The chosen scripting language was JavaScript.
2. Configuration Setup: After finalizing the framework, the team reached out to ChatGPT for guidance on the folder structure and configuration files. Initially, ChatGPT suggested a single config file, but the team required three files. They queried ChatGPT again, and it recommended three files - one for Android, one for iOS, and one shared configuration.
3. Automation File Creation: With ChatGPT's assistance, the team proceeded to create essential automation files, including an assert library file, a util file, files for actions and selectors, and swipe functions.
4. Debugging Challenges: Debugging proved to be a critical phase, as the team encountered issues like 'get location' returning two values instead of one and difficulties with the 'scroll into view' function. They dedicated some hours to resolve these issues successfully.
5. Gherkin Test Case Conversion: The team transitioned each scenario into Gherkin syntaxed test cases, which proved to be a swift and straightforward process. ChatGPT efficiently generated the desired results in no time.
6. Step Definition Creation: Crafting step definition files and managing page interactions was made notably smoother with the aid of AI. However, manual work was still required for Xpath identification.
7. Automation Enhancement: Test case automation was significantly eased with the integration of AI. The team used AI to create environment files, logger, base utilities, and more.
In summary, leveraging AI greatly facilitated our test case automation journey, simplifying various aspects of the process and contributing to a more efficient and productive workflow.
Time Comparison Chart
This chart visually illustrates the difference in time between two scenarios, one with AI and one without AI. It typically uses bars to represent the time taken in each scenario, making it easy to compare and understand the efficiency gains achieved through AI integration.
Concluding Thoughts: Harnessing the Power of AI in Automation
In conclusion, our exploration of mobile and web automation, both with and without the assistance of AI, has provided us with valuable insights into the power of artificial intelligence in streamlining and enhancing our automation processes.
In the realm of web automation, our journey was marked by smooth sailing. AI played a crucial role in simplifying various tasks, from configuring the framework to generating test results. The efficiency and productivity gains were evident, showcasing how AI can be a valuable ally in web automation endeavors.
However, when it came to mobile automation, we did encounter some challenges, particularly during the debugging and troubleshooting phases. Despite these hurdles, AI proved to be a significant asset, helping us minimize the time spent addressing these issues. It acted as a guiding light, providing solutions and suggestions that ultimately expedited our progress.
In our next blog, we'll explore the exciting world of performance testing and its transformation through AI. Stay tuned for insights comparing AI-driven performance testing to traditional methods. It's a journey worth following!