Hyper automation is a term used to describe the integration of multiple technologies, including Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), and other tools, to automate and streamline business processes. Hyper automation enables organizations to automate a wide range of tasks, from routine, repetitive tasks to complex, data-driven processes, resulting in increased efficiency, improved productivity, and reduced costs.
This blog will cover AI-powered software testing and automation, including the benefits and challenges of hyper-automation. We’ll also provide best practices for implementing a hyper-automation strategy in testing and automation.
How AI is Reshaping Automation Testing?
In recent years, automation testing has advanced significantly from functional testing to using various tools and frameworks. However, to keep up with the changing software testing landscape, new and innovative testing methodologies have emerged, including AI- and ML-based testing tools.
Today, companies aim to use AI and ML algorithms to develop more effective test automation tools. The advantages of AI-based automation testing are numerous, including faster and continuous testing, increased automation, and faster ROI. By incorporating AI and ML into automated software testing processes, test scripts become more efficient, reliable, and effective.
However, traditional automation testing methods still present significant challenges to businesses. Fortunately, AI- and ML-based automation tools can help overcome these challenges and enhance the overall testing process.
Automation Testing using OCR Powered by AI
OCR is revolutionizing how quality assurance teams conduct front-end software testing in the software development industry. GUI tests can become unreliable when developers update an application’s underlying code because on-screen elements can change, making it difficult to locate objects using existing tools. This poses a challenge for teams to scale their testing efforts, as tests may fail if UI elements cannot be found.
However, automated testing tools that incorporate OCR can now recognize previously unidentified objects, allowing testers to develop stronger and more scalable UI tests quickly. This development has significant implications for automated UI testing, as OCR enhances the stability and reliability of automation scripts in unexpected ways, without the need for manual object rules and object repository maintenance.
Moreover, AI has further improved OCR’s capabilities, providing even greater potential for improving software quality. This article will explore the impact of OCR on automated UI testing, the potential of AI for OCR, and the tools available to leverage OCR for better software quality.
Some of the applications of OCR in test automation include:
1. Reading PDFs and Scanned PDFs
Using write-protected PDF documents is a popular method for creating and sharing documents, but it can be difficult for test automation tools to read or process them and compare them to expected results. If the PDF document is scanned, it’s usually impossible to compare it to anything. As a result, testers often manually verify that the file name matches the expected result, which is both time-consuming and prone to errors. However, OCR with AI can overcome these limitations by extracting text from scanned PDFs, even if they are imperfect or scanned at an angle. This text can be compared with text from a database, other documents, screenshots, or data stored in the test software itself, which can significantly improve the efficiency and accuracy of testing efforts by automating the comparison process.
2. Applications Similar to Terminal Server
In Microsoft Terminal Server, the server sends a bitmap to the computer screen, making the client a “dumb” one that sends mouse clicks, screen coordinates, and type commands to the server, much like the old mainframe green screens. As a result, Terminal Server applications usually have only one large window and do not have unique IDs for controls like buttons. Nevertheless, these applications still contain buttons, menus, and other user interface elements. By locating the text on the button or element, users can determine its center and click it. This method also applies to menus and other user interface elements.
3. Unpredictable Identifiers
Testers may face challenges when an element ID is unpredictable, such as when a logistics company employs a smart-coded ID that corresponds to the order number of a new order. However, OCR and AI can be utilized to read the page as a string and locate the radio button adjacent to the first group of numbers, allowing for it to be selected.
4. Missing Identifiers
Locating objects during testing on older platforms without unique identifiers can be difficult. An example of this is a Java Swing application that generates a dropdown list dynamically without assigning an identifier. However, OCR and AI can be utilized in these situations to identify the selected text, reposition the dropdown, and select a particular option.
5. Text That Won’t Change But Might Move
The existing test tools used in several applications depend on the parent container object or object ID to locate the text. However, if there is a change in the parent object or ID, it may become difficult to find the text. By using OCR and AI, testers can detect the object and verify if the text is still the same, regardless of changes in the parent object or ID.
Automation Testing using ChatGPT
ChatGPT, a language model based on GPT-3.5 architecture, has gained immense popularity due to its conversational style of interaction. It can handle queries, follow-up questions, and even acknowledge its mistakes, making it a useful tool for various applications. One potential area where ChatGPT can make an impact is software testing. It has the ability to generate code in response to natural language requests in multiple languages and frameworks, including Selenium code in different programming languages, which can be modified accordingly.
Some of the possible applications include:
- Creating automated test cases for any scenario
- Building a complex test automation pipeline using CI/CD
- Testing an application that utilizes multiple microservices
- Providing clear instructions and straightforward code for using the generated code
ChatGPT, as a low-code tool, has the ability to generate code using the Cucumber testing framework, which follows a behavior-driven development (BDD) approach. Test scenarios can be expressed in plain English using keywords like “given,” “when,” and “then” with the Cucumber framework.
Here are some reasons why ChatGPT is an effective low-code testing tool for creating use cases:
- It supports natural language, making it easier for users to write in plain language.
- It supports multiple programming languages, libraries, and frameworks such as Cucumber.
- It uses the Page Object model to update code when the element locator or application structure changes.
- It separates the code from the test cases, making the test script more maintainable.
Overall, integrating ChatGPT with AI-based test automation is seen as a promising development for the future of software testing.
Self-Healing Tests Powered by AI
Self-healing is a solution in test automation that addresses pain points such as maintenance of automation test scripts, where scripts break due to changes made to object properties such as Name, ID, XPath, CSS, etc. Identifying and updating object changes in the object repository manually can be time-consuming, increasing overall testing efforts.
Implementing a self-healing solution can help project teams adopt a Shift-Left approach in the Agile Methodology, leading to a more efficient testing process with increased productivity and faster delivery. There are various commercial and open-source tools available that use machine learning and other AI-driven algorithms to automatically detect and fix changes without any human intervention.
The self-healing technique involves an end-to-end workflow where an AI engine detects any change in an object property that may cause test failures. It then extracts the entire document object model (DOM) and the object properties, using dynamic location strategies based on a weighted scoring system developed using machine learning algorithms to extract the updated property. These new object properties are used to perform automated actions on the object during runtime, ensuring seamless test script execution without the user being aware of any changes.
Image-Based Visual Testing Powered by AI
The rise of digital media has highlighted the significance of seamless user experiences for businesses across their digital channels. This article explores the potential of AI and computer vision to enhance the accuracy, scalability, and value of traditional visual regression testing methods.
We will explore an AI-powered visual test automation framework and its significance for web development and testing teams as they adopt this innovative approach to testing. Visual regression testing, also known as user interface (UI) testing, involves verifying the aesthetic accuracy of everything that end-users encounter and interact with after code changes are made to a website. This type of testing differs from functional testing, which ensures that the application’s features and functions work correctly.
Visual regression tests aim to detect visual “bugs” that functional testing tools may not uncover, such as misaligned buttons, overlapping images or text, partially visible elements, and issues with responsive layout and rendering.
Before diving into the details of visual AI, let us examine a few commonly used visual testing methods.
1. Pixel or Snapshot Comparison
One commonly used visual testing method involves taking a screenshot of a webpage and comparing it to a previous version of the page to detect any visual changes. However, this pixel-based approach has several challenges. It can produce false positives and identify small changes that are not noticeable to the human eye, such as alterations in font anti-aliasing or image scaling due to different rendering processes by the browser or graphics card. Moreover, it cannot handle dynamic content that changes frequently, such as a blinking cursor or a page with frequently updating content. Additionally, when there are differences in subsequent pixels, they may obscure actual issues further down the page.
2. Document Object Model (DOM) Comparison
The Document Object Model (DOM) is a programming interface that represents web documents as nodes and objects, allowing programs to modify the document’s structure, style, and content. Although the DOM approach may seem like a straightforward solution for visual test automation, there are some limitations to consider. Firstly, the DOM includes both rendered and non-rendered content, which means that a simple page restructure can result in false positives being identified as differences by the DOM comparison. Secondly, the DOM comparison cannot detect rendering changes. For instance, if a new image file is uploaded to a page using the same old file name, it will go unnoticed by the DOM comparison, even if the user sees a difference on the rendered page.
Therefore, relying solely on DOM comparisons is insufficient to ensure visual integrity.
How Visual AI Works?
Visual AI is an approach that overcomes the challenges of pixel and DOM methods. It identifies the visual elements that make up a screen or page and uses computer vision to recognize these elements and their associated properties, including dimensions, color, content, and placement, much like the human eye. Instead of examining individual pixels, Visual AI compares the properties of a checkpoint element with a baseline to detect any visible differences.
Automated visual testing now employs computer vision algorithms, similar to those used for facial recognition, for object recognition, and visual comparison. These tools, known as visual AI testing tools, have a learning algorithm that interprets the intended display of visual elements and compares them to the rendered page’s actual visual elements and their locations. Unlike pixel-based tools, visual AI testing tools capture page snapshots during functional tests and use algorithms to detect errors.
One advantage of visual AI testing tools is that they do not require static environments to function accurately. They have demonstrated high levels of precision with dynamic content because they base the comparison on relationships between elements, rather than individual pixels. As a result, AI-powered testing tools can handle a wider range of issues than snapshot testing tools.
Below is a comparison of the kinds of issues that AI-enabled visual testing tools can handle compared to snapshot testing tools:
Benefits of Hyper Automation
1. Increased Efficiency
Hyper automation allows businesses to automate repetitive, mundane tasks, freeing up employees to focus on more strategic and value-added work.
2. Improved Productivity
By automating time-consuming processes, hyper-automation helps to reduce errors and improve the speed and accuracy of business processes, resulting in increased productivity.
3. Reduced Costs
Hyper automation can help to reduce labor costs by automating tasks that were previously done manually. Additionally, it can help to reduce the cost of errors and rework, resulting in cost savings for the organization.
4. Better Customer Experience
By streamlining and automating business processes, hyper-automation can improve the speed and accuracy of customer interactions, resulting in a better customer experience.
Challenges of Hyper Automation
Hyper automation involves the integration of multiple technologies, which can be complex and challenging to implement and manage.
2. Resistance to Change
Employees may be resistant to change, particularly if they perceive that automation threatens their jobs.
3. Data Quality
Hyper automation relies on accurate and high-quality data, so organizations need to ensure that their data is clean, up-to-date, and easily accessible.
To sum up, hyper-automation can bring substantial advantages to organizations seeking to enhance their productivity, efficiency, and customer satisfaction through test automation. Nonetheless, it is crucial to adopt a strategic approach to hyper-automation, starting with small-scale implementations and prioritizing high-impact processes while ensuring data accuracy and involving employees. With meticulous planning and execution, hyper-automation in test automation can generate significant rewards for organizations.