AI-Driven Self-Healing Tests With Playwright, Cucumber, JS

Automated testing is essential to modern software development, ensuring stability and reducing manual effort. However, test scripts frequently break due to UI changes, such as modifications in element attributes, structure, or identifiers. Traditional test automation frameworks rely on static locators, making them vulnerable to these changes. AI-powered self-healing automation addresses this challenge by dynamically selecting and adapting locators based on real-time evaluation. Self-healing is crucial for automation testing because it significantly reduces the maintenance overhead associated with test scripts by automatically adapting to changes in the application's user interface. This allows tests to remain reliable and functional even when the underlying code or design is updated, thus saving time and effort for testers while improving overall test stability and efficiency. When UI elements change (like button IDs or class names), self-healing mechanisms can automatically update the test script to locate the new element, eliminating the need for manual updates and preventing test failures due to outdated locators. By dynamically adjusting to changes, self-healing tests are less prone to "flaky" failures caused by minor UI modifications, leading to more reliable test results. With less time spent on test maintenance, developers can focus on building new features and delivering software updates faster. Modern applications often have dynamic interfaces where elements change frequently, making self-healing capabilities vital for maintaining test accuracy. Initialize a playwright project and install cucumber dependencies by executing the command: Cucumber allows for writing tests in Gherkin syntax, making them readable and easier to maintain for non-technical stakeholders. This function is designed to "self-heal" by trying multiple alternative selectors when attempting to click an element. If one selector fails (for example, due to a change in the page's structure), it automatically tries the next one until one succeeds or all have been tried. The cucumber.js file is the configuration file for Cucumber.js, which allows you to customize how your tests are executed. As you see in the above screenshot, the code tried to find the selector and when it couldn't find the selector, the code moved on to finding the next alternative selector , which was successful; hence, clicking the element using the selector Let's explore with an example on how to incorporate multiple locator strategies into AI-powered self-healing tests with a fallback method. The idea is to try each known locator in a predefined order before resorting to the ML-based fallback when all known locators fail. Below is an example of the AI-powered self-healing approach, showing how to integrate TensorFlow.js (specifically ) to perform a real machine-learning-based fallback. We'll extend the findElementUsingML function to load an ML model, run inference on a screenshot, and parse the results to find the target UI element. Note: In a real-world scenario, you'd have a trained object detection or image classification model that knows how to detect specific UI elements (e.g., "Add to Cart" button). For illustration, we'll show pseudo-code for loading a model and parsing bounding box predictions. The actual model and label mapping will depend on your training data and approach. Let's begin by setting up the playwright project and installing the dependencies (Cucumber and Tensorflow). Create the folder structure below and add the required files: This file contains a helper function to find an element using multiple locator strategies. If all fail, it delegates to the AI fallback. In util/aiLocator.js, we simulate an ML-based locator. In a production implementation, you'd load your trained ML model (for example, using TensorFlow.js) to process the screenshot and return the location (bounding box) of the "Add to Cart" button. Below is a mock example of how you might load and run inference with TensorFlow.js (using ), parse bounding boxes, and pick the coordinates for the "Add to Cart" button. Disclaimer: The code below shows the overall structure. You'll need a trained model that can detect or classify UI elements (e.g., a custom object detection model). The actual code for parsing predictions will depend on how your model outputs bounding boxes, classes, and scores. Using TensorFlow.js for self-healing tests involves: This approach provides a robust fallback that can adapt to UI changes if your ML model is trained to recognize the visual cues of your target elements. As your UI evolves, you can retrain the model or add new examples to improve detection accuracy, thereby continuously "healing" your tests without needing to hardcode new selectors.

DZone

Thu, 13 Mar, 12:03 PM UTC

Say Goodbye to Manual Code Testing In Cursor with Operative.sh

What if the most time-consuming, error-prone aspect of web development could be transformed into a seamless, automated process? For years, developers have wrestled with the challenges of testing -- debugging intricate edge cases, validating functionality across countless scenarios, and making sure flawless performance under pressure. These tasks, while critical, often drain resources and slow down innovation. But now, a breakthrough has emerged. By combining the power of Cursor AI with tools like Operative.sh, the toughest bottlenecks in web application testing are being shattered. With AI-driven automation, developers are no longer bound by the manual grind, unlocking a new era of efficiency and precision. AI Labs explores how Cursor and Operative.sh are transforming the testing landscape. From automating repetitive tasks to generating test cases with natural language instructions, these tools are designed to tackle even the most complex scenarios with ease. You'll discover how features like the Web Eval Agent and browser state management streamline workflows, reduce human error, and empower developers to focus on innovation rather than troubleshooting. Whether you're navigating intricate edge cases or optimizing performance, this exploration will reveal how AI is reshaping the future of software testing -- one automated process at a time. The integration of Cursor with Operative.sh provides a seamless and efficient way to automate web application testing. This combination enables developers to handle everything from basic login processes to complex edge case scenarios using natural language instructions. By automating the generation and execution of test cases, the need for extensive manual scripting is eliminated. This not only saves time but also reduces the risk of human error, resulting in a more reliable and comprehensive testing process. Even intricate applications benefit from this streamlined approach, making sure robust performance across various scenarios. Operative.sh is equipped with a range of features designed to optimize the testing experience. These features automate repetitive tasks and improve overall efficiency, allowing developers to focus on more strategic aspects of their projects. Key components include: These tools work in tandem to simplify the testing workflow, allowing developers to concentrate on higher-level tasks while the intricacies of testing are managed automatically. Here are more guides from our previous articles and guides related to AI automations that you may find helpful. Setting up Operative.sh is designed to be straightforward, making sure accessibility for developers of all experience levels. The tool can be installed manually or through an automated process via GitHub. To activate the system, an API key is required, making sure secure and controlled access to its features. Once integrated with Cursor, Operative.sh operates seamlessly within your development environment. This integration allows you to initiate and manage tests effortlessly, making it a practical addition to any development workflow. The testing workflow offered by Operative.sh is both intuitive and efficient, catering to a wide range of development needs. Tests can be executed in either headless or visible browser modes, depending on user preferences. Results are presented in a comprehensive dashboard that provides detailed insights, including: In addition to these features, the tool automatically generates and tests edge cases, making sure that your application performs reliably under various conditions. This level of automation reduces the need for manual oversight, allowing developers to identify and resolve potential issues early in the development cycle. While Operative.sh offers numerous advantages, it is important to acknowledge its limitations. For highly complex scenarios, the testing process may take longer compared to manual scripting. Additionally, certain tests may require manual configuration or the use of supplementary tools to achieve optimal results. Despite these challenges, the tool's ability to automate repetitive tasks and handle edge cases makes it a valuable asset for developers seeking to enhance efficiency and accuracy in their workflows. Integrating Operative.sh into your development workflow can significantly improve how your team approaches web application testing. By automating repetitive tasks and identifying issues early, the tool minimizes the likelihood of bugs as your application scales. This allows your team to dedicate more time to innovation and feature development, ultimately enhancing productivity and software quality. The streamlined testing process also ensures that applications are thoroughly validated, reducing the risk of errors in production environments. The evolution of AI-driven tools like Operative.sh is set to transform the field of software testing further. As these tools continue to advance, they are likely to become comprehensive systems for managing and tracking test cases. This progression could make the testing process even more efficient and accessible for development teams, regardless of project complexity. By automating more aspects of testing and integrating seamlessly with other development tools, AI-driven solutions have the potential to redefine how software testing is conducted, paving the way for greater innovation and reliability in the software industry.

Geeky Gadgets

Wed, 28 May, 10:04 AM UTC

Deepseek Engineer V2 : Say Goodbye to Bloated Software, Free Precision AI Coding

What if your coding assistant could be as light as a feather, yet powerful enough to handle complex workflows with ease? Enter Deepseek Engineer V2, an innovative AI-powered coding tool that's rewriting the rules for developers. In a world where bloated software often slows down productivity, this lightweight, single-file solution offers a refreshing alternative. Imagine a tool that doesn't just assist you but actively enhances your workflow -- seamlessly integrating into your processes while keeping memory usage to a bare minimum. Whether you're juggling multi-agent operations or fine-tuning Continuous Integration (CI) pipelines, Deepseek Engineer V2 promises to deliver speed, precision, and cost-efficiency like no other. AI Code King unpacks the unique capabilities that make Deepseek Engineer V2 a standout choice for modern developers. From its real-time reasoning capabilities to its customizable features, this tool is designed to simplify even the most intricate coding challenges. You'll discover how its intuitive terminal interface, advanced error handling, and robust file management tools can transform your workflow without overwhelming your system. But what truly sets it apart? A design philosophy that prioritizes adaptability and efficiency, making it a perfect fit for developers who demand more from their tools. As you explore its potential, you might just find yourself rethinking what's possible in AI-powered coding. Deepseek Engineer V2 is equipped with a suite of features tailored to meet the demands of modern software development. By using Deepseek R1, an advanced reasoning model, the tool ensures accurate and reliable real-time chain-of-thought reasoning for complex tasks. Here's what makes it stand out: These features collectively make Deepseek Engineer V2 a robust and versatile tool for developers seeking to streamline their workflows and improve efficiency. Deepseek Engineer V2 is designed with simplicity and adaptability in mind. Its single-file architecture ensures an easy setup process while offering flexibility for customization. Developers can modify the tool to add new functionalities or use it as a foundation for building their own AI-powered coding assistant. This adaptability allows it to cater to a wide range of development needs, from small-scale projects to more complex workflows. The lightweight design is particularly advantageous for CI workflows and multi-agent tasks, where speed and resource efficiency are critical. Its low memory footprint ensures consistent performance, even when running multiple instances simultaneously. This makes it an excellent choice for developers working in environments where computational resources are limited or cost-sensitive. Check out more relevant guides from our extensive collection on Deepseek that you might find useful. Deepseek Engineer V2 provides a comprehensive set of functionalities to support your coding tasks. These include: The tool integrates seamlessly with CI workflows, allowing automation of repetitive tasks and improving overall efficiency. Additionally, its compatibility with multi-agent operations makes it a valuable asset in complex development environments, where multiple processes need to run concurrently without performance degradation. One of the standout aspects of Deepseek Engineer V2 is its focus on cost-efficiency. The tool is optimized for minimal resource consumption, making it an excellent choice for developers working within tight budgets. Despite its lightweight architecture, it delivers robust performance, completing tasks quickly and accurately without compromising reliability. This balance between performance and resource efficiency ensures that Deepseek Engineer V2 remains a practical and dependable solution for developers who need to maximize output while minimizing costs. Its ability to handle complex tasks with minimal overhead makes it a preferred choice for both individual developers and larger teams. When compared to other AI coding assistants, Deepseek Engineer V2 distinguishes itself through its speed, flexibility, and ease of use. Unlike bulkier tools such as Ader, it offers a more streamlined experience with a smaller resource footprint. Additionally, it provides greater compatibility and customization options than alternatives like Claude Code, making it a superior choice for developers seeking a versatile and efficient solution. Its single-file architecture and real-time reasoning capabilities set it apart from competitors, making sure that developers can rely on it for a wide range of tasks without the need for extensive setup or additional resources. This makes it particularly appealing for those who value simplicity and adaptability in their tools. While Deepseek Engineer V2 excels in many areas, it does have some limitations. For instance, it cannot execute terminal commands, which may restrict its functionality in certain scenarios. This limitation means that developers may need to rely on additional tools for tasks requiring direct terminal interaction. However, its core capabilities -- such as advanced reasoning and file operations -- compensate for this drawback, making sure it remains a valuable tool for most coding tasks. For developers who prioritize efficiency and precision in file management and workflow automation, Deepseek Engineer V2 continues to deliver significant value. Deepseek Engineer V2 is particularly well-suited for tasks that demand speed, efficiency, and precision. Common use cases include: Its adaptability and performance make it an excellent choice for developers aiming to optimize their coding processes and enhance productivity. Setting up Deepseek Engineer V2 is straightforward and requires minimal effort. Follow these steps to get started: Once installed, you can begin using the tool for both manual and automated file operations, guided by intuitive prompts. Its user-friendly interface ensures that even developers new to AI-powered tools can quickly adapt and start benefiting from its capabilities.

Geeky Gadgets

Thu, 5 Jun, 12:23 PM UTC

Leveraging LLMs for Software Testing

As software systems become more complex, traditional testing methods often fall short in keeping up with the fast-paced development cycles and changing user needs. Fortunately, advancements in machine learning and generative AI are bringing intelligent and adaptive testing strategies that improve test coverage and decrease maintenance efforts to speed up the entire testing process. This article details using large language models (LLMs) to test a Python codebase project. LLMs can greatly accelerate the testing process by automating tasks such as test case generation, execution, and analysis. This automation allows testing teams to concentrate on more strategic activities, such as exploratory testing and test planning. LLMs can enhance test coverage by identifying edge cases and generating test scenarios that manual testing might overlook. This results in a more comprehensive and robust testing process, reducing the risk of defects being released into production. LLMs can analyze code changes and automatically update test scripts. This process significantly minimizes manual effort and the potential for errors. The following Python code uses OpenAI's API to generate test cases for a given user story. It sets an API key, defines a function that creates a prompt using the user story and interacts with the GPT-4 model to generate test cases. This helps with the bootstrapping required for creating manual test cases. Pytest is Python's popular testing framework. The following Python code uses OpenAI's GPT API to generate pytest test cases for a function, that calculates BMI based on weight and height and categorizes it ("Underweight," "Normal weight," etc.). Using the module, the script extracts the function's source code and prepares a prompt asking GPT to generate parameterized test cases, including edge cases like invalid or zero inputs. This method is very effective in generating automated pytest cases, which can then be included as a separate test file in the project. In some situations, sharing code directly may not be feasible due to privacy or security concerns. However, ChatGPT can still assist in these cases. LLM-powered test automation represents a significant advancement in software quality assurance. While challenges exist, organizations that successfully implement these technologies gain substantial advantages in testing efficiency, coverage, and reliability. As AI capabilities evolve, we can expect even more sophisticated testing approaches that will further improve software quality and reduce manual effort.

DZone

Mon, 17 Mar, 5:05 PM UTC

Revolutionizing Software Testing with AI-Driven Automation

The future of AI in test automation lies in self-learning systems, which continuously improve their performance over time. These systems can autonomously expand test coverage, adapt to changes in application environments, and optimize test execution without human intervention. Self-learning frameworks use unsupervised learning techniques to adapt to new scenarios, making them increasingly efficient with each test cycle.Organizations employing these systems have experienced up to a 22.4% increase in defect detection rates and a 54.8% reduction in maintenance effort. This level of automation ensures that testing is always aligned with the latest application updates, without requiring constant human oversight. There are several far-reaching economic and operational benefits provided by AI-driven test automation. By cutting down on maintenance, increasing the detection of defects, and further improving the optimization of resources, AI-driven frameworks help software teams divert their attention to innovation rather than troubleshooting. In conclusion, the use of AI in test automation has eased many of the burdens traditionally associated with testing, setting the stage for adaptive, intelligent, and efficient testing frameworks. By taking over mundane tasks, reducing the continuous upkeep of scripts, and assisting in the active management of defects, AI-driven systems are fast becoming an indispensable toolkit in software quality engineering. As the technology advances, it will take on a greater role in ensuring software is reliable and resilient in this fast-track era wherein updates are an everyday occurrence. The contributions of experts like Jainik Sudhanshubhai Patel are a significant step towards fully autonomous, self-optimizing quality assurance systems that will keep innovations going in the tech world.

Analytics Insight

Tue, 27 May, 4:05 PM UTC

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