Object recognition, or the ability to differentiate between different elements within a UI, is an essential component of UI testing. Testers usually use automation scripts to help them validate components like buttons, text fields, and menus.
However, without the right tools, it’s tricky for UI testers to work through more complex applications with dynamic content. That’s why users need tools like Ranorex Spy, which provides testers with precise object recognition.
Object Recognition in Complex Environments
Object recognition is critical to UI testing for the following reasons:
- Test automation: Object recognition enables UI testing by automating scripts that recognize and interact with UI elements consistently, which reduces the need for manual tests.
- Consistency: Object recognition tools adapt to different environments, so you can cover how UI behaves under various scenarios.
- Regression test execution: Automated object recognition quickly finds UI elements and ensures they work the same as before the new code changes.
- Speed: The automation speeds up test execution, eliminating the need for extensive manual testing, which can be slow and labor-intensive.
- More complete coverage: Object recognition helps testers locate all UI elements, ensuring nothing is overlooked.
- Better accuracy: Using an object recognition program makes automated tests more precise in locating various UI elements, improving reliability and reducing human errors.
🎯 Challenges in Object Recognition
When implemented correctly, AI object recognition helps make automated testing more seamless. However, using different object recognition methods does bring its share of challenges, as outlined below.
Challenge #1: Variability in Object Appearance
The evolution of responsive design has allowed web designers to develop innovative ways to present content to users. However, that means UI elements can look different when used on devices of varying screen sizes. For example, an element that appears front and center on a desktop monitor can disappear into a menu when viewed on a mobile device.
Elements sometimes change dynamically based on user input, real-time data, or changes to the application state. This alters a component’s appearance or even how to locate it in the Document Object Model (DOM).
Web page elements can render differently depending on the browser used. A page viewed using Chrome can change based on rendering engines and CSS support in a different type, leading to discrepancies in the size, styling, and position of UI elements.
Components like buttons, links, or modals often change state depending on user interactions. Applications that support multiple languages can appear differently depending on the user’s locale.
Challenge #2: Scale and Position Variance
The size and position of elements differ based on screen size and orientation, making them difficult to locate with an object recognition tool. That’s especially true of high-DPI (dots per inch) displays designed to render high-resolution images. Things get more complicated when you scale UI elements.
You can remedy these difficulties using relative positioning with your object recognition logic instead of absolute positions. In addition, try employing flexible selectors like XPath or CSS that can adapt to DOM structure changes.
Check for anchor elements with stable, predictable positions. If a drop-down’s position changes, you can find it by looking for a specific label where it’s always located. Finally, use responsive design testing with your automated object recognition object tests to ensure you account for an element’s changes under different conditions.
Challenge #3: Background Complexity
Many web designers add dynamic or complex backgrounds to boost a website’s aesthetic appeal. However, this makes it harder for UI testers to locate and interact with UI elements accurately.
If your background changes frequently, like one that is animated or plays a video, it can obscure elements and make object recognition harder. High-contrast or patterned backgrounds add visual noise, making it harder to find different components.
Using transparency can cause elements to blend into the background. Other elements like modals, tooltips, and floating buttons can add extra layers of visual information, making it difficult for object recognition software.
Some strategies you can employ to overcome these difficulties include the following:
- Advanced imaging processing techniques like color filtering, edge detection, and contrast enhancement improve the visibility of UI components in complex backgrounds
- AI and machine learning (ML) models to recognize elements based on combinations of features, including size and relative position
- Object segmentation, which partitions an image into regions that correspond to different elements to separate them from complicated backgrounds
- Template matching, which involves matching parts of a screen to a stored template to help an algorithm locate elements
Challenge #4: Real-Time Processing Needs
Real-time processing helps maintain the flow of automated tests. Below is an overview of some of the challenges testers face.
- Slow processing: Object recognition algorithms must process elements quickly to keep up with real-time application changes. Any delays can lead to missed interactions and test failures.
- Constant changes: The ever-changing content of many real-time applications forces object recognition to adapt quickly.
- Need for concurrency: Automated tests may need to work with multiple elements one after the other or simultaneously. Object recognition must handle those operations without lagging.
- Resource limits: Real-time object processing takes up a lot of CPU and memory. Many testers run into challenges balancing application performance and object recognition needs.
Below are some strategies testers can use to overcome the above challenges.
- Optimizing Algorithms: For faster execution, try optimizing the algorithms used with object recognition using feature and template matching techniques.
- Parallel Processing: Use parallel processing to distribute the object recognition load task across multiple CPU cores. That helps reduce processing time and allows testers to handle concurrent operations more efficiently.
- Incremental Processing: This technique processes recognition results in different increments. That means the UI only captures results at specific intervals rather than reprocessing an entire form from scratch.
- Use of Data Structures: Create data structures like hash tables and spatial indexes to help manage and access UI element information.
Challenge #5: Integration with Other Systems
One issue UI testers encounter when using object recognition tools in other systems is compatibility. The platform may use protocols, data formats, and APIs that conflict with other software. You also want to ensure seamless data exchanges between the tool and other testing platforms.
One way to avoid those issues is to use standardized APIs and protocols during integration. RESTful APIs and WebSockets can streamline communications between object recognition software and other tools. If developers have the proper skill set, they can build middleware or adaptors to help translate data formats and protocols between different types of software.
Challenge #6: Learning from Limited Data
It’s hard to build genuinely robust data models with small datasets. That can lead to issues like:
- Poor generalization: There may be insufficient information to train models on UI element variations like screen size, themes, and dynamic content.
- Not enough variation: Using limited datasets to create models can lead to model knowledge gaps, which makes them less effective in assessing real-world scenarios.
- Bias: Smaller datasets are more likely to introduce bias, where model predictions get skewed towards the training data, neglecting other possibilities.
- Low Accuracy: A dataset may need more information to allow an object recognition model to learn enough about features to accurately locate and interact with UI elements.
The following strategies can help mitigate these issues:
- Data augmentation: Using transformations to increase the diversity of a dataset artificially.
- Transfer learning: Taking pre-trained models and fine-tuning them using a more limited dataset.
- Synthetic data generation: Testers can use synthetic data to set up realistic UI elements, which makes for more diverse training datasets.
Challenge #7: Adapting to New Object Classes
It can take time to train object recognition models using new classes. That often involves retraining the models to maintain accuracy with current and new classes. An example includes training a model to recognize new object classes while remembering all the previously learned info.
A common issue is limited data being available to create more robust models for the new classes. This makes it harder to ensure that models stay accurate without degrading performance.
In addition to transfer learning, testers can use incremental learning to overcome these challenges. For example, a method like Elastic Weight Consolidation (EWC) helps models avoid experiencing catastrophic forgetting, where they forget previously learned information when absorbing new data.
✅ Best Practices for Overcoming Object Recognition Challenges
Testers and developers can avoid having these issues impact their testing efforts with deep learning object recognition by following these best practices:
1. Utilize Stable Locators
To locate UI components, incorporate unique, stable attributes like IDs or RIA labels. Avoid using locators that are likely to change frequently.
2. Incorporate Data Augmentation and Synthetic Data
Data augmentation techniques make training models more diverse, which helps with generalization. When it’s hard to get accurate information for UI testing, try substituting synthetic data.
3. Use Advanced Model Techniques
Try using pre-trained models and updating them from more limited datasets. You can also use few-shot learning, a framework where models learn to make more accurate predictions from a few labeled examples.
🔍 Ranorex Spy Is Here to Help
Take advantage of the Ranorex Spy tool provided with Ranorex Studio to make object recognition easier. It has everything needed to conduct end-to-end application testing. Contact us for a demo if you’re interested in learning how we can help optimize your entire testing process.
