Have you come across any modern application that hasn't included even one AI feature? No? This is because artificial intelligence has become one of the most important components of providing the best digital experience to your customers. The inclusion of AI in web development also asks for including it in the testing process as well.
While using AI for software development and testing, various people are also concerned about how their data is being used and whether the organizations are complying with the regulatory and privacy compliances. Ensuring that your application remains accessible even after implementing all of these advanced and dynamic features can be a very challenging process.
Struggling to implement accessibility testing with AI test tools? Don't worry! We are here for you. Our article will help you understand how AI is an important factor in accessibility testing and some of its major capabilities to solve accessibility concerns. Finally, we'll also go through some of the best practices for including AI-based accessibility testing in your software testing workflow.
Why Accessibility Testing
Before we go into the process of verifying your software's accessibility with AI-powered testing tools, we must understand that why should you even bother about investing so much time and resources in this process. To shed more light over this segment, let us go through some of the major factors that justify the inclusion of accessibility testing:
- Digital platforms have to serve all users effectively and with full respect. Therefore, people who use screen readers or keyboard navigation should have the same level of functionality and information access, like all those using a standard mouse. This is impossible to implement without proper accessibility testing.
- There are various countries which have strict regulations to implement web accessibility. For example, in the United States, you have the Americans With Disabilities Act and Section 508 of the Rehabilitation Act. These demand all the public and private websites to meet with proper accessibility standards. If you fail to comply with all these regulations, you can face severe lawsuits, penalties, and damage in terms of reputation.
- Implementing software accessibility is not just about complying with regulations, it's about implementing the ethical values of inclusivity and fairness. If your organization invests in accessibility validation, you are showing a commitment to social responsibility. Factors like these will play a very important role in building a positive brand reputation.
Key Capabilities of AI-Powered Accessibility Tools
Now comes another important question in this regard: with so many accessibility testing tools available in the market, why should you bother about investing in AI-powered tools. To answer this question, let us go through some of the major features that are available with only AI test tools for accessibility validation:
Automated Detection of Common Issues
If you observe closely, you will see that most AI-powered tools have a foundation similar to traditional accessory checkers. This means that they will be scanning for missing alt attributes, invalid HTML tags, or insufficient color contrast.
The magic of artificial intelligence is that apart from all the above processes, it also adds learning from previous errors. It implements this by analyzing thousands of pages and refining the prediction algorithms. The advantage of this process is that it reduces false positives and also broadens the coverage.
Image Recognition and Alt Text Generation
AI test tools have computer vision models that can interpret images and automatically suggest the alt text. This will be a massive benefit for ecommerce websites that have huge product logs or news platforms that are constantly updating the news images. Although human reviews will still be required to guarantee the accuracy, AI will reduce the labor time for finding the images.
NLP for Content Clarity
NLP or Natural Language Processing can verify whether link text and headings accurately describe the destination or the context. For example, “Read More” will be a vague text compared to “Read More About Our Sustainability Efforts”. If you use an AI-driven NLP engine, it can analyze all the labels and suggest better alternatives.
Context-Aware Recommendations
Apart from finding errors, AI can also propose contextually relevant solutions like color contrast issues that are existent in a site's banding. In this scenario, the AI tool will suggest alternate palette options that will align with the brand's color schemes while improving the overall accessibility of the website.
Live Monitoring and Alerts
Some AI platforms will offer continuous monitoring to track changes in content or structure. This means that when new issues appear, the system will send alerts allowing immediate remediation. This will be a very important part of the agile methodologies to ensure that issues are found out at the previous stages before they can turn into serious concerns.
Popular AI-Powered Accessibility Tools
To help you understand the role of AI test tools in accessibility validation, let us go through some of the major AI-powered tools that are currently available in the market. From the hundreds of options that are currently out there, we have chosen only the most popular ones that will serve almost every use case:
Microsoft Accessibility Insights
This tool is primarily created with an AI-driven engine. It will provide automated checks along with guided manual assessments to understand the accessibility maturity level that has been implemented on the user interface of your application.
Microsoft Accessibility Insights acts as a browser extension which can evaluate single-page web apps, static websites, and also native Windows or Android applications simultaneously. This means you can use this tool to verify your accessibility on both desktop and mobile devices.
Axe DevTools
Deque’s Axe engine is a very popular scanning tool in the entire accessibility community. Although its foundational tool is rule-based, it comes with newer offerings that can integrate machine learning to reduce noise and also discover more nuanced issues within your application infrastructure.
It also smoothly integrates into your development pipeline to help you implement the testing process on multiple platforms, including both mobile and web counterparts.
Google Lighthouse
Although Google Lighthouse isn't entirely AI-based, it continues to evolve with machine learning techniques that allow it to refine the audits. Using this tool, you can run a suite of tests like accessibility, performance, and many more that can be easily integrated within your automation workflow.
This tool has also gone through various recent updates that hint at upcoming deeper accessibility insights that will be backed by AI-driven pattern analysis.
Siteimprove
Siteimprove is an AI accessibility tool that uses the power of artificial intelligence to provide comprehensive scans of multiple websites. During the scanning process, it will highlight issues like broken links, SEO concerns, and accessibility violations.
Using its AI-driven analytics, it allows multiple organizations to prioritize the most critical issues to ensure that their application is accessible to all users.
LambdaTest
If you want an AI platform to execute real device testing, LambdaTest will be your best option. LambdaTest is an AI-powered test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ real devices, browsers, and OS combinations.
With this platform, you can not only execute real device testing but also advanced testing processes like automated cross-browser testing and AI QA testing. Moreover, after running all the testing scenarios, LambdaTest will automatically display a comprehensive test report consisting of multiple multimedia elements like screenshots and videos to find the faulty elements and implement the resolution steps.
UserWay AI-Powered Accessibility Solutions
Finally, UserWay AI Solution will automatically scan your application for accessibility issues and fix the most common ones, including all text generation and color contrast adjustments.
It is primarily available as an overlay widget that has been designed to provide immediate frontend fixes. It also has a deeper scanning capability to identify structural issues which will require direct remediation within the source code of the application.
Best Practices for Integrating AI Tools into Development Workflows
Finally, let us go through some of the best practices that the testers must consider while integrating AI tools into their application development workflows. These practices will not only improve the overall scalability of the infrastructure but will also have an indirect role in improving the quality of the application that is being developed:
- We suggest the testers to adopt a shift-left approach to integrate AI testing tools at the earlier phases of the development cycle. This will allow you to find errors and fix them before they can deeply integrate within the architecture of the application. It is a very important task to save both development time and resources.
- You must remember that AI testing will not be able to completely remove manual testing from the accessibility testing process. Instead, you must create a proper balance between AI and human testers to enhance the entire software testing workflow.
- We suggest the testers to add AI-powered scans within the continuous integration and continuous deployment pipelines so that whenever new changes are made to the code, the tests are automatically initiated to maintain the level of accessibility within the software infrastructure.
- You will see that there are various AI testing platforms that allow custom rules within the testing workflow. So you can configure these tools to reflect your organization specific standards or brand guidelines. This approach will also help reduce irrelevant alerts and focus the tool on the unique design patterns that you have chosen.
- Finally, you must provide your developers, designers, and content managers with training on both accessibility fundamentals and how to understand the AI tools test reports. This is because if the teams understand the ‘why’ of the entire process, they will have a clear understanding about the ‘how’ when it comes to fixing the errors.
The Bottom Line
Based on all the factors that we have put forward in this article, we can easily say that AI-powered accessibility testing tools will have a very important role in implementing innovation, inclusivity, and compliance within your software development and testing workflow.
You must also remember that AI isn't the supreme power in terms of technology. To use its full power, you must integrate it with manual reviews, genuine engagement with disabled users, and a corporate culture that celebrates inclusivity. With the evolution of AI models, we must expect that accessibility testing will become more precise and more deeply integrated with all the continuous deployment pipelines.
Finally, you should also remember that accessibility testing is a collective operation where the developers, testers, product managers, and QA teams have to work together to implement it as a core organizational value rather than an afterthought. Your final goal should be to create digital experiences that welcome everyone, without any exception.