As we enter the digital age, accessibility is becoming a necessity. The growing technical complexity has strengthened the Software Development Life Cycle (SDLC) to make digital applications accessible to everyone regardless of their abilities. To deliver high-quality applications to users with disabilities, accessibility testing is important.
However, for large and complex digital applications, identifying and fixing accessibility issues can be a difficult task for developers. It needs deep clarity of accessibility guidelines, manual testing, time, and resources. This is where the need for machine learning arises.
Machine learning automates accessibility testing, helping testers easily identify and fix the issues. Combining AI and accessibility testing greatly improves the QA experience and helps in reducing the need for manual labour and achieving additional benefits.
This article will discuss the use of machine learning to identify and fix accessibility issues, including its types, techniques, challenges, and strategies for implementing machine learning in fixing accessibility issues. To start, let’s define accessibility issues and machine learning.
What are Accessibility Issues?
Accessibility issues are problems that prevent people with disabilities ( like visual, hearing, and cognitive disabilities) from accessing, using, or interacting with digital applications, services, or environments. It can arise from various factors like poor choice of design, technical issues, inaccessible content creation, insufficient colour contrast between background and text colours, missing alt text for images, inconsistent layout of web applications, technical language, etc.
These issues create exclusion and frustration in disabled users and provide them with limited opportunities for participating in digital activities.
Understating Machine learning
A subset of artificial intelligence called machine learning enables machines to learn and perform a variety of jobs. AI systems use ML technology to interpret learning processes and obtain insights. The study of machine learning focuses on how to make computers behave without explicit programming. It is a type of software technology that stores and performs statistical analyses on data using mathematical models and then uses the analysis’s findings to improve the models.
Machine learning mimics human learning by using data and algorithms, gradually improving its accuracy. Every day, ML is becoming more prevalent in people’s lives, from e-commerce recommendations and self-driving cars to computer games and speech recognition. Replication of human intelligence in robots is the aim of artificial intelligence. It endows systems with the ability to reason, perceive, and make judgment calls. The goal of AI technology is to create a problem-solving method that will allow the software to manage operations without the need for manual programming.
Benefits of using machine learning in fixing accessibility issues
Automated accessibility testing: Nowadays, testers need to use highly advanced methods to identify and fix accessibility issues and make high-quality applications available for all users, regardless of their abilities. The most effective way for testing in a continuous delivery cycle is AI-based automated testing. AI uses Machine learning to automate accessibility testing for analyzing digital applications and services, provides immediate feedback for improvement, and identifies issues that prevent disabled users from accessing or using the digital services and applications.
Improve accuracy: Machine learning algorithms can analyse large datasets and identify the patterns that indicate accessibility problems with high accuracy. It increases the accuracy and decreases the risk of false positives and false negatives, and hence helps the developers in creating more accessible and accurate digital products and services.
Enhanced user experience: Testing techniques are generally judged by their user-friendly features. Machine learning algorithms can analyse insufficient color contrast, missing alt text, enable personalized accessibility features, and provide proactive support to ensure a smooth user experience. A positive user experience can enhance brand reputation and is important for the success of web applications.
Types of accessibility issues
Visual accessibility issues: Visually impaired users often face this issue in reading, understanding, accessing, and using digital products and services. This is due to low colour contrast, inadequate alt text, inaccurate image descriptions, inconsistent font sizes, styles, and poor visual feedback. By leveraging machine learning, testers can identify inaccessible visual content like colour contrast, missing alt text, etc, and fix this issue by using alternative colours and generating descriptive alt text.
Auditory accessibility issues: Hearing-impaired persons generally face this issue in understanding and accessing digital applications. It is due to a lack of captions, inadequate transcripts, and poor audio descriptions in audio and video content.
Motor and cognitive accessibility issues: This issue stops the motor or cognitive disabled users from using, understanding, navigating, and interacting with web applications and services. It includes small click targets, complex gestures, inconsistent navigation, complex language, inconsistent design, and overwhelming content.
How machine learning helps to fix accessibility issues
Code generation: machine learning generates code, such as alt text for images or improving colour contrast. This code is accurate and helps the developers to minimise errors, improve the quality of applications, and provide immediate feedback.
Predictive remediation: Machine learning enables proactive fixes by predicting potential accessibility issues and provides recommendations for remediation.
Personalised experience and adaptation: machine learning enables personalised experience for disabled users and dynamically adapts digital applications according to users’ needs and ensuring accessibility and usability.
Continuous monitoring: To identify and fix accessibility issues, machine learning continuously monitors digital applications and enables remediation and improvement. It creates a feedback loop and allows developers to define and improve digital applications based on user feedback and accessibility testing results.
Machine learning techniques used in accessibility testing
Computer vision techniques
Computer vision techniques analyse the images and objects in videos to identify issues such as poor colour contrast or missing alt text, and the generation of descriptive text in images and videos in visual content. Developers generally use this technique to automate the accessibility testing process and improve accessibility for visually impaired users, and create more useful digital products and services that meet the needs of visually impaired users.
Natural language processing
This is an important technique of machine learning to improve accessibility issues for disabled users. It recognises spoken language and generates text-to-speech output by creating interaction between computers and humans in natural language. It enables disabled users( visual or hearing) to access written content and use all digital products and services using voice commands.
It also allows developers to translate text from one language to another and make digital services more useful to impaired users or users who speak different languages, and helps to provide a good user experience.
Machine learning algorithms
ML algorithms are a key technique to identify accessibility issues in digital products and services. It detects images and provides descriptions for visually impaired users. It is also useful for deaf users as it provides captions after recognizing spoken language. This algorithm has predictive modelling ability, which predicts user behaviour and provides personalised support for disabled users. There are two types of machine learning algorithms.
- Supervised learning- It is trained on labelled data where it learns to recognise patterns in the data and identify accessibility issues such as poor colour contrast, missing alt text, and consistent navigation.
- Unsupervised learning- These are trained on unlabelled data, where they discover patterns and anomalies in the data to identify potential accessibility issues. Developers used this algorithm to identify unusual navigation patterns or unexpected changes in layout.
Challenges of machine learning in identifying and fixing accessibility issues
Data quality- Machine learning algorithms require high-quality and sufficient data to learn and make accurate predictions for solving accessibility issues. If the data is incomplete, the algorithm may not function well.
Bias and fairness-The capability of perpetuating and amplifying biases from the training data leads the machine learning model to unfair and discriminatory outcomes. If the machine learning algorithm is not carefully designed for fairness and transparency, then it starts to show its biased nature. In some accessibility issues, it faces difficulty in understanding the context, which leads to false positives or false negatives.
Contextual understanding- Due to a lack of understanding and domain knowledge, machine learning models do not fully understand human behaviour, language, or context and which leads to misinterpretations and misunderstandings.
Strategies for implementing machine learning in fixing accessibility issues
Integrating with an ML-based platform: Integration of machine learning with an ML-based platform enables developers to easily identify and fix accessibility issues. It helps testers to guarantee the quality of the applications and provides it to all users regardless of their abilities.
AI-powered platform analyses and fixes the accessibility issues in a short amount of time, and greatly improves the QA experience while also helping to reduce the need for manual labour and achieve additional benefits. One such AI-based automated testing platform that employs Machine Learning and Artificial Intelligence to accelerate the creation and maintenance of automated tests is LambdaTest. It provides a range of testing tools, including accessibility testing tools, enabling developers to improve their capabilities to test their web applications for accessibility issues.
LambdaTest is a cloud testing platform. With this platform, users can perform manual and automated testing for web applications across more than 3000 environments, and real devices hosted on the cloud. To help testers quickly start and run accessibility testing, LambdaTest provides a wide range of extra functionalities. Its primary capabilities include geo-location testing across several regions, support for many browsers’ live browser testing, parallel testing, resolution display testing, automatic screenshot testing, and testing in multiple locations at once.
The ability to build reliable, codeless, and scriptless automated tests is among its benefits. Moreover, testing infrastructure is fully maintained in the cloud, tests can be scaled infinitely, and they can all be run simultaneously. In addition, it offers features
like increased test coverage, less code maintenance, quick debugging, cross-browser, cross-device testing, responsive design testing, etc
Prioritise user-centred design and inclusivity: Consider the diverse needs of disabled users and choose design interfaces, development, and interaction according to their adaptability and accessibility.
Model Development and Training: Choose the right machine learning algorithm for a particular accessibility task and train the model with diverse data for the generalisation of new scenarios. For development, continuously evaluate their performance.
Using Several test scripts: When there have been updates, alterations, or changes to the code, it might be challenging for testers to estimate how many tests are necessary. Artificial intelligence and machine learning-based accessibility testing systems can determine if an application needs multiple tests or not. This helps to save time and money by avoiding the execution of ineffective test cases. Also, it is practical to evaluate a system’s overall performance without repeating the test scripts.
Foster collaboration and transparency: Encourage collaboration, feedback, and contribution between users from different fields and developers to improve the accessibility of the machine learning solution.
Conclusion
In conclusion, Machine Learning (ML) is the next step of automation toward accessibility. Using this approach, developers can create more accessible and inclusive digital applications and services that meet the needs of users with disabilities.
The testers can also utilise ML to examine and enhance the automated testing process with little to no human involvement. It gives testers the ability to automate test cases, improving accuracy and efficiency. In parallel, tools like Selenium ChromeDriver play a key role in executing browser-based automation, allowing teams to run tests on Chrome with precision while integrating ML-driven enhancements for smarter and more efficient workflows.
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