Web Testing: A Complete QA Overview

Hari Mahesh

Websites have made many aspects of our lives easier and more convenient. We can pay bills, apply for government services, purchase items from stores worldwide, and book travel accommodations in foreign countries with a few clicks. As a result, having a website for a business has become essential for survival in today’s marketplace.

Currently, web applications are no longer static or fully deterministic. AI-driven features, rapid UI generation, personalization engines, and continuous deployment pipelines have fundamentally changed how web applications behave and evolve. As a result, web testing is no longer just about validating predefined requirements, but about continuously assessing risk, behavior, performance, and user experience across rapidly changing systems.

Simply having a website or a web application is not sufficient, though. Many factors must be taken into account, including the following:

• The application must load quickly and remain stable even when multiple users access it simultaneously.
• The website should display consistently on all screen sizes and browsers (don’t forget about mobile browsers!).
• The application must be free of functional bugs or issues.
• Navigation links and menus should be easily accessible.
• The website must be secure.
• The website must be accessible to people with disabilities.
• International and localization standards must be followed when the same website is accessed in different regions.
• The application should behave consistently even when AI-driven or dynamically generated content is involved.
• The system should adapt safely to frequent UI and backend changes introduced by AI-assisted development.
• User behavior patterns, including unpredictable or edge-case flows, should not break the application.
• Quality signals should be continuously monitored, not only validated before release.

QA teams are responsible for ensuring they take all the above points into consideration when forging a testing strategy. Different testing types are required to ensure all the above criteria are fulfilled. Different testing types are mentioned below, and we will go through each of them in the next section.

• Functional Testing
• Database Testing
• Compatibility Testing
• Accessibility testing
• Visual Testing
• Security Testing
• Performance Testing
• AI Behavior and Model Validation Testing
• Observability-Driven Testing and Production Validation

Functional Testing

The objective of functional testing in web applications is to ensure that the application’s functions and features work as expected, and that the system behaves correctly in response to user inputs. This involves testing various aspects of the application, including the user interface, application logic, data validation, error handling, and system integration.

Functional testing typically involves the creation of test cases that cover all of the application’s functions and features, with the aim of uncovering any defects or errors that may be present. The tests are designed to simulate real-world user interactions with the application, such as filling out forms, clicking buttons, and navigating between pages. By conducting functional testing, testers can identify and fix any issues in the application before it is released to users, ensuring that the application meets the requirements.

In modern web applications, functional testing goes beyond static test cases. AI-assisted testing allows teams to validate application behavior based on intent rather than exact steps, enabling tests to remain stable even when UI elements, layouts, or flows change frequently. This approach better reflects real user behavior in fast-evolving web systems. Read: Functional Testing Types: An In-Depth Look

We can typically break all functional tests into two parts: smoke and regression.

Smoke Testing

Smoke tests typically represent a small subset of tests necessary to verify the core functionality and determine whether the build is good enough to proceed with a deeper level of testing. It is typically performed every time a new build is deployed, and therefore is one of the first candidates for automation. Automating smoke tests allows teams to get results in minutes and reject the build almost instantly if any critical issues are identified. 

Smoke testing is increasingly augmented with AI-based health checks that automatically validate critical user journeys, service availability, and core integrations immediately after deployment. Instead of relying solely on predefined scripts, AI-driven smoke tests can detect abnormal behavior patterns and fail builds proactively. Read: What is Smoke Testing in Software QA?

Regression Testing

Regression tests are typically a massive set of tests, responsible for verifying the entire application’s functionality and making sure nothing is broken. These tests have to be executed at least once before every release, and can take multiple days (and sometimes even weeks) when done manually. Therefore, regression is another excellent choice for automation – literally saving the team days on each run, since automation tests don’t require human intervention.

Modern regression testing increasingly relies on AI-driven test selection, where only the most relevant tests are executed based on recent code changes, risk analysis, and historical defect data. This significantly reduces execution time while maintaining high confidence in application stability. Read: What is Regression Testing?

Database Testing

Database testing is performed to check the web application’s tables, schema, and other database-related components. It can also assert the database performance and query processing speed.

In AI-enabled systems, database testing also focuses on detecting anomalies in data patterns rather than validating only static values. AI-assisted tools can identify unexpected spikes, missing relationships, or abnormal data distributions that traditional query-based testing might overlook. Synthetic test data generation is also increasingly used to safely validate complex scenarios without relying on sensitive production data. Read: Database Testing Best Practices

testRigor provides support for database testing. It helps to check if data is correctly inserted from the web application by creating and executing queries. testRigor can connect to multiple databases and create complex queries for testing purposes, making the testing process faster and more efficient. You can find more details here.

Compatibility Testing

Compatibility in web testing means the web application should work properly and appear the same across all browsers, operating systems, and platforms. To achieve browser compatibility, the web application should have similar visual and functional features across all browsers and their different versions.

In modern web testing, compatibility testing is often guided by real user analytics and AI-based risk modeling. Instead of exhaustively testing every possible browser and device combination, AI helps prioritize the environments that matter most based on actual usage patterns and detected visual or functional risks.

Many websites contain JUnit, JavaScript, or AJAX for various functionalities. Therefore, ensuring that these scripts work without issues across all browsers and their versions is crucial.

OS compatibility ensures that the script or the interfaces are compatible with operating systems such as Windows, macOS, and Linux. Platform compatibility verifies that the web application is compatible with mobile browsers. Recent technology uses responsive templates to develop web applications, which means that when the application is opened on a mobile browser, the contents adjust to the mobile screen resolution, and the menu becomes easily accessible within the screen.

For testing compatibility, both manual and automation methods can be used. In manual testing, test cases must be executed under various combinations, such as the same browser with different versions, different browsers in the same operating system, the same browser in different operating systems, and different platforms. However, that would be highly inefficient and time-consuming to accomplish with manual testing.

testRigor’s automated tests are more executable specifications – meaning they can be reused across different browsers, saving time and effort. Read: Cross-browser Testing with testRigor

Accessibility Testing

Accessibility testing is critical to ensure that a web application is usable by everyone, including individuals with disabilities such as vision impairment, hearing loss, physical or cognitive conditions, and older age. This testing, also known as 508 compliance testing, is required by law for all websites to adhere to accessibility rules and regulations. Accessibility testing is increasingly treated as a continuous process rather than a one-time compliance activity. AI-driven accessibility validation can automatically detect issues related to contrast, keyboard navigation, dynamic content, and assistive technology compatibility, while human testers focus on complex usability and cognitive accessibility concerns.

Assistive tools such as special keyboards, screen readers, speech recognition, screen magnifiers, and text contrast can aid accessibility. Accessibility testing can be performed manually, which can be time-consuming and expensive since testers must try out each option, such as changing the font size to large and testing all scenarios, or hovering over text to check if the voice-over matches the text. Read: Accessibility Testing: Ensuring Inclusivity in Software

testRigor supports the automation of accessibility scenarios, allowing testers to quickly determine if a website complies with accessibility rules and regulations. Watch this video to learn more about automating accessibility testing with testRigor.

Visual Testing

Visual testing focuses on validating how a web application actually appears to users, ensuring that layouts, visual elements, and overall presentation remain consistent and usable across releases. In modern web applications driven by rapid UI changes, responsive design, personalization, and AI-generated interfaces, visual defects can occur even when functional tests pass successfully. Issues such as broken layouts, misaligned components, overlapping text, missing UI elements, or poor contrast often slip through traditional DOM- or logic-based tests. Visual testing addresses this gap by validating the user-perceived experience, making it a critical layer of quality assurance alongside functional, compatibility, and accessibility testing.

With AI-driven visual validation, visual testing no longer depends on brittle pixel-by-pixel comparisons. testRigor uses intelligent visual recognition to identify meaningful visual regressions while tolerating acceptable changes in dynamic content. This significantly reduces false positives and test maintenance, even as UIs evolve frequently. By expressing tests in a human-readable, intent-based format and combining them with visual validation, testRigor enables teams to continuously verify that critical user journeys not only work correctly but also look correct, ensuring a stable and trustworthy user experience across browsers, devices, and releases. Read: Vision AI and how testRigor uses it

Security Testing

Security testing involves evaluating the system’s ability to prevent unauthorized access, protect data, and maintain confidentiality and integrity. It is responsible for identifying vulnerabilities and weaknesses in the web application, including authentication, authorization, input validation, encryption, and error handling.

Security testing is vital because it ensures the web application is secure against cyber-attacks and that users’ sensitive information is kept safe from potential data breaches. A security breach can cause significant damage, including financial loss, loss of reputation, and legal implications. Moreover, in some cases, it can put people’s lives at risk, such as in healthcare applications.

Security testing is crucial in detecting vulnerabilities that attackers can exploit to gain unauthorized access or manipulate data. By identifying and fixing these vulnerabilities, the web application can be more secure and give users greater confidence in its ability to protect their sensitive data.

There are various levels of security testing that a company can perform. More often than not, it does not fall into the responsibility of QA teams – aside from verifying negative use cases when performing functional testing. While advanced security testing often involves specialized teams, QA plays an increasingly important role in validating secure behavior early in the development lifecycle. AI-assisted security testing can help identify abnormal input patterns, authentication weaknesses, and misuse scenarios during regular functional testing cycles. Read: Security Testing

Performance Testing

Performance testing is a non-functional testing method that verifies the performance of a web application under different conditions. This testing method primarily focuses on load and stress testing to determine how much load and stress the application can handle.

Load testing checks how the system performs when multiple users access the application simultaneously. For example, in an online shopping site that offers deals during Thanksgiving Day, around 500K users may log in simultaneously. Load testing checks if the application can handle such a large number of users without crashing, and how it manages simultaneous API or database calls.

In modern environments, performance testing extends beyond pre-release load and stress tests. AI-driven performance monitoring enables teams to detect anomalies in response times, resource usage, and user experience continuously in production-like environments. This allows teams to address performance risks before they impact end users.

Stress testing takes the application to the point where its performance starts to deteriorate. It helps to determine how much load the application can handle, even with maximum concurrent users. In the above example, if the maximum concurrent users are expected to hit 500K, then in stress testing, the user count will be increased past this number to determine when the application’s CPU or memory usage gets hit. Read: What is Performance Testing: Types and Examples

Performance testing can only be performed using tools, not manually. testRigor supports load testing of web applications, and there is no need to create separate scenarios. Load testing can be performed using the same scripts used for functional testing.

AI Behavior and Model Validation Testing

AI behavior and model validation testing focus on ensuring that AI-driven components within a web application behave reliably, safely, and in alignment with business expectations. Modern web applications increasingly rely on artificial intelligence for features such as search relevance, recommendations, personalization, chatbots, dynamic content generation, and fraud detection. Unlike traditional rule-based functionality, AI systems are often non-deterministic, meaning the same input may not always produce the same output. This makes validating AI behavior fundamentally different from conventional functional testing.

This form of testing emphasizes validating outcomes rather than exact responses. QA teams assess whether AI-generated outputs are appropriate, contextually relevant, and within defined boundaries, even when inputs are ambiguous, incomplete, or unexpected. It also involves testing edge cases, adversarial inputs, and negative scenarios to ensure that AI systems do not produce misleading, biased, or unsafe results. As AI models evolve over time through retraining or data changes, behavior can gradually drift away from original expectations. Continuous model validation helps detect such drift early, ensuring that changes in data, model updates, or environmental factors do not negatively impact user experience, reliability, or trust. Read: Machine Learning Models Testing Strategies

Observability-Driven Testing and Production Validation

Observability-driven testing and production validation extend quality assurance beyond pre-release environments and into real-world system behavior. In modern web applications with frequent deployments, feature flags, and continuously evolving functionality, it is no longer sufficient to rely solely on testing performed before release. Many quality issues only surface under real user conditions, where traffic patterns, data diversity, and usage behaviors differ significantly from test environments.

This approach focuses on continuously analyzing signals such as logs, metrics, error rates, performance data, and user interaction patterns to validate application behavior in production. These signals provide valuable insight into how the system actually behaves, enabling QA teams to confirm assumptions made during development and identify unexpected regressions or degradation. 

Production validation techniques, such as canary releases, gradual rollouts, and controlled feature exposure, allow teams to safely test changes with limited user impact. By combining observability data with automated testing, QA teams can refine test coverage, prioritize high-risk areas, and respond proactively to emerging quality concerns, making quality assurance a continuous, data-driven activity rather than a one-time gate. Read: Production Testing: What’s the Best Approach?

Building a Scalable Web Testing Strategy in the Age of AI

In terms of prioritization, functional testing is typically addressed first, followed by non-functional testing types such as load, performance, and security. When defining a web testing strategy, it is also useful to consider the traditional test automation pyramid, which provides a structured view of how different layers of tests contribute to overall quality and stability.

In AI-driven testing strategies, this pyramid is no longer static. It is increasingly complemented by an intelligence layer that continuously evaluates risk, analyzes user behavior, and monitors system changes to determine what needs to be tested, when testing should occur, and how deep validation should go. This approach allows teams to focus effort where it delivers the highest value, rather than executing large volumes of tests indiscriminately.

When choosing a test automation tool, it is therefore important to prioritize solutions that support AI-driven, low-maintenance testing approaches. Tools that rely on natural language, vision-based validation, and self-healing capabilities enable QA teams to keep pace with rapid development cycles while minimizing the ongoing cost of test maintenance. With these considerations in mind, testRigor provides an effective way to build robust end-to-end tests for web, mobile, and desktop applications while significantly reducing the effort required to maintain them over time.

“We spent so much time on maintenance when using Selenium, and we spend nearly zero time with maintenance using testRigor.”

Keith Powe VP Of Engineering - IDT