10 Ways to Use AI in Software Testing

A Smarter Approach to Quality Testing

It’s the era of smart testing and using AI in Software Testing

The complexity of applications across domains has significantly increased over the years, making software testing critical to ensure that the system, as a whole, meets both functional and non-functional requirements. 

Teams must rethink how they develop and test applications. Test automation improves efficiency and accuracy in the software testing process. However, traditional test automation frameworks often require significant manual effort in script development and maintenance, limiting their scalability and effectiveness. 

AI-powered tools and testing services like Webomates address these challenges by leveraging AI and ML algorithms to enhance various aspects of test automation, from test case generation to result analysis.

10 Ways AI is Revolutionizing Software Testing

Embedding AI into your quality engineering processes helps to turn the usual into extraordinary, offering a transformative approach to test automation. 

Get ready to dive into the top 10 ways AI can transform your test automation process.

1. Visual Testing

Have you ever clicked on a website and found buttons out of place, text overlapping, or images missing? These visual inconsistencies can frustrate users and damage your reputation.

Visual testing tackles this challenge by verifying the visual aspects of an application’s user interface (UI) or graphical components. It detects any unintended visual changes, such as disrupted layout, font changes, or hidden or invisible text. By integrating visual testing alongside other automation techniques, you can boost the quality of the product within the same timeframe.

Pro Tip: Webomates performs Visual testing with the Galen framework for testing the layout of web applications. It validates the layout with Galen specs in all browser configurations. 

2. Intelligent Test Case Generation

Is your application stable after undergoing modifications? How often do your tests fail due to developers making changes to your application?

AI can analyze requirements, code changes, existing test cases, and code to automatically generate relevant test cases that include any boundary conditions, and potential edge cases too.  The AI-powered model-based testing approach automatically generates the test cases and scripts from a model, and is executed and checked based on the formal specifications of the system under test.

Pro Tip: Webomates CQ Setup process uses a multi-stage AI engine that leverages the power of Generative AI to generate test strategy, test cases, and test scripts. It can reduce the test case generation time to days instead of weeks. You can free your testers from repetitive tasks and focus on strategic testing with the power of AI. 

3. Self-Healing Test Automation Scripts

Agile development thrives on continuous improvement, but frequent code changes can put a strain on traditional test automation. Test cases become fragile and require constant maintenance, slowing down the release cycle.

AI-powered test automation frameworks can understand these changes and intelligently adapt to code changes. They can:

• Self-heal test scripts: AI can automatically self-heal the tests and re-execute them within the same cycle, eliminating manual intervention and saving valuable time.
• Reduce maintenance overhead: As your code evolves, the AI seamlessly adjusts test cases, keeping your automation suite efficient.
• Accelerate release cycles: By minimizing the impact of code changes on testing, AI helps you deliver high-quality software faster.

Pro Tip: Webomates guarantees that with AI Healing, all Test cases and test scripts will be healed in 24 hours, while a subset or a module of test cases and test scripts will be healed in 8 hours and retested within the same test cycle.

4. Test Data Generation 

AI algorithms can analyze existing data sources, such as databases to understand data patterns. Using this knowledge, AI can generate entirely new datasets that mimic real-world data. This synthetic data preserves the structure of your original data, but with a crucial twist: it masks sensitive information. By simulating various scenarios and edge cases, AI produces diverse datasets for testing different functionalities and scenarios. 

Pro Tip: Webomates test automation solution facilitates the regular updating of test data with minimal human intervention.

5. Enhanced Defect Prediction and Reporting

Did you know that failure can also be a False Fail which means there may be no defect and the system may be working as expected? A lot of time is spent on understanding whether or not there is an actual defect in the system. 

AI algorithms can analyze past defect data, including defect types and frequency to identify trends and patterns. Tools like the AI Defect Predictor tool can help you save massive amounts of time spent in analyzing the defect. 

Pro Tip: You can streamline your defect management with Webomates. Webomates helps you with:

• Defect identification, triaging, and tracing the defect to its origin. 
• Access and initiation of software tests and test results for your team. 
• Reporting details like defect summary, steps for replicating the defect, priority suggestions, and the test cases mapped with the defect
• 20-second video recordings for every defect identified, thereby reducing the time consumed during the review process.

6. Increase Your Test Coverage

Worried about security gaps in your application? What if you miss out on testing an edge case?  

Traditional testing methods often struggle to achieve comprehensive test coverage. AI steps in to bridge this gap by analyzing requirements, code, and existing tests to automatically generate a diverse set of test cases, covering edge cases, and positive and negative cases that manual testers might miss.

But does that mean you can automate ALL tests? AI can help to prioritize test cases that are the right tests for automation. With this, teams can unlock a myriad of benefits, from increased efficiency and speed to enhanced test coverage and early defect detection.

Pro-Tip: Webomates offers regression testing as a service that is a combination of test cases based on testing and exploratory testing. 

7. Automating Mobile App Testing

Mobile applications dominate today’s digital landscape and all devices are IoT-connected. However, ensuring a flawless user experience across a vast array of devices and operating systems (OS) can be daunting.

Leveraging AI in mobile testing can help to test the UI, functionality, performance, user-friendliness, and security of an app. AI can also help run numerous tests in parallel, saving valuable time and resources.

Pro Tip: Along with supporting multi-platform and cross-browser testing, Webomates can seamlessly integrate with BrowserStack, Saucelabs, and AWS devices to help users test their app layouts and designs on real devices. It also leverages the power of crowdsourcing for network testing to help its customers provide diverse mobile app testing services.

8. Automate Security Tests 

Is your application secure from vulnerabilities, external threats, exposure to sensitive data, and intrusion?  

AI-powered Security Testing can identify threats and detect vulnerabilities like SQL injection and cross-site scripting (XSS). Embracing security testing in an existing QA framework significantly improves the application’s test coverage, thereby resulting in a secure application. 

Pro Tip: By using AI in Security Testing, Exploratory Testing, and Performance Testing, you can safeguard your application by making the system hack-proof and sustainable.  

9. Test Optimization and Maintenance 

AI can help in analyzing the code changes, data, and historical test results to understand the potential impact of modifications. 

AI plays a crucial role in optimizing your testing strategy by focusing on: 

• Identifying the Testing Area: AI analyzes changes and user behavior to pinpoint areas most susceptible to defects.
• Recommending Test Methods:  AI suggests the most efficient approach such as Manual testing or crowdsourcing, or automation for each test case.
• Failover and Prioritization: AI can handle unexpected scenarios, prioritizing critical tests if failures occur.
• Test Maintenance: AI-based testing tools are capable of keeping the test data source updated. 

Pro Tip: Intelligent automation focuses on test optimization as the right set of tests that are most likely to uncover defects using the right testing methods.

10. Testing APIs

API Testing involves verification of functionality, performance, and robustness of APIs along with how it handles the load tests. AI can generate diverse and realistic test data sets, including valid, invalid, positive, negative, and boundary values. This ensures the API can handle unexpected inputs and edge cases, leading to more comprehensive testing.

Pro Tip: Webomates tests APIs using Manual and Automation testing. Tools like Postman, Swagger, JMeter, and API Fortress are already integrated into its Testing as a Service platform.

Speed Up Releases, Deliver Faster: Unleash the Power of AI

As teams strive to deliver the highest quality products in an increasingly competitive market, embracing AI in test automation is not just a choice but a necessity for success.

The faster you test, the faster you can release! In today’s competitive landscape, time to market is everything. Traditional testing methods can slow you down, delaying releases and hindering the ability to capitalize on opportunities.

By implementing AI-based software testing, organizations can accelerate their testing processes, improve test coverage, and deliver higher-quality software products.

Partner with Webomates!

Whether you want to bring new software to market or transform your testing – we are here for you with the right skills and capabilities to help you succeed. 

Ready to embark on your journey with intelligent testing? Explore how AI and Webomates can revolutionize your development and testing processes and empower you to deliver exceptional software.

Tags: AI, Artificial Intelligence, Artificial Intelligence In Testing, Software Testing