How to Apply AI in Software Testing?

The paradigm of software testing has evolved significantly over the past two decades. Right from manual to automation, where selenium was considered to be one of the finest tools, the testing journey has been quite promising.

With the dawn of AI-based testing, it has now become very impactful with the use of autonomous agents referred to as “test bots.” These intelligent agents help automate activities such as application discovery, modeling, test creation and failure identification. Their implementation is performed by combining different ML techniques including neural networks, tree learning, reinforcement learning and more.

• Differential: Helps test different versions of similar applications. Carries out a comparison to understand differences, versions overbuilds and learn from classification feedback.
• Visual: Image-based testing needs visual validation. With pattern and image recognition capabilities together, it helps detect visual bugs to test the look and feel of an application.
• Declarative: Aims to specify test intent in a natural or domain-specific language. It lets the system decide on how to conduct the test.
• Self-healing Automation: Helps auto-correct elements in tests with the change of user interface (UI).

The above-listed testing approaches help save time from days to mere hours which translates directly into cost savings. Interestingly, automated tests produce quality websites and applications.

The biggest challenge with AI-based testing is about choosing the right type. Of course, the optimization does not apply to all the models. Hence there’s a need for an enhanced setup and complex flow.

The other challenges of AI-based test automation include:

• Researching the exact use cases
• Lack of complete awareness of what actually is expected.
• Validating the actions of the applications based on the input.
• Analyzing the app for performance, functionality, scalability, security and stability.

From enhancing accuracy to tackling manual testing constraints, AI-based integrated solutions can considerably improve the overall process. This helps you to produce effective results, much faster. Setting up a dedicated system might have some initial investment, but over the long haul, it will certainly help you save effort and cost.

The application of Artificial Intelligence enables taking automation to a new level where the testers don’t need to deal with repetitive tasks anymore. Here are the core benefits:

• Functionize: A cloud-based automated tool that leverages ML for performance, function and load tests purposes. The tool utilizes intelligence capabilities to improve test creation, analysis & maintenance.
• Appvance: This tool uses AI technology to create test cases depending on user behavior. The test portfolio depicts what real systems will perform on the production systems. It helps enhance the quality, performance & security of the application.
• Testim: Automated functional tool uses ML to write, execute and maintain tests. It allows users to create E2E (end-to-end) tests that are coded, codeless, or both.
• Testcraft: AI-driven automation testing platform for continuous and regression testing. It is also used to monitor websites or web applications. Using this tool, testers can visualize automated Selenium-based tests through a drag and drop interface and run them on various browsers and work environments.
• Applitools: An automated intelligence platform for software monitoring, visual user interface tests and visual management. This is one of the widely used AI testing tools that scan app screens and analyzes them just like humans’ brains by identifying potential defects and bugs. It is widely used in DevOps, manual QA, digital transformation and test automation.

Our team of proficient QA & Software Testing professionals has hands-on exposure of the above-listed tools. As a next-gen QA and testing services provider, we can help you to get faster and quality releases with more efficiency and accuracy.

It is clear by now that AI in test automation helps address the limitations of traditional automated testing to deliver enhanced value. It enables organizations to validate quicker and better while reducing human dependencies and costs.

Let’s take a closer look at some real-life applications:

• Unit Testing: With AI-enabled RPA tools, testers can reduce flaky test cases while performing unit testing. It also helps manage unit test scripts maintenance.
• API Testing: Test automation tools leverage AI to convert manual UI tests into automated API tests. It uses reasoning to recognize the relationships and patterns in a series of API calls while exercising UI. By analyzing the user behavior, the machine intelligence can create more advanced inputs and patterns for verifying the API.
• UI Testing: Ensures more accuracy in comparison to manual testing. It is hard to manually detect parameters such as size, layout, Graphical User Interface (GUI), and colors, which can be easily identified with AI.
• Continuous Testing: As part of the continuous testing method, software professionals can determine modified controls with frequent algorithm updates.