Alice, the founder of Technoville, is famous for her dedication to creating flawless applications. One day, a massive project lands on her desk. Rather than risk the large, data-intensive project on human testers, Alice decides to leverage Artificial Intelligence in testing.

Alice was ahead of her competitors in adopting AI testing, and was generally criticized for the unnecessary expense. Many thought that Alice would regret wasting her money on a ‘gimmicky’ investment.

One day, as the testing phase was underway, the AI testing tool detected a subtle but critical flaw in the application’s payment gateway. This flaw, unnoticed by human testers, could have led to financial discrepancies. Thanks to the AI’s precision and inability to get bored or make mistakes, the team rectified the issue, avoiding a potential disaster upon launch of the app.

Artificial Intelligence (AI) is revolutionizing the field of software testing by enhancing efficiency, accuracy, and speed. Let’s explore.

Here’s how AI helps with testing.

AI-powered testing tools autonomously generate test cases based on the application’s functionalities. They detect patterns in user behavior, predicting where potential issues might arise. Automated tests can be executed in parallel, providing rapid feedback to the development team. Of course, not every AI tool will deliver the same features, or to the same extent.

Test Case Generation

Automated Test Creation: You can save significant manual effort with automatic test case generation based on AI algorithms’ analysis of available specifications

Data-Driven Tests: You can also save time on manual planning by allowing AI to use test specifications to identify relevant data combinations to ensure comprehensive coverage without wasting time on manual planning.

Test Execution and Analysis

Automated Test Execution: AI-powered tools automate test execution; this will especially help you in regression testing scenarios.

Smart Test Prioritization: Which test should you run first? AI analyzes code changes and user behavior to prioritize tests, focusing efforts on high-impact areas and critical functionalities.

Bug Detection and Root Cause Analysis

Automated Bug Detection: You can use AI tools to identify patterns and anomalies in code, pinpointing potential issues and vulnerabilities that might be missed by manual testing.

Root Cause Analysis: Since AI analyzes code changes, test results, and system behavior to identify the root causes of defects, it helps your developers with rapid issue resolution.

Predictive Analytics

Defect Prediction: You can predict potential defect-prone areas with AI-powered historical data analysis. This enables focused testing efforts on critical modules.

Release Readiness: Are you release-ready? AI assesses the application’s readiness for release, predicting risks and issues that might affect the user experience.

Regression Testing

Self-Healing Tests: AI identifies and corrects flaky tests. That means you can be sure that test results are reliable and not affected by transient issues.

Continuous Regression Monitoring: Do you test every time new code is written? You should! AI constantly monitors applications for regressions, and automatically triggers tests whenever new code is introduced.

When should I use AI testing?

Increasingly, AI automation is gaining popularity as the best fit for your testing tool. Ensure you opt for AI-powered testing automation if

  • manual testing is inadequate for any reason, especially for the application’s critical functions
  • you have multiple repetitive tests, numerous BVT cases, or regular regression test iterations
  • The test parameters require the simulation of multiple users accessing the app in parallel

How do I select which AI tool to use?

There are several AI test tools available in the market, with more launching by the day. How do you select the right one?

First, assess your own team’s innate skill sets, time availability and other requirements. Do you need a codeless testing tool? Which tools do your team members already have experience with? Within the universe of AI testing automation, you may prefer to choose a low hanging fruit. We’re referring to whichever tool can be used right away, without training. Also investigate what collaboration and integration support is offered by the platform. Is 24×7 support available?

Next, sort through your project’s specific requirements. Are you using a specific programming language for the testing automation? What kind of application are you testing – desktop, API, mobile or web app? Will you need cross-device testing? What platforms or browsers require testing?

Ensure that your chosen tool includes data-driven testing capabilities and comprehensive reporting features. Can you run multiple tests on a single data set? Does the tool provide a clear outline of the failed test cases, with screenshots to highlight? How long does it take to execute the test and report?

Of course, it’s also important to keep your budget, ease of use, ease of maintenance and reusability in mind.

Take the decision in collaboration with your development or quality assurance partner. Ziffity would be happy to discuss this subject with you to help you understand your options.

Which tool should I choose?

Different tools offer different features and benefits. Here’s a few popular tools you could consider:

Applitools

This visual AI testing tool can function across environments to ensure UI consistency. It’s completely automated, offering faster testing with minimal code. It provides

  1. Automatic validation of visual appearance of application
  2. Detection of visual bugs/differences across devices and browsers
  3. Accurate rendering of UI elements across devices and browsers
  4. Accurate defect detection and visual regression testing to identify visual discrepancies
  5. Predictive analytics and reporting for UI testing

Testim

Testim is an automated testing tool that offers real-time predictive analytics and reports on test executions. It reduces the need for manual scripting and uses machine learning algorithms to identify defects from patterns in test results. Testim provides

  1. Faster authoring, execution, and maintenance of automated tests
  2. Automatic identification of various elements on web applications
  3. Machine learning for self-learning test automation and dynamic test creation
  4. Autonomous testing for smart test maintenance with self-healing capabilities
  5. Parallel test execution for faster testing cycles

Mabl

This intelligent, low-code AI test automation platform enables high-velocity software teams to tightly integrate automated end-to-end tests into the entire development lifecycle. Mabl provides

  1. Automatic test maintenance and identification of regressions
  2. Autonomous testing capabilities to create and maintain tests easily
  3. Auto-healing tests, dynamic test creation, parallel test runs, integrations with CI/CD tools
  4. Accurate defect detection using ML, to identify patterns and anomalies
  5. Detailed reporting and analytics, highlighting trends over time

Functionize

Functionize is a cloud-based intelligent testing platform that uses NLP to turn plain text into automated tests. It provides

  1. Intelligent autonomous test creation based on application behavior and user interaction
  2. Self-healing tests to keep tests functional after changes in application
  3. Auto-healing tests, dynamic test creation, parallel test runs, integrations with CI/CD tools
  4. Detailed predictive analytics and reporting, focusing on test efficiency and coverage
  5. Complete control of all smart tests

TestCraft

TestCraft leverages the power of GPT-4 to act as an AI-powered assistant in software testing. Using AI, TestCraft automatically generates tests for Cypress and Playwright. It provides

  1. Intelligent autonomous test creation based on application behavior and user interaction
  2. Self-healing tests to keep tests functional after changes in application
  3. Auto-healing tests, dynamic test creation, parallel test runs, integrations with CI/CD tools
  4. Detailed predictive analytics and reporting, focusing on test efficiency and coverage
  5. Complete control of all smart tests

Artificial Intelligence is the future of automation. AI’s ability to handle complex, repetitive tasks allows testers to focus on the creative and exploratory aspects of testing, ensuring a holistic approach to software quality assurance. With AI, testing teams can significantly enhance their productivity, catch more defects and reduce testing cycle times. Ultimately, this results in higher-quality software deliverables.

It’s crucial to choose the right tool, and use it the right way, to get the most out of AI in your quality processes. While demos, trials, and case studies can provide valuable insights into how these tools perform in real-world scenarios, include a trusted QA partner like Ziffity in the decision-making process. Talk to us about how to get started today.