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The Next Testing Frontier: How to use AI in Testing

AI is everywhere, but how can you use AI for testing? In this article, we break down AI’s impact on testing and test automation

Testing isn’t easy, but AI makes it a little more simple.

By introducing intelligent algorithms and automated systems that can analyze vast amounts of data and make informed decisions, AI has been transforming software testing. In this article, we will show you how you can use AI in testing

Checking in with AI and Testing

The last few years has seen a world obsessed with artificial intelligence, how it can be applied, and where it’s going next. A recent Pew Research poll found that 27% of Americans interact with AI every day, and another 28% interact with it several times a week.

Testing and test automation are finding new applications with AI. Even with its flaws, AI is making testing easier, faster, and more accurate. According to a recent article by Forbes:

“Cognitive technologies that simulate activities of the human brain, such as machine learning, have stepped up the rhythm of testing and product releases in a major way. This has led to novel software testing approaches that deliver more refined applications with error-free functions delivered in less time and without programming.”

What QA Teams Should Know About Machine Learning for Software Testing (Forbes.com)

It’s the increased time to value that makes AI so promising. So, what exactly is test automation improving? How can you use AI for testing?

How to use AI in Testing

Here are a few ways AI is being used in testing.

Prioritizing and Optimizing Test Cases

You can utilize AI and Machine Learning algorithms to identify critical test cases for your mobile app. These tools analyze the application code base, document functionality, and write test cases to ensure code coverage. Some tools can even identify gaps in test cases, revealing opportunities for improvement.

AI helps prioritize tests based on user impact, code coverage, and potential risk level, letting testers spend more time validating critical functionality. With AI, test suites are optimized to maximize efficiency, reduce redundancy, and find the quickest path to code coverage.

Test Automation

Test automation is a cornerstone of a successful development lifecycle. In fact, It’s absolutely critical for any organization that hopes to make the important shift left. AI-powered testing frameworks are helping to automate repetitive testing tasks even faster.

  • Generate test cases by analyzing requirements, code, and past test data. It can identify edge cases, boundary conditions, and combinations of inputs to create comprehensive test scenarios.
  • Generate test data by understanding the application’s behavior and data dependencies. From there, It can create realistic and diverse datasets for testing.
  • With AI, test execution can be more fluid and dynamic. It is able to prioritize test cases based on risk, code coverage, and historical data. Again, less redundancy, more value.
  • AI can analyze test results and data to identify patterns to predict potential defects. By leveraging machine learning techniques, it can provide insights into which areas of the application are prone to errors so QA teams are spending their time wisely.
  • Deep context helps AI understand the intent behind the test, rather than just binding to an element or a location on the screen. This makes tests much more resilient and less prone to breaks.

Prediction and Analysis

Besides analyzing historic patterns, trends, and data, AI-powered technology can help with real-time monitoring, logging, and observability: AI can help continuously monitor testing environments and log parameters like code coverage, performance metrics, and user interactions. By processing real-time data, AI can detect anomalies from expected behavior. Proactive monitoring helps identify issues early on.

AI algorithms can also perform statistical analysis on test results to assess the risks associated with specific components or functionality. Static code analysis can dive into code complexity, test coverage, and historical defect rates while an AI model estimates the likelihood and impact of potential defects.

By analyzing bug reports, AI can also help with bug triage. For example, this tool might automatically assign severity levels, identify duplicate bug reports, or suggest root causes. This automated triage process will naturally accelerate bug resolution and help critical defects receive quick attention.

Natural Language Processing and Test Generation

NLP has come a long way in the last few years – especially with the release of GPT-4 and the wide adoption of AI technology. Even as early as 2021, computer scientists were struggling to create consistent test automation from natural language. The effort needed large data sets, extensive pre-processing, and refined parameters.

Tools like Sofy can now create manual test steps directly from a link to a Jira story – all thanks to their recent introduction of Co-Pilot. AI and Machine Learning algorithms can translate user stories, acceptance criteria or other documentation to create executable test cases. This allows QA teams to streamline test creation and enhance communication between testers and developers.

User Experience Testing

Users are complex and unpredictable. Some tools are using AI and computer vision to monitor user interactions and feedback within an application to find out how your users are interacting with your application. This lets you capture user behavior and sentiment to identify usability issues, bottlenecks, and optimize user experience.

Sofy and Co-Pilot

We think we’re on to something at Sofy. While we were improving our codeless mobile automation platform, we were also busy building AI capabilities. Now, with the release of Co-Pilot, QA teams can harness the power of OpenAI’s GPT-4 to get real time feedback on test performance, generate manual test steps automatically, quickly address code coverage issues, and discover regression issues.

You can also go to SofyBot, your personal AI companion, with any of your testing questions:

  • What are some recent test results?
  • Share insights from device metrics.
  • Why did my test fail?
  • Summarize my last test run.

If you’d like to see what Co-Pilot can do, give it a spin.

Conclusion

AI is opening doors for testers. Using AI in testing making the software development lifecycle faster, more accurate, and more agile. With the increasing pace of the application market, QA teams, developers, and any organization that relies on delivering quality software has a responsibility to adopt superior methodologies. AI has its flaws, but its promising future has just begun.

Are you ready to adapt?