Simplifying Testing with Predictive QA and Test Intelligence

Are Engineers Using AI for Software Testing?

According to Stack Overflow Development Survey 23.87% of development teams are using AI for software testing.

That does not sound like an exciting number. But numbers don’t often paint the complete picture. Do they? They don’t. Especially, when they are looked at in isolation.

AI developer tools are embraced with open arms by developers across the software development lifecycle (SDLC). From code generation to debugging, documentation, reviews, software testing, deployment, and infrastructure monitoring, AI is ubiquitous across SDLC.

• 82.55% of developers already use them to generate code.
• 48.89% of software engineers use AI tools to debug their code
• 34.37% use them to document the code and whatnot.

Why is there a lower adoption for AI software testing tools?

Well, it’s not “lower adoption”, instead you can call it an imbalance between demand and supply. The stats for AI software testing tools adoption are low because not many mature and effective products have yet made it to the market. The AI software testing stack is still in its early stages. They are continuously evolving. LamdaTest’s Test Analytics & Test Intelligence are one of the first few to the market, leading from the front. Hence, the number of dev teams using AI software testing tools is fewer.

However, there is a huge desirability and demand for AI tools in software testing. A whopping 55.17% of software engineers expressed interest in using AI tools for software testing. The most desirability among all other use cases of AI in software development.

Now, that paints a clear picture. AI presents a huge opportunity for all software testers, aka quality assurance (QA) engineers.

From increasing the productivity of software testers to improving the accuracy of the test cases these professionals write, the overall test coverage, and the reliability of tests (bye-bye flaky tests), the use cases of AI in software testing are ample.

However, the demand-supply gap needs to be bridged. And that’s exactly what TestMu AI’s Test Intelligence tool is for. Test Intelligence will equip all the QA & software testing engineers across the world with the most intelligent testing software powered by advanced AI algorithms. Continue reading to understand what Test Intelligence is, what to expect from it, and how to lead with automated software testing.

Why Test Intelligence?

Testing is integral to SDLC. Traditionally, software testing was done manually, where QA professionals would test software hands-on to identify issues and loopholes. It was an excruciatingly slow process. Hence, manual testing was a big bottleneck crippling the pace of software development. The software engineering needed a way where QA can be done at the pace of development. Maybe, faster.

That led the way to automation testing frameworks like Selenium, Appium, Cypress, etc. To a large extent, these software testing frameworks did solve the pain points of manual testing. But with the increasing complexity of software projects in terms of ever-increasing features and ever-evolving design, the complexity of writing test cases using these frameworks increases too. Especially for projects that are fluid and dynamic i.e., applications that change a lot over a short period, automation testing is not enough to ensure flawless functionality, usability, scalability, and security.

Of course, you can scale the QA team, but then it will be a cost center for you. So, the question is “What’s the solution?” Test Intelligence.

Benefits of AI-led Predictive Software Testing

Here are some of the potential benefits of AI based predictive testing:

• Security: Unearths bugs early in the software development lifecycle, before they become a headache.
• Testing Efficiency: Enables QA teams to walk shoulder-to-shoulder with the development team by unlocking an unprecedented pace of testing software applications on the go. Thanks to auto test case generation, comprehensive test coverage, and test data generation.
• Accelerate Product Delivery: Helps developers stick to the product backlog cadence, and deliver new features on time to the market with faster (real-time) feedback. Hence, enabling startups & enterprises to grab the market opportunity before competitors.
• Improved Product Quality: Quickly eliminates issues in applications that are spoiling the user experience for customers by auto-generating test cases and analyzing the tickets raised.
• Improved Consistency Across Environments: Connects the test reports from different application environments, and identifies any inconsistencies, to fix them before they trouble users.
• Improved Maintenance & Reliability: Reduces test flakiness, and automatically tests the applications for edge cases, boundary cases, and positive & negative scenarios.
• Self-healing Testing Infrastructure: Improves the accuracy of software testing with auto-detection of flaky tests, and accordingly sanitizes the script to overcome the issue.
• Developer Productivity, Resource Utilization, and Costs: Minimizes test case duplication and unnecessary runs, thus saving a lot of time for development teams, and minimizing costs that would have been otherwise wasted on deploying resources for those unnecessary test runs.

Summing it up!

Going forward, to keep innovating at a good pace in the AI age, Test intelligence-powered cloud software testing platforms will help software teams test software applications at lightning speed with auto test cases & test data generation, test execution, test analysis, test case maintenance, and self-healing capabilities. In fact, software testing platforms will need to evolve to automate not just the testing scripts generation & execution, but also automate testing infrastructure provisioning, orchestration, and infrastructure management with zero to minimal human intervention, while optimizing for low cost & high performance. Explore more about TestMu AI’s Test Intelligence.