AI in Testing: Friend or Foe? Debunking the Myths

Do We Need AI?

AI in Testing? Imagine a tireless teammate who never sleeps, can work 24*7*365, analyze humongous historical and current data, identify repetitive tasks, and even predict potential defects before they occur. 

Now, you might wonder anxiously, “So will AI replace me?” 

Let’s take a new banking application as an example. AI can tirelessly try every login credential combination, check every edge case, and ensure security. But it can’t tell if the user interface is confusing or overwhelming. And that’s where human expertise is still needed.

AI and human intelligence operate differently. AI can only mimic human intelligence. And this intelligence isn’t driven by emotions, human intuition, and creativity, but rather by the ability to learn, reason, and solve problems.

“So, AI is like your super-powered assistant!”

Exactly! With AI, you can “Code at speed. Test at speed.”

Why AI Adoption Matters?

In a C3.ai webinar, Enterprise AI: Separating Reality from Hype, Mike Gualtieri of Forrester Research stated that 73% of companies surveyed reported a positive impact from AI adoption. He predicts that by 2025,nearly all organizations will have deployed at least one AI-related use case.

In Accenture’s Inside The Heart Of Change episode, it’s mentioned that 3 out of 4 C-suite executives believe if they don’t scale AI effectively within the next 5 years, they risk running out of business completely

Without AI, there is a major human and time investment involved. Let us look at some of the drawbacks:

Debunking The Myths

The problem with these misconceptions is that they can deter teams from adopting AI processes, limiting their ability to grow and stay competitive. 

Let’s delve into the common myths surrounding AI in Testing. 

Myth 1: Automate ALL Test Cases to Cover Every Possible Scenario 

Reality: Not all test cases need automation.

Identify the right testing type: Certain test cases are complex and require a significant amount of effort to automate. It is difficult to create and maintain such complicated test cases. They are better suited for Manual testing or crowdsource testing. 

Run only the right tests: Do you need to run all test cases whenever there is a change in the application? No! AI helps you to identify the test cases that are modified and run only those cases! 

Beyond Automation: Automation execution is not always the right answer for executing a test case. AI systems can help to determine the area to test along with the method to test.

Further Reading: Factors That Influence Software Automation

Myth 2: Application is too Small and Local to Pose a Security and Data Privacy Threat 

Reality: No application or company is too small to be targeted by cybercriminals.

Security testing: Today’s devices are IoT-connected. Applications are now available across every device, every browser, all the time to provide a frictionless experience! Hackers can target any possible weakness they can target, regardless of the application size or location. 

By using AI in Security Testing, Exploratory Testing, and Performance Testing, you can safeguard your application by making the system hackproof and sustainable.  

Fraud Detection: AI can help teams build greater levels of cyber resilience that can stand up in the face of immensely powerful vulnerabilities. It can also analyze user behavior and identify patterns to prevent fraudulent activity and enhance fraud detection.

Personalized Security Solutions: Based on the application’s functionalities and user behaviors, AI can be used to customize security measures.

Myth 3: AI will Replace Human Testers. 

Reality: AI is more likely to augment human testers’ capabilities rather than replace them.

Human intuition, creativity, and expertise still matter:AI thrives only on patterns and data. AI Testing systems must be set up and require data modeling, training, and continuous monitoring by human testers. 

Collaboration: The future of test automation lies in a balanced collaboration between AI-driven technologies and human expertise. While AI testing excels in functional, regression, and performance testing due to its ability to handle repetitive tasks and large datasets, human expertise is needed for exploratory testing and usability testing.

Contextual and domain-specific knowledge: AI has numerous benefits but it doesn’t come without limitations. For example, we know that AI learns from data patterns. If a particular defect hasn’t been encountered before, AI might not be able to identify it. Identifying such edge cases or any domain-specific errors still requires a great deal of human thinking and ingenuity, 

Myth 4: AI is Error-Free

Reality: AI is still learning and can give false failures

False Failures: Whenever an automation test suite is executed, the result is a pass or fail.  False failures can range from 0% to 100% of the Fails that are seen in an automation execution result. Investigating and fixing false failures takes time away from real issues. 

AI Defect Prediction: Tools like Webomates’ AI Defect Predictor help to identify True Failures vs False Failures and also create a defect report using an AI engine for True Failures. 

Rectifying AI: Human testers are still needed to detect true fails from false fails and re-execute testing for all false fails. At times, it’s difficult to fix an automaton script within a short timeframe. In such cases, human testers are crucial as they can use manual or crowdsource testing.

Myth 5: AI in Testing Requires a Complete Overhaul of The Existing Testing Practices.

Reality: AI can be integrated progressively into existing testing workflows. 

Seamless adoption: AI can seamlessly integrate with your current testing practices. For example: While most trained QA testers can execute about 50 test cases per day, AI Testing services like Webomates can execute an infinite number of test cases in 15 minutes, without needing to overhaul any testing teams and practices. 

Scalability: Whether you are an early-stage startup or a big enterprise, your teams can access AI Testing services and get started within minutes. Example: Webo.Ai is designed exclusively for startups looking to accelerate their speed of product releases.

The Verdict 

AI isn’t here to replace human testers; it’s here to empower them. 

Humans excel at innovation, creativity, domain knowledge, and the ability to handle edge cases, while AI excels at automating repetitive tasks, analyzing vast amounts of data, and identifying patterns.

The future of testing lies in collaboration between AI and humans. Based on your specific project requirements, your team/leadership needs a realistic and accurate understanding of what AI can and can’t do for you. Accordingly, you can develop a balanced testing strategy that leverages the strengths of both AI and human expertise.

Ready to Experience The Power of AI Testing?

Accelerate your Testing with Webomates. 

Webomates is at the forefront of Intelligent Automation with its Testing-as-a-service patented AI testing platform. We understand the dynamics required to accelerate product development and testing. 

Take the next step towards a more efficient and reliable testing process. 

• Explore our intelligent test automation solutions that help you scale with agility.
• Reduce error-prone manual testing with patented AI Defect Predictor tool
• No Code. No Script. Low Touch. AI automate your application in just 3 steps.
• Power your strategic agility with Shift Left Testing
• Implement  CI/CD/CT to accelerate code release. 

Take a closer look at our success stories and find out what Webomates Intelligent Testing services can do for your business. 

Please click here and schedule a demo, or reach out to us at info@webomates.com

Tags: Ai Debunking Myths, AI Friend or Foe, AI in Testing, Ai Myth, AI Testing, Software Testing