Manual testing will never die! Here’s why.

The recent developments in AI have resulted in QA professionals and stakeholders doubting the future of manual testing. But you can be assured of this testing methodology – it is here to stay.

In this blog, we will expand on just exactly how and why manual testing will survive the AI onslaught.

But before we bring you the meat of this article, let us first dive into understanding manual and automation testing.

Manual Testing vs Automation Testing: What’s the Difference? 

Manual testing is a testing methodology without using any tools i.e., you manually test every component of a system.

On the contrary, Automation testing involves writing automation scripts to perform testing. It involves the use of tools to do the testing for you.

AreaManual TestingAutomation Testing
EfficiencyCan be less efficient as it requires human involvement.Can be more efficient as it does not require human involvement.
CostInitial investment for manual testing is low but ROI is lower.Automation Testing has high ROI but can be costly initially.
TimeManual testing can be a bit more time consuming as the testing involves following step-by-step instructions.Automation Testing is relatively less time consuming and involves the use of tools that does the testing for you.
FrameworksManual testing involves the use of guidelines, checklists, and processes instead of frameworks and tools.Automation testing has different tools and frameworks such as cypress, selenium, etc.
Ideal SituationManual testing is great for testing business critical scenarios, exploratory testing, and more.Automation testing is best for performing repeated testing and tasks that are too mundane for manual tests.

As you can probably guess, Automation testing is where AI can be used but don’t let this fool you into thinking that people don’t use AI for manual testing.

In fact, you can use AI to generate test cases for manual testing. We don’t recommend doing this though as test cases are system specific and the output from Large Language Models are mostly generic.

Let’s see the use of AI in Automation and Manual Testing.

Area of UseAutomation TestingManual Testing
Writing Test CasesCan use code generation tools to write code for test cases.Can use large language models (LLMs)to generate test cases.
Test ExecutionCan use AI generated automation scripts to perform regression testing.Need to execute tests manually. But AI bots can be used to perform manual tests.
Reporting and DocumentationCan use LLMs to report bugs and document findings.Can use LLMs to report bugs and document findings.

AI’s Role in Testing

AI is powerful, no doubt about it.  But it is not as smart as it may seem at first glance. Sure, AI can write you 100 test cases in two minutes, but you need to ask yourself, “how many of these are quality test cases that I can actually use?”

5? 10?  Let’s be generous, 20?

See the thing with AI is that it is only as good as the human using it.  You may get 100 test cases in 20 minutes but close to 90 of them will be irrelevant.

So, unless you are well-versed in quality assurance, your hope of streamlining AI for QA successfully is highly unlikely.

What about automation tools like Cypress and Selenium, you ask?

Well, they are great. Every company needs automation engineers who can use these tools to enhance the accuracy of testing and optimize time and resources.  But these tools are not entirely AI. An engineer still has to write the script manually.

Another area where AI can help QA is when it’s time to execute test cases. AI can analyze the code, boost coverage, and swiftly detect bugs.

In addition to this, AI can help simulate user interactions to identify performance issues proactively.

But these advantages are clouded by how error-prone AI is and how under unfortunate circumstances, AI can do more harm than good.

Why Manual Testing Will Survive?

Manual testing will survive because Artificial Intelligence can never replace Natural Stupidity! 

The human mind is complex – it’s the result of millions of years of evolution. Our neurons help us recognize new patterns and process information in distinct ways.

While AI and automation are valuable when testing repetitive tasks where you can stick to a template, it cannot handle scenarios which are new.

And in Quality Assurance, such novel scenarios can arise every day. During such situations, it is highly unlikely that an AI could help you.

The one caveat with AI is that it can help us solve problems that have a solution template but when you throw something new at it, AI spews out nonsense.

Also, an AI cannot understand the project like a human does. It cannot process context unless explicitly stated. Even then, it fails to understand the nuances of a project.

In addition to this, AI requires large amounts of quality data to even be able to solve routine problems, which makes it unqualified for most systems. It only understands the prompts.

Finally, the cost of AI far exceeds the benefits it provides – You need to invest in tools and large language models but since the return isn’t guaranteed, businesses will surely hesitate to invest.

Do’s and Don’ts of AI in QA

AI is not entirely the sole culprit, there are certain ways to get value out of this technology, there are also a few things you should be avoiding. Let’s discuss them.

Do’s

  • Use Automation and AI to streamline regression testing.  
  • Use AI to brute force a system and identify performance bottlenecks.  
  • Use large language models to fix grammar in your documents, ensure document readability and help you write better documents.

Don’ts

  • Write entire test cases for testing.  
  • Automate every aspect of a software product without any manual input. 
  • Give it full freedom to write important reports and documentation. 
  • Use AI as a crutch more than a tool. 

Final Words

The use of AI is bound to happen in QA but the honeymoon period that the world is with this technology right now could end soon.

When this happens, there will be hesitation in using AI. It is true that certain aspects can be streamlined with AI, and it does give you a decent result but there should be proper human input.

We should be mindful while using this technology for work. And to be brutally honest, AI can be very helpful when it is used to automate a few mundane aspects of a QA job but please don’t let it be the one setting the tone for your QA projects.

And to conclude, Manual testing will never be on the back burner as it is the foundation for shipping a quality system.

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