Are you thinking about making the switch from manual to automated or AI QA testing?
You’re in good company.
The increasing reliance on AI is driving more companies towards different forms of automation.
In 2022, the automated testing market was worth $20 billion, and Global Market Insights predicts a 15 % CAGR from 2023 to 2032.
However, AI is not the only driving force for automatized testing. Users are calling for faster software deliveries, urging companies to cut down on time to market while maintaining consistently high quality – and that’s hard to do with manual testing.
In this guide, you’ll learn all the pros and cons of manual, automated, and AI QA testing, along with the upcoming testing trends and how AI-powered tools can give your business a vast competitive advantage.
Let’s start with the basics and advance as we go.
What is QA testing?
Quality assurance testing (QA testing) is the process of ensuring that software meets required standards before it is released to end-users. The tests are usually conducted by software developers, also known as QA testers, using various tools and techniques to identify any issues that could affect the software quality.
There’s a wide range of QA testing tools and techniques available, and they’re usually divided into three categories: manual, automated, and AI testing.
1. Manual QA testing
Manual QA testing is hands-on testing without the use of automation. It can involve testers acting like end-users, interacting with the software’s features manually to search for potential defects.
The pros of manual testing are that it allows us to see things from a human perspective. Letting testers explore as they go may lead to unexpected, subjective discoveries that maybe wouldn’t have been found through automated testing.
However, a big con with manual testing is that it’s time-consuming and difficult to scale in comparison to, for example, AI autonomous testing, which allows for much bigger test coverage. We also have to factor in the risk of human error that’s inevitable in manual testing.
2. Automated QA testing
Automated QA testing uses automated testing tools, scripts, and frameworks to verify software. The automation simulates user behavior – like navigating menus, entering data, and clicking buttons – repeatedly and compares the outcome with expected results to capture potential defects.
According to a 2022-23 report by Kobiton, a whopping 97% of companies are using automated testing. The top three factors for adopting automated testing were improving user experience (40%), improving app functionality (34%), and speeding up time to market (13%).
In other words; automated QA testing is something most of us are doing already, and it’s not a question of if businesses will expand further into it, but rather about when.
The pros of automated testing are obviously efficiency, being able to conduct tests quicker, on an effortlessly consistent basis. However, a con is that you lose the human judgment and intuition that, despite the risk of human errors, can be essential, especially when testing user experience.
A form of automated testing that’s been on the rise lately is low to no code testing, let’s touch on that briefly.
Low to no code QA testing
There’s a growing trend to democratize software development with low to no code-testing tools. The goal is to involve more team members in the testing process, regardless of technical background, to achieve higher transparency, efficiency, and quality.
Low to no code testing tools use visual and user-friendly interfaces, allowing testers to execute test cases without relying on programming and scripts.
A big pro with this approach is that it opens up testing for non-coders, allowing companies to allocate resources more efficiently. As much as 43 % of software developers work in testing. If non-coders were responsible for testing, software developers could devote more time to improving the software.
However, human testers are still needed to set up and maintain the tests, so the efficiency wins aren’t as big as with, for example, an AI autonomous approach (more on that further down).
3. AI QA testing
For the past years, we’ve witnessed a steady stream of new QA testing tools powered by artificial intelligence (AI) and machine learning (ML) make their way onto the market.
There are two forms of QA testing that leverage AI – AI autonomous and AI-assisted testing. Even though they might sound similar, they use very different techniques, so it’s important not to get them mixed up.
AI autonomous testing: AI + human-like interactions
AI autonomous, also known as AI-first, testing involves using AI and ML to automate software testing, eliminating the need for human intervention.
This is what you would call hands-off testing, with AI calling the shots. AI autonomous testing tools will simulate user behavior and execute test cases across various devices and platforms based on what the algorithm sees fit.
QA.tech falls into this category of testing tools, using AI to discover, plan, execute, and evaluate tests, as well as fix potential issues.
Traditional automated testing relies on predefined scripts, while AI autonomous testing leverages machine learning algorithms, enabling human-like interactions.
A big pro of AI autonomous testing is that the algorithm has great self-learning capabilities and will adjust its strategies based on the information it picks up from previous testing.
AI autonomous testing will increase testing coverage, efficiency, and accuracy on a level that can never be achieved through manual or traditional automated testing.
It’s like combining the skills of a QA engineer with the speed of automated testing. Helping businesses shorten time to market, cut costs, and enhance user experience.
Next up, we have AI-assisted testing, which involves using a combination of AI and human testers. Unlike AI autonomous testing, where AI handles the testing entirely, without any human intervention.
AI-assisted QA testing: AI + human testers
With AI-assisted testing, humans are still very much involved in planning and executing tests, as well as analyzing results, taking actions based on them, and fixing issues. AI tools are used to improve efficiency, test coverage, and accuracy, but not to the point that they totally replace human involvement.
Human testers are still needed, which can slow down the testing process and limit scalability. It also involves a risk of human errors, which is completely eliminated with AI autonomous testing.
AI-assisted testing can be a good option to get started if you’re looking to automate QA testing, but it still demands human involvement, which limits opportunities to scale, free up resources, and reduce time to market.
The impact of AI on quality assurance going forward
The ratio between manual, automated, and AI QA testing will vary depending on your business needs. As with most things, it’s all about finding that healthy individual balance.
Will we see a continuous surge in AI-driven QA testing going forward? Definitely.
The move towards more automated software testing is inevitable, along with the growing reliance on AI. More companies are looking for ways to integrate AI into their testing process to achieve faster releases without sacrificing quality.
That means that companies that don’t opt for AI quality assurance will quickly fall behind. It’s not a question of if, but when you should start automating and integrating AI into your software testing.
Think of it this way: what if your developers could focus on creating software users love instead of spending time on testing?
Our AI autonomous testing tool will do the testing for you. Combining the skills of a QA engineer with the speed of automated testing.
Don’t let your competitors beat you to it – join the waitlist.
