app development

Types of AI testing strategies for QA (and which you should choose) 

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There is hardly any industry that doesn’t use AI these days. But, of course, the one that’s making the most use out of it is the tech industry. Many IT companies are driving growth and productivity through their use of AI in day-to-day processes. One particular field where tech companies can use AI with a significant effect is testing and QA. 

Most companies today use the method of continuous tests on their pipeline. This method is time-consuming and costly. Using AI can significantly reduce the strain on the QA team by creating tests that are faster and more accurate. AI is a proven solution for many business test cases. But having the right tools is just a part of it. There’s also the need for a good AI strategy for QA, along with knowledgeable QA professionals that help you accomplish your business goal through that strategy. 

What strategies are available for businesses that are thinking of applying AI to drive their tests? 

Strategy 1 – No Automation

In this strategy, the testers test most of the code themselves. They write code that can run on all iterations of the application. It’s the most basic of approaches but it has a few problems. 

For instance, you’ll have to keep writing repetitive code for every new feature you want to introduce. Given that most standard tools have a lot of features added and removed in real-time, the whole process quickly becomes costly and ineffective. 

This technique will take a long time as you‘ll have to check all elements individually for bugs and proper integration. Most companies have stopped using this strategy. According to recent surveys, only 9% of companies don’t use automation in any form. 

Strategy 2 – AI assistance

Here, AI is used only for visual adjustments, like auto checking a few specific known fields in a form. Of course,  AI is only as smart as the baseline that’s set for it. Hence, AI can scan a tool for reference and assist marginally, but can only perform a certain part of a known task, using a baseline. 

Once the AI understands the requirements, it can adjust accordingly. Instead of coding for each element, you only have to write code for more complex parts, leaving AI to take care of the easy ones. For this, you need to configure AI algorithms for comparison to baselines. But the final decision regarding the bug-fix would be yours.

There is another level of this strategy, i.e., if you allow AI to work on the bug report and decision making part. In this level, even though AI is still checking against the baseline, it will assist you in bunching together a list of similar failures and correcting all of them in a single go. This way, you’ll save a lot of time by fixing a particular bug from all modules. 

Strategy 3 – Near Full Automation 

With this strategy, you can configure AI to understand failure and success. AI would have the power to understand whether a change is acceptable or not. Using machine learning algorithms, it can identify whether the given field is a space or an exclamation mark. It can also create self-healing scripts. 

You can also check how much the field changed since the last time you’ve checked it. Once AI understands abnormalities inside the data, it can self summarise and send it for manual checking. One of the biggest advantages of this technique is that you can configure AI to understand whether a required test is successful or not, without taking confirmation from a human, which will save a lot of time. 

One level above this you can find AI running its own test cases. It’ll generate its own scripts to create a near-full automation setup. AI will differentiate between distinct areas and features, just as humans do. It’ll understand patterns and user behavior, as well as system flow. What’s more interesting is that AI would be able to recognize new modules and pages it hasn’t seen before and analyze them.

There can be another level above that, where AI understands individual requirements by actually talking to humans. Of course, this isn’t possible right now, but once this happens, AI will work with the QA team to strategize the test cases and run them on its own. This kind of AI should be much more elegant and sophisticated than the one we are using right now. It’ll still need humans to tell them what to do, just like voice assistants. Humans will have to instruct AI regarding business requirements and functions. The difference is that these tests would be self-generated, self-validated, and save a lot of time and money.


Most companies right now are hovering between strategies 1 and 2. Tech Businesses have yet to figure out how best to adapt AI into their testing processes. There are other points to consider too, such as return on investment (ROI), the sensibility of test cases, maintainability, and severity of the defects. 

Once you understand these strategies, you can make an informed decision. Using these techniques means fewer tasks need to be done by manual intervention. But this doesn’t mean human intervention is not required. QA professionals will have to qualify the test result based on the bug reports. Automation won’t replace humans, but rather create a different and more efficient way of working. It’ll eventually help your business in the long run. 

Anik is an IT professional and Data Science Enthusiast. He loves to spend a lot of time testing and reviewing the latest gadgets and software. He likes all things tech and his passion for smartphones is only matched by his passion for Sci-Fi TV Series.