AI In Quality Assurance – This Is How AI Is Revolutionising Quality Assurance (QA)

Quality Assurance (QA) can be something very complicated and take up a lot of company time no matter the industry.

However, QA is something that you simply cannot avoid because if you fail to organise QA sessions then it can jeopardize the integrity of your operations and output.

Only if there was a solution that could learn from its mistakes and be trained to do QA tasks exactly as you want and even be utilised to automate Quality Assurance (QA).

That sounds very familiar and exactly like Artificial Intelligence (AI) because that is exactly what we are talking about.

Artificial Intelligence (AI) is the perfect solution to completely automate the Quality Assurance (QA) testing process for any company.

AI and QA are a match made in heaven when it comes to software quality testing and analysis which allows organisations to focus on efficiency and the proper management of resources.

In this blog, we are going to look at how Artificial Intelligence (AI) can be and is being utilised to revolutionise and automate Quality Assurance (QA) testing in software deployment.

Let us start by looking at the evolution of Quality Assurance (QA).

Evolution Of Quality Assurance (QA) Tests

Early Stages – Manual Era

The first stage of the evolution of Quality Assurance (QA) is the stage when everything happened manually and testing was completely manual.

Test scenarios were executed manually one step at a time by programmers and bugs were also discovered manually and dealt with one bug at a time.

You do not see any automation or tools in this early stage of software Quality Assurance (QA) testing evolution and the main challenge of the stage is that it is very time-consuming and there is no guarantee of quick and easy or even practical solutions.

Scripted Automation

The second stage of Quality Assurance (QA) was quite an improvement from the manual stage because this is a stage where programmers could run scripts and expect some degree of automation.

If we compare efficiency to what we have today then it is still going to look like a lot but compare that to the manual stage of QA testing and it is light years ahead.

This helped companies understand the need for automation and the need for data reference solutions that could allow greater test coverage and also reduce the need for human intervention.

Continuous Integration/Continuous Delivery (CI/CD)

It is now the early 2000s and we see the emergence and popularity of the agile methodology which brings continuous testing into the mainstream where we see tools being utilised in the CI/CD pipelines.

This was a time when automation was slowly but surely taking on the role of being a necessity in the software development industry because nobody had the time to manually do things and test automation was going into the areas of performance and security.

However, we are still not in the area of AI and it will still take some time for it to catch on now. ?However, the one thing that changed in this stage was that Quality Assurance (QA) teams were becoming evolving into their own department.

Generative AI

Finally, we come to the generation of Generative AI which has the potential to completely transform Quality Assurance (QA) into a highly automated process that requires little to no human intervention.

While this stage of the evolution of Quality Assurance (QA) is still in its early stages but we expect great things from the introduction of Artificial Intelligence (AI) to Quality Assurance (QA).

Technologies Used in Quality Assurance (QA)

Computer Vision

The first technology we are going to talk about that is currently being used in Quality Assurance (QA) is definitely going to be computer vision because it allows for automated visual testing and also the utilisation of OCR (Optical Character Recognition) testing.

Computer vision makes it possible to compare multiple images which can mean QA engineers then utilise a control image and the test image for very accurate results and testing every time.

Something like this can be utilised for testing Graphical User Interfaces (GUIs).

Machine Learning (ML)

Automated Quality Assurance (QA) would be impossible without talking about the utilisation of Machine Learning (ML) because it allows ML models to utilise predictive analysis in order to detect problems and prioritise areas.

Machine Learning (ML) is excellent when it comes to anomaly detection because it is able to analyse test results from the past and identify unexpected behaviour very well and also improve the performance of test accuracy each time.

The introduction of Machine Learning (ML) into Quality Assurance (QA) means you can now completely automate tests that learns from its mistakes and improves its accuracy and we can’t forget about the fact of how versatile this is.

Natural Language Processing (NLP)

Natural Language Processing (NLP) bridges the gap between humans and the software meaning the QA engineers can dial down in on the test requirements even better and the NLP can understand that with the help of things like chatbots.

The introduction of NLP means that Quality Assurance (QA) will open up to a much larger subset of people making QA testing much more accessible.

NLP is also excellent when it comes to sentiment analysis and this is excellent when it comes to focusing on different areas of prioritization in QA testing.

Advantages of AI Utilisation in QA Testing

Predictive Analysis

One of the advantages of utilising Artificial Intelligence (AI) for Quality Assurance (QA) is that it can be excellent with predictive analysis and especially when it comes to understanding and examining customer data.

This is the kind of testing that will help companies improve their products after being launched.

That is why if you want to utilise changes after analysing user behaviour then this is the way to go.

Faster SDLC

Artificial Intelligence (AI) just speeds up the process of Quality Assurance (QA) by making software testing quite fast and easy and this saves time for the deployment of the entire software.

This means you can expect a faster Software Development Life Cycle (SLDC) just with the inclusion of AI because testing is also part of the SLDC.

This is only possible because it is better than manual testing and the best part about this is that the model will only improve its accuracy with each test.

CI/CD Enhancement

Continuous Integration and Delivery (CI/CD) is just something we take for granted now simply because of how practical it is when we have to do repetitive code integration and testing as well as deployment.

Artificial Intelligence (AI) can automate that process making it fit perfectly with Continuous Integration and Delivery (CI/CD) pipelines and thereby improving the software development speed.

This is very helpful from a business perspective because CI/CD is the way to go.

Resource Optimisation

No company has unlimited resources whether it is in the form of computational power or in the form of software engineers that they can specifically allocate to redundant tasks.

If you are looking for the ultimate form of resource management in the form of an employee who does not get fatigued or does not make mistakes in spite of having to do the same thing the whole day then Artificial Intelligence (AI) is the answer.

Quality Assurance (QA) can be difficult if you are running on limited resources but Artificial Intelligence (AI) makes resource allocation and optimisation possible with extreme levels of automation.

Better Defect Tracing

There might be some issues in the software that are not apparent right now and are not causing issues right now but might pop their ugly head when you least expect it.

This might cause issues in the future even after the app has been officially launched and this can result in a very bad experience for your clients.

Artificial Intelligence (AI) comes with predictive analysis and better future defect tracing that flags issues in the present so that they do not become too big in the future.

Better Bug Detection

A large portion of Quality Assurance (QA) is bug detection and it is about the analysis of all the code you have in place and large volumes of data.

This is not possible manually or even through conventional methods of testing but it is possible through AI because AI has the capability of analysing large volumes of data.

It can then utilise its identification methods in order to find bugs that might not even be visible during regular use but only during simulated stress scenarios.

UI Testing Improvement

The User Interface (UI) of any app or software can be written with a lot of flaws or you might receive a lot of feedback regarding problems with the UI before the final launch.

If you want to detect these issues that might come to the surface only during adverse situations like high traffic scenarios and other scenarios then AI is the answer.

Artificial Intelligence (AI) will let you test the app's performance based on multiple simulated situations so that you and never unprepared for whatever comes in the future.

Test Planning Accuracy

Testing something needs its own set of planning and that can also be very time-consuming not to mention the fact that the time you are planning could be spent on analysis and testing.

The amazing thing about AI is that it can create custom plans for each individual test case so that you get to test out applications without spending a lot of resources and time on test planning.

This might seem insignificant but do that for the whole year and you can save a lot of time and resources.

These are all the levels of AI Quality Assurance (QA)nbsp;Testing

Level Zero

At this stage we only see the very basic and traditional manual testing approaches being applied to AI systems.

This means that this is the stage where you manually validate outputs and check results and this is quite hard work.

What’s challenging about this stage is that if the testers are not careful then they can jeopardise the entire process of testing because a lot of manual involvement is required.

Level One

This is a little bit more advanced stage of utilising AI for QA testing as this where we see testing tools being integrated which helps in introducing automation to the tests in the form of regression testing as well as validation of outputs.

This is a stage where we see scripting tools being used and custom frameworks being applied so that that testing is a lot more automated and this is where we start to see the benefits of AI in QA.

Level Two

This is when things get a little bit more serious because this is where we find AI technology incorporated to identify areas of risk that might be a problem in the future.

During level two of the process, we see Machine Learning (ML) models being utilised to test data and defects and we see a lot of AI tools being utilised.

Level Three

Quality Assurance (QA) testing will only be as accurate as the AI models and level three is all about testing the very AI models themselves.

These specially created AI models are attested in multiple kinds of environments in order to find out if they are actually able to notice bugs and problems.

While this stage of AI training is not really common with all companies but if you would like to utilise AI at the deepest level possible then you should definitely reach this level.

Level Four

At level four we see AI being integrated into continuous integration/continuous delivery (CI/CD) pipelines for continuous testing and development.

This is a stage where automated AI testing frameworks are utilised and this continuous testing makes sure that the software is always validated for problems.

Level Five

If you look at the levels of AI in QA testing from levels one to four then we are going to see that these AI tests need to be run by someone manually or semi-autonomously.

However, when we are at level five of AI testing, this is where the AI itself is responsible for conducting the tests and it is also utilised as the subject of the tests.

This is the stage where we require little or no human intervention and the test can be very much autonomously controlled.

We hope this blog has helped you understand the importance of Artificial Intelligence (AI) in automatic Quality Assurance (QA) testing.

If you are someone who is interested in AI integration and would like to include AI in the QR process of their Software Development Life Cycle (SLDC) then we are here for you.

We are Think To Share IT Solutions and we are one of the premier names when it comes to AI integration and implementation and we are renowned for our custom AI solutions for every need possible which also includes QA testing.

We utilise AI ourselves when it comes to the QA testing of our proprietary products and we would love to extend that knowledge to you.

We welcome you to visit our website and check out everything we do.