AI in Quality Assurance: The Next Stage of Automation Disruption
If we sit to analyze the mobile app development journey ever since agile came into picture, we will find that the ways of how apps are delivered have completely changed.
Before the introduction of agile approach in mobile apps, there used to be a monthly or sometimes bi-monthly release. Ever since the approach has come into the picture, app releases have taken a weekly, bi-weekly approach.
To keep up with these frequent build releases, Continuous Testing was brought into existence and automation suits were built for sanity and regression testing. This new testing approach supported fast deliveries and even faster-paced testing cycles.
Now, with the world moving in the direction of Digital transformation, the need to anticipate market requirements in advance and developing a system that is scalable and predictive enough for catering to the future trends is at its climax. Moving beyond the continuous testing approach is now inevitable.
In the present situation, testing needs assistance for accelerating delivery. AI in Quality Assurance can help us reach there.
In this article, we are going to look into the detailing of the role of AI in quality control. We will look into the different facets of AI in Quality Assurance: role of AI in quality management, the benefits of AI quality control, the popular AI Quality Assurance tools, and the challenges associated with the integration of AI in mobile app testing.
Evolution of Quality Management Methods
The Quality Assurance strategies have changed. The ones that existed back in the 1980s have crossed several strides to keep up with the changing requirements of the software development and delivery approaches and cycle.
The present case, popularized as continuous testing, is dominated by the Agile and CI/CD approach. Even after being denoted as one of the most developed stages of software QA testing, continuous testing brings some key challenges in Quality Assurance processes:
- Siloed automation
- An absence of end to end requirements visibility
- The high volume of test
To solve these issues, the industry must move to autonomous testing, which ensures a zero-touch QA.
Role of AI in Quality Assurance: Test Cases for Autonomous QA
Incorporation of AI is what updates the QA process to its autonomous software testing stage.
Mobile app testing consists of a number of different tasks such as scripted automated testing, manual testing, and non-functional testing. The impact of AI in software testing can be seen adding value in the present testing efforts by enabling auto-exploring apps on actual devices for making sure that all of the functionalities and user flows work as they are supposed to.
The use of artificial intelligence in testing can also help in identifying new bugs or defects which are introduced during the app exploration phase. The QA teams can make use of AI testing tools for supplementing normal testing efforts, while getting the best test coverage in expedited time and great accuracy.
The role of AI in Quality Assurance and testing will also be seen in the changing of testing tools, the tests will be enhanced with AI-powered visual verifications, which will give out a range of different outcomes.
If we explicitly talk about the test cases of AI in Quality Assurance, here are the ways testers are using AI presently –
- Implementation of AI through image-based testing
- Determining whether to run a test script
- Using AI spidering
- Monitoring API testing
- Automating tasks
With the active participation of AI and machine learning in Quality Assurance, the time to come will be extremely experimentation driven for the QA specialists.
Benefits of Using AI Technology in Testing & Quality Control
There are many ways in which our team of developers speed up the app development process. Incorporating disruptions in the testing process is one of them. In place of going through thousands of lines of codes, AI will be able to sort through the log files, scan the codes, and detect errors within seconds. Additionally, AI lacks the burnout syndrome and thus yields better and more accurate results.
Also, AI can evolve with the code changes. It can adapt and identify new functions and can be programmed to decide if something is a new feature or a bug arising out of code change.
Well-Researched Build Release
By using Artificial Intelligence in Quality Assurance, it becomes possible to examine similar apps and determine what contributed to their success in the market. Upon understanding the market requirements, new test cases can be made for ensuring that the app doesn’t break when it comes to achieving specific goals.
Effortless Test Planning
Presently, a good amount of QA expert’s time goes into planning of test case scenarios which would get them app launch confidence. The same process as to be applied every time a new version is released in the market.
AI test automation tools can help testers analyze the app by crawling through every screen and generating, executing test case scenarios for them, thus saving on the planning time.
Expanded Role of a Tester
With AI entering the picture, the teams of QA engineers will find themselves learning new skills. They will need to up their skills in neuro-linguistic programming, business intelligence, math optimization, and algorithmic analysis.
Job title wise, we can foresee these names coming into prevalence –
- AI QA Strategists
- AI Test Experts
- Data Scientists
The Six Levels of AI Testing
At this stage, writing the code is a repetitive process. Adding a field to page would mean adding to the test. means adding a test. Adding any form to a page means adding a test that checks all the fields. Adding a page means looking at all the components and forms through a whole new test.
The more the tests, the greater are the instances of you failing to ensure the app’s functionalities as a whole. To solve this, you check all the failed tests to know whether something is a bug or a fresh baseline.
At this level, the better an AI sees your application, the more autonomous your QA will become. AI should not just look at the page’s Document Object Model but also its visual picture. Once the testing framework sees the page holistically, it will help you write checks which you otherwise had to write manually.
For this testing level, you would need AI algorithms which can determine the changes which are not actually changes and which are.
Today’s AI technology can help you with writing the test code by writing the checks. Also, they can check whether a test passes. And if it fails, it should notify you so that you are able to check if the failure is real or happened because of a software change.
Through Level One, the QA specialist would avoid the time-taking aspect of writing checks, while you can also use the AI for testing the visual elements of the page. But what follows – checking every test failure – is a tedious task.
At this level, your AI understands the differences in terms which the app users would also be able to understand. Thus, it will be able to group the changes from a number of pages, as it understands them semantically.
At Level 2, AI can tell the tester when the changes made are the same and ask whether it should accept or reject the changes as a group.
In the previous level, human intervention is still needed for vetting any change or failure detected in the app. At Level 3, AI does the job.
For example, by applying the machine learning techniques, AI can examine visual elements of the app and decide if the UI is off, on the basis of the standard rule of design.
The AI at this level can evaluate pages minus human intervention, simply by understanding the data and design rules. It would look at hundreds of results and analyze how things change over time. Then, with the help of machine learning, it’d be able to identify differences in changes.
Till now, humans have still been driving the tests. Level 4 is where AI would take over.
Since Level 4 AI is able to examine an app semantically and understand it like a human would, it can drive the tests. This AI will be able to see the user interactions over time and visualize the interaction, understand the page and user flow.
Once AI understands the page type, it will use reinforcement learning techniques to start driving tests, automatically.
This part is out of a science novel right now. At this stage, AI will be able to communicate with the product manager, understand the app, and drive the tests – all by itself.
While presently, AI is still on Level 1, there are some automation events that are already using Artificial Intelligence: Visual UI testing, API testing, Automated testing, and Spidering.
The Popular AI Testing Framework and Platforms
Although slow but Quality Assurance has been making entrance and prevalence in the third wave of automotion with the help of AI-driven quality assurance platforms.
Here are some of the top AI test automation tools operative in the market today
1. Eggplant AI.
It makes use of intelligent algorithms for navigating software, predicting the many defects, and solving the challenges through the help of advanced data correlation. It also enables automating of test automation engines and provides graphical analysis of test coverage and outcomes.
The platform provides software’s deep analysis through machine learning and delivers “app blueprints” models which apply cognitive generation. These blueprints hold the capability to develop multiple test cases in only a few minutes. Appvance also comes with a Test Designer functionality that can be combined with screenshot comparison, data driven tests, and AJAX or DOM auto capturing.
It makes use of machine learning for authoring, execution, and maintenance of the automated tests. It focuses on functional end-to-end and user interface testing. The platform continuously becomes smarter and the stability of its test suites increases with more runs.
It is one of the most commonly used AI-driven tools for continuous automated testing. The platform makes use of natural language testing processing for writing quality auto tests. It also identifies relevant test cases for test run and saves sudden test failures.
Challenges In Quality Assurance Processes and AI Combination
Although the future of AI and quality management belongs together there are many obstacles that it will have to cross for being 100% adopted in software testing. Here are some of those challenges –
Data management is not a mainstream process
Processing terabytes of unstructured data requires a lot of manpower and financial support. Today, there are only a handful of businesses which have the ability to handle heavy data, let alone analyze it and prepare it for machine learning.
Lack of AI expertise
Artificial Intelligence, as a skill, is still at a nascent stage. This makes finding the expertise who can take control of an intricate process like app testing is extremely difficult. And even if you manage to find the resource, it would cost a lot for a startup level business to handle.
The solution to this can be to outsource the job to a QA expert offshore.
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