How AI in Quality Assurance Revolutionizes Testing Architecture

The emergence of artificial intelligence has completely reshaped the software development landscape. But can AI be the future of quality assurance and testing as well?

Updated 26 February 2024

Anand Prakash
Anand Prakash

VP – Pre Sales at Appventurez

Today, almost all businesses out there are paving their paths toward digital transformation. However, it calls for high-quality products with faster time to market, which could be really challenging to achieve in a traditional testing environment.

Due to this, companies are now shifting their focus to AI-powered solutions that make quality assurance seamless and more efficient. Leveraging AI in quality assurance not only complements the agile development approach but also ensures a profound testing coverage that accelerates the app release cycles.

Besides, integrating AI in quality assurance involves several techniques that enhance the process of identifying defect hubs in the code. These AI-enabled methods made businesses switch their focus from just UI-based testing to multi-tier stack automation, enhancing the product’s efficiency and time-to-market.

Methods of Quality Management: The Evolution of AI in Quality Assurance

Quality assurance has always been an important part of the product development life cycle. A professional software development company specifically pays attention to the QA and testing part for seamless performance and immersive user experience.

However, the way it is performed has changed significantly throughout the years. Conventionally, it was entirely a manual process. The quality assurance and testing teams used to review software code using manual methodologies which are indeed labor-intensive and time-consuming.

quality assurance

In the late 1980s, developers started following different software development lifecycle (SDLC) techniques to manage and analyze software quality. The Waterfall SDLC model was one of the most commonly used methodologies used by the developers. Many other development approaches came into play to accelerate the development and testing operations.

Then came the agile and CI/CD approach that supported continuous testing, making the product development cycle fast and efficient. Between the Agile and Waterfall approaches, the former is preferred for its more flexible nature.

However, even after having a plethora of benefits, there are some areas where continuous testing shows issues. They were:

  • The visibility of end-to-end requirements was absent
  • Occurrence of high volumes of tests
  • Siloed automation

To rectify these issues, autonomous QA testing using AI and ML was introduced and adopted across the quality assurance domain.

These power-automated testing trends and solutions take continuous testing to the next level. Besides analyzing the software functionalities, the inclusion of Test Driven Designs (TDD) and Behavior Driven Designs (BDD) in the mainstream offers detailed insights to the QA team. 

This method helps them understand the behavior of application software from the end customer’s point of view. Moreover, AI has also expanded the testing scope from just functionalities to the system’s internal perspective, resulting in improved code quality.

What is the Role of AI in Quality Assurance?

In order to test software and its capabilities, a test suite needs to be created, and test data must be produced. However, this process takes a lot of time. On average, testing software using manual techniques takes over one-third (35%) of the total time consumed within a test cycle.

software testing life cycle

Additionally, manual testing is more likely to involve human mistakes, which can increase costs and take up more time and money, neither of which any business wants to spend.

Moreover, as software gets more complex, the number of tests expands unavoidably, making it more challenging to maintain a test suite and guarantee adequate code coverage.

In such cases, using artificial intelligence in quality control can help accelerate the mobile app testing process and overcome all the aforementioned challenges. The major areas where AI in quality assurance optimizes the testing processes are:

  • Complete monitoring of API testing
  • Automating testing operations 
  • Identifying when and how to run a test script
  • Using AI-based spidering tools for better data analysis

Benefits of Using AI in Quality Assurance: How Does It Make Work Easier for Businesses?

When a product is launched, there are a plethora of things that the product owners need to take care of. Using AI in QA and automation testing processes can relieve IT companies from several mind-boggling tasks and save them time that can be invested in other business operations.

Speeding Up the Testing Process

Developers always look for the best ways to reduce the time taken to build and test software. Leveraging AI testing tools is one such practice. Instead of evaluating a pile of code, developers can use AI tactics to sort through the log files. It not only makes scanning the code easier but also detects errors quicker than before. Besides this, using AI testing frameworks mitigates burnout syndrome, resulting in better and more accurate outcomes.

Moreover, the best thing about AI in quality assurance is that it can adjust to changes in code. With minimal coding, it can easily identify the new functions in the code, adapt to it, and detect the bugs arising due to code changes.

Seamless Test Planning

Planning test scenarios is one of the most time-consuming processes for software developers. And this becomes more challenging when a new version of specific software is released.

With QA automation tools, testers can do the software inspection by crawling through all its screens while creating and implementing test case scenarios. It helps them save time that would be required for planning tests.

Enhanced Predictive Analytics

By leveraging AI in quality assurance testing, you can easily analyze customers’ data to figure out the user behavior and expectations of the software. This data provides valuable insights and helps the developers, designers, and testers to meet the users’ standards, offering quality products and services.

Besides this, incorporating ML algorithms can also help predict the weak spots and provide testing engineers with more details about the functional areas that need testing.

Optimization of Test Cases

One of the best things about using AI in quality assurance is that it enhances the quality of test cases. It assists developers in forming real test cases that are easily operable and regulating.

This is something that traditional methods of testing miss out on, restricting developers from evaluating additional possibilities for the test cases. Since the entire project analysis happens in much less time using AI, developers can easily figure out new approaches to optimize the test cases.

Complete Assurance of Business Operations

Artificial intelligence QA provides a boost to the overall operational efficiency of a business. By profoundly evaluating individual functionalities associated with testing, ensures that the software is capable of fulfilling business requirements and consumer demands.

When all the technical areas of a software application work well, the risks of its failure decrease significantly.

AI in Quality Assurance: Six Stages to Achieve Success

In order to provide customers with the best experience, businesses are working efficiently in the quality assurance area. Due to this, AI quality assurance methods are implemented, making QA testing quick and seamless. However, using AI for quality assurance requires a strategic process, which is divided into six critical stages:

level of ai

Stage 1: Initial Phase with Zero Autonomy

This is the initial stage where code is written and analyzed for better functionality. Whenever you add a new field on the page, a new test is added. In simpler words, when you add any form to a page, a new test evaluates all the fields on that page.

Similarly, adding an entire page will require analyzing all the elements and forms through a brand-new test. The greater the number of tests, the more the chances of you failing as you have to ensure that all the software functionalities are working fine. To overcome this, you need to check all the failed tests to identify the bugs and the root-cause error in the code.

Stage 2: Virtual Assistance for AI

The next stage is more about interaction than action. All the visual elements available in your app must be accessible to the AI testing tools integrated into your system. The reason is that the more AI interacts with the visuals, the greater support it can provide during automation testing.

But why is AI visualization so important?

Well, through AI visualization, the system sees the data that your users will perceive as well. With this accessibility, AI can immediately inform regarding the changes that otherwise would require manual effort to be identified.

However, automation quality assurance experts need AI testing frameworks to differentiate between relevant and irrelevant changes. Similarly, they need specific tools to test whether a specific set of procedures passes or fails while testing the application.

One more thing to keep in mind while enabling AI visualization is that the AI-based systems must be allowed to check all the app’s visual features against a baseline.

Stage 3: Partial Automation – The Beginning of Autonomous Testing

In this stage, you can use AI to test the visual components of the pages in your application software. However, this can be a tedious task.

At this level, your AI model understands the differences in terms that the app users could also comprehend. Thus, it will be able to group the changes from several pages, as it understands them semantically.

This stage mainly describes how AI can tell the testers when the changes made are the same and ask whether they should accept or reject the changes as a group.

Stage 4: Conditional Automation

This stage primarily focuses on ML techniques to analyze the changes made. However, it is not entirely automated, and human intervention becomes essential to identify any errors occurring in the application software. At this level, using AI in quality assurance could only inspect the off-based design of the application, like alignment, font size and color, layout, whitespace used, etc.

Besides this, developers can also leverage AI to check other critical details, such as how many times the changes have been made. In fact, AI-based bug tracking can also let you know whether a change is sorted out or not in a specific test. However, it is only possible when artificial intelligence understands the visual data associated with the application software.

It would be surprising to know that AI checks hundreds of such test results simultaneously and determines how the changes occur in the app code over time. By integrating ML techniques, AI QA automation tools can easily figure out the abnormalities in the code and data and share them with developers to be verified automatically.

Stage 5: High-level Automation

Until this stage, some sort of human intervention was present; be it for writing codes, running tests, or identifying the changes. But with this level, artificial intelligence itself runs the test suites.

From this point of autonomous QA, developers can leverage power automation to understand user engagement at different steps, anticipate the interactions, and analyze the system flow.

AI can also perform tests continuously with the help of different ML techniques, such as reinforcement learning. In addition, it can write test cases for quality assurance AI professionals to ease their tasks.

Stage 6: Complete Automation – The Final Level

This is the final stage where AI will completely dominate the QA infrastructure. At this point, there will be direct communication among QA AI testing teams. Here, the AI-based system will understand the application software more comprehensively to drive the test cases themselves.

AI must become smarter and faster than tech geniuses and business owners to completely automate the testing process. A total understanding of this level is still in progress and it might take some time to explore it further.

ai in quality assurance

Real-Life Applications of Using AI in Quality Assurance and Testing

Several renowned companies across different verticals are availing the benefits of AI automation in quality assurance. Below are some real-life examples where AI for QA testing is utilized.

IBM

IBM Watson, an AI-based platform by IBM, empowers data scientists, analysts, and testers to run and manage AI business models. It also uses AI and its subsets in quality control, ensuring software performance across various industries, such as healthcare, finance, e-commerce, and supply chain.

Google

The leading tech giant utilizes AI for QA testing of software products on staging, along with analyzing code and detecting bugs for seamless performance.

Tesla

Tesla, a renowned automotive company known for manufacturing driverless cars, leverages computer vision and artificial intelligence in its manufacturing processes, detecting flaws in car components during production.

Amazon

By using computer vision systems and AI-powered robotics, Amazon improves quality control in its distribution centers and warehouses. This way, AI in quality assurance makes sure that orders are fulfilled on time and products are of utmost quality.

Samsung

Samsung leveraged AI in quality assurance and employed mechanized quality control systems to navigate issues and enhance product standards.

Most Popular AI Testing Tools for Quality Assurance

The benefits of AI in quality assurance are immense and that’s one of the reasons why around 42.5% of businesses consider test automation as a critical player in QA. But do you have any idea about the most popular AI QA automation tools? Well, the following section will highlight the top-notch automated testing platforms to enhance quality assurance.

Test Sigma

Being one of the most common automated testing tools, Testsigma uses natural language processing technology for writing high-quality autonomous tests. Developers can also use this platform to identify relevant test cases and save them from failure.

Applitools

If you want to generate visual test cases without setting up visual processing configurations, then don’t look further. Applitools can be the perfect solution for you that automatically analyzes which changes could be the bugs and which ones are genuine changes in the app.

SauceLabs

SauceLabs is one of the most powerful tools that leverage machine learning and power automation. It is a popular continuous testing platform offering over 900 combinations for different operating systems and web browsers along with a plethora of real devices as well.

TestCraft

Being an open-source test automation framework, TestCraft is a codeless platform that leverages specific visual modeling techniques for QA testing. One of the best things about using this selenium-based automation tool is that you do not need any specific coding skills or knowledge to use it.

What are the Major Challenges in Quality Assurance?

One of the most common challenges faced by developers in the QA team is receiving requests for last-minute quality assurance checks. Although AI in automated quality assurance is faster than manual testing, the process still needs time.

Since sometimes, the development takes more time than expected, the QA and testing teams do not get enough time to perform their operations. Due to unbelievably tight deadlines, the testing team often performs their activities in a rush, consequently leading to technical and performance-based issues.

Besides this, the other challenges include:

  • Unreliable testing environment
  • Lack of efficient tools, unskilled and inexperienced QA professionals and testers, etc.
  • Insufficient coordination between the development and testing teams

How Can Appventurez Help You in AI Development for QA Testing?

Delivering products and services within deadlines without compromising their quality standards is what every project manager and QA team wants. To ensure this, it is crucial to test them in and out before the delivery, and AI-based techniques can do that job better.

Autonomous QA has already replaced over 50% of the manual testing effort in 46% of instances. Although several businesses are investing in this technology, many of them are still not aware of its efficiency. So, if you are also thinking about streamlining your QA and testing operations, consulting a professional AI app development company like Appventurez could be the best option.

Appventurez’s advanced autonomous testing platform is capable of discovering critical test cases both for functional and non-functional testing. Our QA team plans out-of-the-box solutions and intuitive strategies as per your project requirements to maximize your output while reducing time and effort.

AI development company

FAQs

Q. How can AI be used for QA?

Using AI in QA helps identify the errors in the app code faster and delivers more precise results than manual testing. The automation quality assurance testers using any AI testing framework analyze a portion of data for potential faults and changes in the code and enhance the overall efficiency of the QA process.

Q. What is the role of AI in quality control?

An AI-based bug tracking system can easily analyze the failure patterns and most common defects that impact the quality of products. Moreover, it determines the vulnerabilities in the system and notifies the QA team about the same to overcome them.

Q. What are the most common challenges in quality assurance testing?

The most common challenges in quality assurance testing include unstable environment and setup, unavailability of the right testing tools, hard-to-achieve deadlines, irregularities in the bug reports, etc.

Anand Prakash
Anand Prakash

VP – Pre Sales at Appventurez

Anand specializes in sales and business development as its VP - Sales and Presales. He supervises the pre-sales process by upscaling on establishing client relationships. He skillfully deploys instruments such as cloud computing, automation, data centers, information storage, and analytics to evaluate clients’ business activities.


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