A Systematic Review on AI-Driven QA Automation: The Next Normal for Continuous Testing
Learn about the relevance, necessity, and aftermath for the world when bridging the benefits of continuous testing with the potential enrichments of AI-driven automation.
Join the DZone community and get the full member experience.Join For Free
When the historians of the future get around the commerce of our times, they are sure to garner overwhelming evidence around the appeal for artificial intelligence and machine learning. The way these two technologies have dug their heels into every possible sector in today’s global market is a revolution in itself. For IT and digitalization industries, in particular, the phrases “DevOps” and “continuous testing” are now finding themselves inseparable from the conversations around AI- and ML-based automation strategies. If anything, it already seems a bit late to expand that conversation to accommodate QA automation.
As new technology is introduced, companies, though reasonably skeptical, are beginning to explore and adapt them against the unique demands of consumers. By implementing safe and efficient automated programs for quality assurance, they are already ensuring minimal downtime and uncompromised service experience for their customers. Continuous testing has become synonymous with the QA strategies in most of the DevOps pipelines. Therefore, it only makes sense to bridge the benefits of continuous testing with the potential enrichments of AI-driven automation. Through the course of this article, we will discuss the various aspects of this bridge, including its relevance, necessity, and aftermath for the world that is already going through its fourth industrial revolution.
1.1 What Is Continuous Testing?
Unlike usual automation testing, continuous testing is the process of consistent automated tests throughout the software delivery process aimed at obtaining rapid feedback against the associated business risks. At its very essence, it is an extension to test automation that best fits the complex need regarding application development and delivery speed. Continuous testing ensures weaves around a safety net for software failure across the DevOps pipeline rather than just at the end of it. While QA automation ensures the precision required for testing, continuous testing aids with the urgency of it.
1.2 Why Does AI Make Sense for QA Automation?
Artificial intelligence is expected to allow better quality testing in terms of efficiency, mainly to the autonomy that comes with it. Advances in AI/ML can help identify patterns that would lead to predictable, zero-touch QA. Moreover, AI/ML would be economical in the long run, eliminating the redundancy of legacy infrastructures and resources we have for QA automation and its administration.
In the existing landscape, enterprises are already dealing with roll-outs and almost daily updates. This already has the dev teams working towards streamlining their part in DevOps, and the same is expected from QA, as well! As we’ll see in a while, continuous testing has already stepped in to do the heavy lifting. We now equip our QA teams with historical insights to attend to predictable cycles, making the QA automation even faster and more efficient. This is obviously where AI-driven QA autonomy comes into play. But in order to understand that transition, we first need to know about the heads and tails of continuous testing.
2. Role of Continuous Testing in the Current IT Scenario
The prime goal that presented the need for a continuous testing strategy was to speed up development. With continuous testing, DevOps teams could evaluate software quality and summon critical feedback metrics much earlier in the CI/CD pipeline while enabling higher-quality and faster deliveries. Time is of the essence in the current IT landscape, and it is only going to get critical from hereon. With the demand for increased development speeds, especially in digital transformation projects, the time-consuming legacy infrastructures need to either be reworked or replaced. Agile and DevOps have already helped organizations to introduce a more incremental software development lifecycle. Continuous testing automation allowed them a step further by offering scope to better the end product anywhere throughout the delivery pipeline.
2.1 Top Companies for Continuous Testing Market
To better understand the potential impact of continuous testing in the market and for the enterprises, let us consider a few of the giants that operate with digitalization at their core.
Of course, the first name has to be one of the fastest-growing digital ventures and the parents for the AWS cloud services. Amazon is famed to have its code updated more than five times in a minute! This implies that instead of introducing some massive updates in maybe six months, Amazon has its code continuously tested, improved, and deployed. Such speeds are not possible with QA automation alone. Strategizing the QA in a way that there’s a scope for testing at virtually every point, right from coding to deployment, is where the key lies.
From DVDs to online streaming was a scary and exciting leap for Netflix. Acquiring the right resources in those early stages would’ve been a challenge altogether. But now, with a reliable DevOps infrastructure in place, Netflix is known to routinely deploy software updates without compromising security. For obvious reasons, Netflix cannot afford any downtimes, and thus providing updates at wartime speed is crucial and only achievable through a continuous testing strategy.
WalmartLabs is rising as a leading technology vendor for e-commerce platforms and mobile applications. Walmart has already been a behemoth in the retail industry over all these years. Venturing into digital services and DevOps has been a whole new chapter, especially with the Hapi and Node.js frameworks bearing no resemblance to product aisles and cash counters. Still, continuous testing and Walmart’s own DevOps frameworks helped them deploy more than a million OpenStack cores in recent times.
Continuous testing is acquiring its well-deserved space in the DevOps pipelines for all the major names exploring the IT market. It all boils down to speedy deployments, quick testing and debugging, and reliable releases. A continuous feedback mechanism is indispensable for this process, and that’s where continuous testing claims its rightful share in the market.
2.2 Services Pertaining to Continuous Testing
Continuous testing encompasses a large number of services to ensure optimized agility and quality in CI/CD delivery cycles. Mostly automated services, these build the wholesome framework that allows the organizations to strategize continuous testing for their DevOps pipelines. Although, the specific services as per the organizations offering them, we can still divide these services broadly into three parts:
Reassessing the QA
First, the existing testing framework of the organization is assessed for its performance and agility offerings. This helps the organization to evolve the legacy systems rather than replacing them with something entirely disruptive for their workplace. The results from the reassessments suggest a few useful tools and frameworks that can improve the existing testing apparatus before it is extended to operate across the CI/CD pipeline.
Roadmap for Testing Integration
In case the organizations have not explored infrastructures like cloud native, it is imperative to initiate a migration for their legacy systems as cloud infrastructure offers the most favorable conditions for continuous testing. Once the cloud-native infrastructure is established, tools and frameworks are added to carry out the following:
- Automated testing
- Test data management
- Test monitoring
- Regression testing
These tools help set up the continuous testing framework for the entire DevOps pipeline allowing enhanced performance and security.
Testing Cycle Optimization
Once the automation testing framework for continuous QA is established, there is also a need for consistent maintenance. Ensuring that the continuous testing processes are well-aligned with the business objectives and the quality standards of the organization is necessary for the long-term benefits of continuous testing.
2.5 Industries Benefitting From Continuous Testing
By 2026, the continuous testing market is expected to reach an evaluation of more than $3 billion. Almost all industries are adopting complex IT infrastructures that digitally empower them to offer their services more smoothly and with a better competitive edge. While DevOps and cloud offer virtualized environments free from manual discrepancies, continuous testing offers the required feedback system that the CI/CD processes cannot practically do without. This growing demand for digitalization has created avenues for continuous testing across multiple industries. Some of the key industries that already have adapted continuous testing for their digital authority are:
- Media and entertainment
2.6 Market Regions Engaged With Continuous Testing
The key players in continuous testing are geographically spread across:
- North America
- The Middle East
- Asia Pacific
These regions also happen to be some of the most active in digital evolution. The advances in the industries discussed above are mainly witnessed in these regions for multiple reasons, including resource availability, talent acquisition, economic support, etc.
Thus, continuous testing is already at its prime and is being accepted almost across the globe and in commercial verticals. It is helping the industries offer quality products and services that can match the high demands without fail. The question then arises, why is there a need to enhance continuous testing by integrating it with AI? The answer to this question is what we will discuss in the next section.
3. AI for Continuous Testing
When it comes to influencing the global market, AI/ML has been far more aggressive than continuous testing. Affecting multiple commercial sectors, including finance, healthcare, retail, education, and (of course) technology, AI has helped with task automation, data mining, cost optimizations, and business intelligence. It won’t be an exaggeration to call AI one of the key influencers when it comes to CX in the 21st century.
Specifically for QA automation, AI can make the testing process smarter and more reliable. Figuring out predictable patterns and improving the testing strategy accordingly can help organizations save even more time and focus on more complex tasks that would actually add value to the business innovation. AI-based systems are also getting more secure, predictable, and favorably functional, which makes them even more deserving of a trial. And maybe that’s why in the later years of the 2010s, AI-driven test automation was being accepted more by organizations. Some of the more popular AI ventures with respect to testing are:
- AI-driven test automation tools
- Self-testing systems
- Testing tools for AI-driven systems
3.1 Need for Continuous Testing to Upgrade for AI
As discussed before, the recent advances in technology and data comprehension have well normalized weekly and even daily product updates. Continuous testing, although still seemingly at par with such speed requirements, is also undeniably on the verge of losing grip. As more and more test cycles go unattended or carelessly dealt with, organizations would soon be looking at huge backlogs and dissatisfied customers. The key problem with Continuous QA automation is its heavy dependency upon structural stability. The processes are well-defined and are forced to not deviate from such pre-meditated instructions. However, more often than not, the malfunction scenarios are unpredictable and take time to be incorporated into the automation processes for future workarounds. This is where AI can take charge. AI-based tools can quickly go over the log data and recognize more useful patterns that manually might not make that much sense. Moreover, these intelligent algorithms can update the test scripts, identify unattended test cycles and improve the QA automation processes for the continuous testing framework. In the next section, we will take a look at some specific examples that can help us better understand the benefits of AI for testing.
3.2 Use Cases and Scenarios Where AI-Driven Testing Makes More Sense
3.2.1 API Testing
Examining an application program interface (API) is generally a part of integration testing. With technologies such as REST taking up the spotlight for more and more tech innovations, APIs are pretty much at the very center of attention. However, even for automation scripts, it is almost impossible to ensure the multitude of scenarios that an API might have to operate under. Therefore, AI-fuelled QA automation can ensure the patterns emerging from API calls are covered more exhaustively so that their functionality, security, performance, and reliability standards are more accurately met.
3.2.2 UI/UX Testing
Validating user interface design can be difficult when complex factors like position, color, size, and appeal of the visual elements are tested with a limited scope of manually curated test scripts. AI-based UI testing tools, on the other hand, can ensure that the finer and undetectable UI bugs are also recognized by recognizing visual patterns that lead to unfavorable user experiences.
3.2.3 Test Data
Test data is critical since it is required by all types of tests in your test suite, including both human and automated tests. Validating common or high-value user journeys, testing for edge cases, reproducing faults, and simulating mistakes are all possible with good test data. However, successfully using and managing test data is difficult. Excessive reliance on data defined outside the scope of the test can make your tests faulty and raise maintenance costs. External data sources might cause delays and have an impact on test performance. Because production data may contain sensitive information, copying it poses a risk. To overcome these obstacles, you must carefully and strategically handle your test data.
3.2.4 Automation Test-Cases
There are AI-based tools that can operate on the document object model to generate sensible behavioral patterns and test-cases autonomously. Identifying the risks for unfavorable deviations for the product performance and experience, these AI-driven test cases can improve the automated test cycles to expand the scope for the continuous testing framework.
It can be concluded that artificial intelligence not only makes sense for continuous testing but is very much a possibility in the near future. The DevOps and CI/CD pipelines can do much better with AI-driven QA automation, as can be seen in the use cases discussed above. We will now look upon some possible entry points that can help bridge AI and continuous testing and help with the further evolution of this marriage.
4. Implementation of AI for Continuous Testing
As we understood from the previous discussion about possible test-case for AI and continuous testing synergy, AI can help continuous testing in the following important ways:
- Eliminating the time and resource wastage for repetitive tasks while digging out exhaustive patterns
- Generating relevant, actionable data for automation test scripts
- Reliably configuring the continuous testing tools to work more instinctively and ensure zero unattended test cycles
Based on these corollaries, we can strategize the implementation of AI-driven QA automation and continuous testing in the following three major ways.
4.1 Autonomous Unit Testing
Unit test is one of the key testing processes when it comes to integration, deployment, and delivery in the CI/CD pipeline. With complex infrastructures and modules running the final product, unit test can be a real challenge. Therefore, AI-based testing tools can be integrated to cut down the resource requirement for continuous testing. These tools can help the DevOps teams to quickly develop test cases that are thoughtfully prepared and align with the business logic. As the unit testing automation becomes manageable, more complex testing processes can also be covered with time.
4.2 Visual Testing
To manage the visual validation, AI-driven tools can be employed to eliminate the overlaps among the UI elements. Such overlaps can be hard to identify and often get skipped by other testing resources. AI/ML tools can develop and execute more complex test cases to weed out these visual bugs by taking into consideration factors like operating systems, browser settings, hardware needs, etc. With non-functional UI testing being managed, more complex functionality behaviors can gradually be assessed with the AI/ML tools.
4.3 API Testing
For API testing, AI/ML tools can operate on the interaction patterns between APIs and the servers or databases that employ them. API QA can be improved by AI-drivel tools that would generate test cases based on communication protocols and security requirements. Moreover, the existing test case can also be improved with time to better suit the QA needs. As discussed above, APIs are the key to comprehending the existing digital systems. Once AI gets an entry in API testing, more complex continuous testing strategies can be built and executed.
Artificial intelligence and machine learning are pretty much operating independently in the digital market — a market where continuous testing has also earned good fame for itself. With rising CX complexities, the demand for digital transformation and automation has become overwhelming. Although continuous testing seems our best bet to ensure the QA standards for the products, we need to augment it for not-so-distant future needs. AI-driven QA automation can therefore be brought into the picture to serve this purpose. As we saw in this paper, AI has already established itself as useful for a variety of testing scenarios. Therefore, using these scenarios to their advantage, organizations can slow build an AI-based framework for continuous testing and QA automation. A critical aspect of software development, test automation can offer better, more reliable products, and artificial intelligence can be just the right avenue for its growth.
Opinions expressed by DZone contributors are their own.