Artificial intelligence (AI) and machine learning (ML) have made deeper inroads in several business verticals. As the technology is becoming mature, it is now also used for more business-critical scenarios where it can enhance efficiency, reduce human errors, decrease costs, and predict information and cases to benefit businesses in the future.

As a result, QA and software testing services have now also started leveraging AI and ML to make the entire process more feasible, efficient, and cost-friendly for businesses.

What are AI and ML in QA?

The application of artificial intelligence and machine learning in quality assurance (QA) and software testing is leveraged to enhance the efficiency of the software development lifecycle (SDLC). Through problem-solving, reasoning, and machine learning, AI is leveraged to automate and cut down the time taken for redundant and tedious tasks.

Well, test automation tools already have the capacity to automate tests but they have certain limitations. Here, AI is used to fill those gaps and eliminate limitations. For instance, the majority of automation tools can be used to schedule, run and publish tests. However, they are not capable of determining the type of test to run. They can either run some predetermined tests or the entire suite of tests. 

In such cases, AI can review the current status of code coverage, test status, recent code changes, and more to identify the most suitable test to run. Hence, it can help in making data-driven decisions to identify the right test coverage. AI and ML have brought a new dimension to the end-to-end QA process. 

Let us check out how QA and software test automation is evolving at a rapid pace with changing industry demands and technologies.

QA is Getting More Complex

Amid increasing technical complexities and cutthroat market competition, the need to roll out reliable and performance-oriented applications has increased exponentially. Hence, the need to execute software testing in more smarter and efficient ways has also increased. This has increased the challenges for QA and software testing teams manifold.

• Ensure Adequate Test Coverage: QA teams are often not sure about the amount of testing that is required for the project. With more complex applications, it becomes even more difficult to ensure an adequate amount of test coverage. In such conditions, test engineers often end up running a predetermined set and miss out on some business-critical defects. There is also a chance of running the entire suite.

• Long Regression Testing Cycles: When you add new changes to the software, the existing code that has been used for testing may not work anymore. After every time a new code is added to the existing one, it is essential to conduct new tests and all the information should be added to the regression suite. It may take a very long time to complete the regression testing cycle.

• Maintain Automated Scripts: Applications undergo changes on a regular basis. However, the updates can break a few UI elements due to the unavailability of objects. Hence, it is crucial to maintain object repositories and test suites for such scenarios.

• Ignored Bugs and Defect Leakage: The issue of ignored bugs is extremely diverse and may bear counterproductive consequences. In the absence of proper attention to data management, you may get a pile of ignored bugs.

How AI and ML can Help Solve QA Challenges?

With the current practice of DevOps, continuous testing, and agile; the software development process has become significantly faster. Hence, to unlock the real potential of software testing, it is crucial to leverage AI and ML. 

Here is how AI and ML can help:

• Accelerate Regression Testing and Identify Proper Test Coverage: AI can help in reviewing current test status, recent code changes and predict the adequate amount of test coverage to ensure app release for production. You can also customize the regression cycle to find out the requirement of tests.

• Test optimization and Reduce Probability of Ignored Bugs: QA testing teams can leverage AI to easily find out the test that is more capable of identifying defects on the basis of risk data. AI will help you concentrate more on risk-prone areas and help in delivering better quality. The focussed approach will help in reducing the number of ignored bugs.

• Auto Generation of Test Scripts and Self Healing: You can leverage AI to autonomously create test scripts and frameworks on the basis of consumer behavioral data and historical test data. An AI-enabled tool can update test suites dynamically after changes are pushed to the application. Hence, enabling auto-maintenance and self-healing of applications.

• Release Impact: Test history combined with neural networks along with data generated from current test runs can identify the impact of the new releases on current users. For instance, to identify whether the current satisfaction is going up or down, organizations can leverage this information. It can help organizations to make required calibration to create a positive impact on the end-user.

• Root Cause Analysis: There are events where even after executing everything correctly, QA teams are unable to identify certain bugs. In such scenarios, QA teams are expected to find out the root cause of the lapse. AI can help test engineers to search for pertinent answers and lapses in the process.

• Forecasting Client Requirements: QA teams capable of adding value to the client are always preferred over those who operate in the status quo. And when a QA team is capable of forecasting client requirements, you can easily give a customer-delight experience to the client. Leveraging ML and AI to forecast also enables businesses to understand their customers in a better manner through new features and recent services or products.

• Latest Trends and Forecasting: As per the World Quality Report, more than 57% of respondents agreed that businesses are already using AI and ML in testing or planning to include in the near future. It can also lead to the emergence of new testing and QA roles to streamline the process.

• AI QA Strategies: The professionals are required to have a deep understanding of both technical and business aspects. They should also be able to understand how AI can help a business.

• Data Scientists: Data scientists of the future need to have a greater understanding of data analysis methods. They should be capable of efficiently leveraging ML test data, statistics, mathematics, and predictive analytics to create self-sufficient test models.

• AI Test Specialists: The specialists need to have extensive experience in testing. They should also be able to understand machine learning algorithms, natural language handling methods, and other advanced skills needed to test an AI application.

Popular Tools

Applitools

The AI-powered tool is used for monitoring and visual testing. It is capable of running tests on multiple platforms and browsers. It leverages AI to find out impactful changes in UI and mark them as enhancements or bugs. It can also leverage ML or AI to run automated maintenance tasks.

MABL

It is capable of automatically detecting the changes occurring in the elements of your application. To compensate for those changes, also dynamically updates the changes. All you have to do is just show the workflow that needs to be tested.

Sealights

It is a cloud-based automation testing platform. It leverages AI and ML to analyze code base and run tests that could cover the bugged area. Its intuitive dashboard reflects analyzed results and also offers continuous test management.

AI

It works like building as a tool that adds an AI brain to Appium and Selenium. A simple format is used to define tests including the BDD syntax of Cucumber. Hence, no code is required and also you don’t need to handle element identifiers.

Appvance IQ

It is capable of generating test scripts on the basis of data generated by real users. You can leverage these scripts for performance and functional testing.

Implementation Challenges

While we discuss the possibilities of implementing AI and ML into the real world of QA and software testing, it is also crucial to discuss all the potential challenges that businesses could face during the process.

Let us find out:

• Seamless integration with legacy applications
• Limited knowledge of AI and ML processes
• Identification of exact uses cases to implement AI and ML
• Availability of unstructured or structured data without human intervention or bias

Wrapping Up

QA and software testing have come a long way from the linear waterfall model to agile and DevOps. And as we step into working with futuristic technologies and advanced workflows, we need to leverage AI and ML to stay aligned with fast-paced SDLC. Hence, the quality assurance domain is poised to witness substantial use of AI and ML in the near future.

AI and ML will help QA teams to achieve greater accuracy, generate more revenue, and cut down the cost of multiple QA processes. Hence, it will help in making your business more competitive while enhancing the customer experience. Now, test engineers need to research more on AI and ML, leverage AI-enabled tools and figure out efficient ways to leverage them in the day-to-day process.