How Can Digital Testing be Facilitated by Artificial Intelligence?

 

Artificial intelligence plays a crucial role in digital assurance and testing, and its scope across industries is also increasing each passing day. As companies adopt software-driven tools like cloud computing and IoT, they can offer a significantly better customer experience. Further, they can achieve growth in any environment by being flexible and responsive to changes. Artificial intelligence takes this a step further by providing intelligent features like voice and image recognition, and chatbots, among others.

Today, AI empowers digital QA testing with automation and analytics that bring substantial change to entire processes. It makes digital QA easier through the automation of redundant tasks, allowing testers to focus on decision-making, thereby shortening testing cycles. Consequently, digital quality assurance enhances client satisfaction levels and helps businesses build a loyal customer base.  

In the below-mentioned segment, we take a look at the different forms of AI and how they make it easier to deliver digital assurance services for software testing. 

 

Artificial intelligence in software testing  

Artificial intelligence is a blanket term for a vast number of processes and technologies achieving the capabilities of human intelligence. These include the ability to learn from previous experiences to reason, discover meaning, and generalize. When breaking down the software development life cycle, digital QA emerges as one of the key areas where the impact of AI can be significant. 

Here are some of the key ways in which AI can accelerate digital QA testing for creating high-quality applications.  

Self-healing automation scripts 

The stability and maintenance of test scripts are one of the most challenging parts of software testing. To make any digital assurance testing foolproof, the test cases need to perform the setup and validation automatically. With AI and machine learning, it makes it easier to extract key parts of the software like attributes, DOM, and others. It then maps them to key actions performed during the tests. This makes the locators self-adjustable to changes, thereby saving time and reducing errors. 

Improve regression testing by identifying high-risk and glitch-prone areas 

Analysis engines can be created based on AI and deep learning to improve the accuracy of defect prediction and prevention models. Thereafter, data on common defects can be fed into the analysis models along with the common features they affect. These analysis engines can then accurately predict the recurrence of the defects in high-risk areas and prevent their appearance. Working models can integrate data on residual errors to identify potential gaps in the testing process. Based on test patterns and relativity data, the models can auto-generate test scenarios in glitch-prone areas, thereby enhancing test coverage.  

Auto-prioritization and selective test execution 

Current automation tools, especially those found in the CI/CD pipelines, cannot choose among the tests based on priority. They end up testing all or certain pre-defined parameters at a time. Artificial intelligence bots can assess the test coverage patterns, among other metrics, critically to decide the tests and their order to run. As a result, digital testing becomes much more efficient, as tests can avoid unwanted areas while ensuring optimum test coverage.  

Automate API test generation and maintenance 

AI and machine learning can also be used to convert manual UI tests into automated API test scenarios. Currently, API testing is primarily done based on pre-defined patterns that do not cover any variations caused by driven UI usage. An API test generator can use reasoning to detect underlying interface calls and variations and analyze patterns and relationships among various API calls. This data can then be used by self-learning models to create automated, stable, and scalable API tests.  

Executing test cases automatically by identifying the hotspots 

Establishing the test parameters is one of the key requisites in digital quality assurance. Test cases and scenarios are developed and executed after the objectives are set. Natural Language Programming and Machine Learning, two of the key areas of AI, can be used to identify and execute tests automatically from pre-defined arguments. Test automation reduces the number of redundant human tasks, which not only frees testers for more advanced tasks that support the bottom line but also reduces the probability of human errors. 

Conclusion

AI allows today’s businesses to be adaptive, creative, flexible, and innovative. As artificial intelligence gets smarter by the day, businesses can accelerate digital assurance to tackle even bigger challenges that might appear along the SDLC comfortably. Applying AI techniques at multiple levels of the testing lifecycle enhances the efficiency and effectiveness of the testing process, thereby improving the overall value of the software solution.