In software development, Quality Assurance is a procedure that ensures the quality of a product before it’s released to the users. Quality assurance includes activities such as testing, code reviews, and inspection of the code. Quality assurance is vital to ensure that the product meets customer requirements and expectations. Quality assurance helps to reduce the risk of errors, bugs, and defects in the product before it is released. Quality assurance is an integral part of the software development process and helps to ensure product quality and customer satisfaction.
Artificial Intelligence (AI) is increasingly essential in Quality Assurance (QA). AI can automate specific processes, such as automated testing and static code analysis. AI also has a role in data analysis, helping to identify patterns and trends in data that can be used to make decisions and develop strategies. AI can also automate the bug triage process, helping prioritize which bugs should be fixed first. AI can also help identify the bugs’ root causes and suggest appropriate remedies. Finally, AI can automate the process of creating test cases, helping speed up the QA process. AI tools like ChatGpt, TnesorFlow, and H2O, among others, have played significant roles in quality assurance. Below are a few of the roles artificial intelligence plays in quality assurance
Visual user interface testing
AI assists in proper user interface design and the visual approval of website pages. AI can test different contents on the User Interface. These tests are challenging to automate, usually requiring human intervention to make a design decision. Nonetheless, with ML-based visualization tools, contrasts in pictures are seen in a way that would not be feasible for people to pinpoint. AI testing removes the manual effort of modernizing the Document Object Model, constructing a structure and profiling risks.
Generating Test Documentation
Writing tests from acceptance criteria is a determining part of the quality assurance process. As a QA engineer, you must ensure that the product in the development process meets the requirements and expectations of the users. One of the ways you can achieve this is by generating and executing tests based on the acceptance criteria provided by the product team or other stakeholders. These then get broken down into test steps and are usually written in a cloud-based service, although some may still use excel.
Generation & Execution of Automated Test Cases
AI testing tools learn from human behaviours through training data and evolve over time to change the way quality assurance operates in a project. So, these AI models can be trained to think and act like a human tester by leveraging the power of predictive analytics to identify patterns, trends, and relationships in data that a human tester may not be able to discern. This would enable the AI tool to generate new test cases, execute them, and report the results without human intervention. This helps the QA team break free from manual testing tasks, improve customary testing efforts, and achieve better test coverage.
Ensuring Test Consistency
Human testers are prone to error. Even the most experienced testers need to improve while performing manual testing tasks. Therefore, one of the most appropriate ways to ensure test consistency is by delegating repetitive and mundane tasks, such as regression testing, to AI test automation tools. These intelligent automation solutions can run test suites multiple times with different input values and report any inconsistencies in the system under test. Moreover, test cases and results are maintained in a centralized repository, helping QA teams to keep track of all testing activities, avoid duplication of effort, and ensure that all tests are being executed as intended.
Defect Detection and Prediction
Thanks to machine learning algorithms, AI-based test automation tools can go beyond traditional functional tests to identify issues and predict potential risks early on during the software development process. By feeding data such as application logs, code commits, and issue-tracking systems into machine learning models, AI can detect anomalies and potential defects before the code is deployed to production. Additionally, by analyzing past defect data, these AI-powered test automation tools can predict which areas of the code are more likely to have bugs and need further testing. This would help QA teams focus on building tests for high-risk areas first and prevent potential defects from slipping through the cracks and making it to production.
Improved regression testing
With fast deployment, there is always an increased need for regression testing, and sometimes the testing is to the point where people need help to practically keep up. Organizations can utilize AI for more tedious regression testing tasks, whereas ML can be used to create test content. In the case of a UI change, AI/ML can scan for colour, shape, or size. Where there would otherwise be manual tests, AI can be utilized to approve the progressions that a QA tester may miss.
Well-researched build release
Using AI in QA makes it possible for AI development companies to examine similar apps and software to determine what contributed to their success in the market. Upon understanding the market requirements, new test cases can be made to ensure that the app or software stays intact when it comes to achieving specific goals.
Conclusion
The possibility of artificial intelligence in quality assurance could be endless. AI serves well in cybersecurity, so why can it do the same for QA and testing? As we have outlined above, AI technologies can open software testing bottlenecks and streamline the entire process in multiple aspects. Remember that autonomous testing is still in the very early days, and we have only just started with artificial intelligence and its potential.
