The smarter, faster and modern forms of testing are important in the race to build better software quicker, owing to the growing complexities of the software-driven world. But it is not simple in the hyperconnected and complex environment, and this is where AI-driven testing can help.
The application of AI/ML in software testing should be focused on delivering real improvements in the testing process and making intelligent automation a reality while making the software development lifecycle easier. While test automation tools are capable to automate some of the manual routine tasks, they cannot perform modelling, failure detection, application discovery or predict uncertainty.
While some of the popular test automation tools are capable of doing that, they certainly have a set of limitations. For example, most automation tools have the feature to schedule tests, run them and publish the findings. However, they are not capable of capturing the changes in real-time. AI, on the other hand, can capture the modifications in code with time stamp, store the current test status, and decide which tests to run.
The requirement for quick development and faster test automation is increasing every day owing to the increasing technological complexity and the competitive market scenario. This only adds challenges in the software testing lifecycle.
While total time to finish an entire SDLC has been reduced with the help of DevOps and agile methodologies. AI can improve it further by unlocking the true potential of software testing. Here is how AI & ML can help overcome the QA challenges, and the benefits it provides:
While testers develop test cases based on the user stories and customer requirements, NLP based tools can do the same and save a lot of time and effort. If the tester collects and puts details such as user stories, acceptance criteria, and test scenario description, NLP techniques and processes can analyze and convert all this information in unified modelling language (UML). UML will further turn this information into a set of diagrams and set linkages between among them. The resulting output will be an automatically generated test case.
Each time when development teams expand the existing code to make changes or add more features, they are required to carry out new tests and add them to the regression suite. As a result, the total time required to complete the testing and deploying the product increases.
By leveraging AI, the recent code changes and current test status can be accessed adequately and test coverage for releasing the application to production can be identified. This can drastically improve the regression cycle time and can be customized to determine relevant test cases from the test suite that covers both modified and affected parts of the code. .
Reduce ignored bugs probability and improve testing
How much testing is enough? – a common question that generally lingers throughout the test cycle.
Ensuring the full test coverage becomes more difficult if the new functionalities are added and the complexity of the application is increased. In such cases, the testers end up running the entire suite or some predetermined set with a risk of missing out on defects.
AI can predict the test case that is capable to find the defect based on the test history, current test status, and code coverage. AI can allow you to focus on the areas at risk rather than taking a haphazard approach to the testing. This helps in improving the quality and delivering overall efficient products.
Changing and adding new features in an application is common and needs reoccurrence. As the objects can’t be found, the updates made in application will typically result in UI test breaking. In such scenarios, maintaining test suits and object repositories becomes a tedious task.
An AI-infused tool can generate test scripts and framework based on historical data of consumer interaction with the product. AI can also be used in dynamically updating the test suites when there are changes in the application – thereby enabling auto-maintenance and self-evaluation.
Impact on release
The ignored bugs can create a negative impact if enough attention is not paid to data management.
When neural networks are developed using the test history and current test cases and test requirements, they are capable to predict the overall impact on the upcoming release.
For example, by having data regarding customer satisfaction, companies can make the necessary adjustment to ensure that the release has a positive impact on customers.
Root cause analysis
While QA engineers do everything right, sometimes, some bugs remain unnoticed. Finding out the cause of the issue becomes critical when the bugs remain unnoticed. AI can help in identifying the root cause in such cases by getting answers to the questions like how, where and when in a matter of minutes or even seconds.
The ML algorithms can compare the relevancy of each new event with learning database and find out if it matches with any previous event. This way, by identifying changes, the underlying defects and root cause can be identified.
To remain ahead of the competition, companies need to deliver top-notch services to the clients. Companies that can provide value-add to their clients are always rated a notch higher amongst their competitors. Forecasting the client requirements to understand the possible testing scenarios can be very helpful in improving overall TAT. Analyzing the data to better understand the existing products, customer behavior and all the upcoming features can also happen by using AI/ML.
While AI and ML are completely revolutionizing the landscape of testing, enterprises will continue to go above and beyond to optimize and improve the software development life cycle. As QA teams embrace automation and welcome AI technology into their software testing methods, the results contribute to new solutions and ways of working, rediscovering where possible.
At eInfochips, we have developed an automated solution for testing of different voice assistants, known as VAQA (Voice Assistant Quality Automation) framework. VAQA framework supports automation of voice enabled or voice assisted devices, like smart speakers, headphones, voice controlled home appliances etc. The automation framework acts as an accelerator resulting in faster time to market and reduced operational costs for end customers.
Our Cognitive QA offerings help organizations perform both “testing of AI” and “testing with AI” in SDLC with advanced machine learning algorithms. We offer expertise, processes, and tools to help organizations overcome challenges regarding the testing process of AI based software, with best-in-class quality metrics/processes and faster time to market. For more information please connect with us today.
This post was last modified on February 19, 2021 8:00 am
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