Artificial Intelligence is the current buzzword in software development industry. But what does it really mean in QA context?
What is AI in QA?
The application of AI in software testing tools is focused on making the software development lifecycle easier. Through the application of reasoning, problem solving, and, in some cases, machine learning, AI can be used to help automate and reduce the amount of routine and tedious tasks in testing.
Although, test automation tools already do that, they have certain limitations and AI can be used to remove those limitations. For example, most automation tools can be scheduled to run tests and publish results. But they cannot determine which tests to run. They end up running either the complete suite or some predetermined set. AI in such cases, can review the current state of test status, recent code changes, code coverage, and other metrics, decide which tests to run, thereby helping with decision making and identifying test coverage.
AI & ML are bringing new dimension to QA. The diagram below shows how testing practices have changed over time. Till 2018, it was focused on CI/CD, Scalability and Continuous testing.
QA is only getting more difficult
The competitive market scenario and increasing technical complexity means that the need to develop at a faster pace and test in a smarter way is increasing every day. This only adds to the challenges that a QA department faces.
- Long regression testing cycles – When adding new changes, existing code that has already gone through testing may stop working. Each time the development team expands on existing code, they must carry out new tests and add to the regression suite. Regression testing cycles can grab a long time to complete.
- Ensuring adequate test coverage – Throughout the test cycle, a big question that lingers around is how much testing is enough? The more complex an application becomes, the more challenging it gets to ensure full test coverage. In such cases, the testers end up running the entire suite or some predetermined set with a risk of missing out on defects.
- Maintaining automated scripts – Application changes are a regular and needed occurrence, but updates made will typically result in UI tests breaking because objects can’t be found. Maintaining test suites and object repositories causes major headaches for testers in such scenarios.
- Defect leakage & ignored bugs – The problem of ignored bugs is very diverse and bears extremely negative consequences. If you do not devote enough attention to data management, then, as a result, you will receive a whole bunch of ignored bugs.
How can AI & ML help solve these QA challenges?
While the current practices of agile, continuous testing and devops are keeping the software development process at pace, unlocking the true potential can only happen by leveraging the power of AI in software testing. Here is how AI & ML can help overcome the QA challenges and the benefits that they provide:
- Accelerating regression testing & identifying adequate test coverage – AI can review the recent code changes, current test status and identify the test coverage adequate for releasing the application to production. Regression cycles can be customized to determine what’s required and how much is required.
- Test optimization & reduced ignored bugs probability – AI can be applied to testing in a way to help companies determine which test is most likely to find a defect based on the risk information gathered. Rather than taking a haphazard approach to testing, AI enables you to concentrate on the areas at risk thereby delivering efficiency gains and ensuring a great quality experience. Additionally, with this focused approach, the ignored bugs probability will get significantly reduced.
- Auto-generation of test scripts & self-healing – AI can be used to autonomously generate frameworks and test scripts based on historical test data and consumer behavioral data collected on how your customers interact with your product. With an AI-infused tool, test suites could dynamically be updated when application changes are made, thereby promoting self-healing or auto-maintenance.
- Release impact – Neural networks, combined with test history and data from current test runs, can predict how an upcoming release will impact users. For example, will customer satisfaction go up or down? With this information, companies can then make necessary adjustments to ensure that the release has a positive impact on customers.
- Root cause analysis – There are cases when QA Engineers does everything right, but for some reason, the bug remains unnoticed. When this problem comes on the scene, the tester needs analyzing the causal relationships of the incident. AI can find answers to questions like how, where and when in a matter of minutes or even seconds.
- Forecasting client requirements – Companies that can provide value-add to their clients are always rated a notch higher amongst their competitors. And when it comes to software testing scenarios, the use of forecasting client requirements for these purposes is just something that can modify the whole situation tremendously in your favor. In this case, forecasting empowers enterprises to analyze customer data for a more proper understanding of the most recent products and features that they need.
Current Trends & Forecasting
According to the World Quality Report survey, around 57% of the respondents said they had projects involving the use of AI for QA and testing, already in place or planned to the next 12 months.
The World Quality Report also forecasts the emergence of new QA and Testing roles to accommodate and imbibe AI into QA strategies.
- AI QA strategists – These professionals will need to have a solid grasp of technical and business aspects and understand how AI is applicable to business
- Data scientists – Future data scientists will need to be experienced in data analysis methods and be able to use machine-learning test data, mathematics, statistics, and predictive analytics to create models
- AI test specialists – These specialists will need an extensive background in testing and be able to understand natural language handling methods, machine learning algorithms, among other advanced skills to take part in testing AI apps.
Current Tools Available
Some of the most popular AI-powered testing tools are listed below:
- Applitools – It is an AI-powered visual testing and monitoring tool that can run tests on different browsers and platforms. It uses AI to identify the meaningful changes in UI and identify them as a bug or enhancement. It also leverages ML/AI for automated maintenance.
- Sealights – Sealights is a cloud-based platform that uses AI and machine learning to analyze the code and run tests which cover the impacted area. The dashboards show analyzed results and provides a continuous test management.
- AI – It is building as a tool that will add an AI brain to Selenium and Appium. Tests are defined in a simple format like the BDD syntax of Cucumber, so it requires no code and no need to mess with element identifiers.
- MABL – MABL can automatically detect whether elements of your application have changed, and dynamically updates the tests to compensate for those changes. You just need to show the workflow to be tested and MABL does the rest.
- Appvance IQ – It generates test scripts based on activities of real users. These scripts can be used for functional & performance testing.
Possible Implementation Challenges
While we speak about translating the potential of applying AI & ML into reality, it is also important to talk about the possible challenges that organizations may face while leveraging AI for testing. These include:
- Identifying the exact use case where it needs to be implemented
- Integration with existing applications
- Lack of AI & ML process knowledge
- Availability of structured/unstructured data without human bias (usually a part of training and testing datasets)
Conclusion
As we have progressed from a linear waterfall model to agile, the future is all about AI and machine learning technologies. ML and AI are undeniably growing to be significant elements in QA as well. AI will advance accuracy, give enhanced revenue and lower costs for all QA processes. Henceforth, it improves competitive positioning and customer experience.
We need to be upfront and start digging more about the various aspects of AI, take the hands-on in AI-powered tools and utilize them. There are so many places where AI has already paved its way and we need to be very keen about how we are going to device out test cases to test such applications and deliver quickly.