AI in QA for enhanced testing capabilities

Written by: Dzmitry Lubneuski, CIO at a1qa

A lot has changed since the advent of AI. For instance, its capabilities allow sales teams to use chatbots to enhance CX; cars are equipped with computer vision systems for better safety; educational establishments leverage AI-driven custom avatars to generate video lectures.

QA and software testing is no exception. But why is AI gaining momentum? 

Perks that AI provides for QA teams

• Superior productivity: As AI speeds up repetitive activities, testers perform more tasks in less time, test more meticulously, and release software faster.
• Reduced time: Manual testing focuses on repetitive checks. AI-driven test automation expedites test runs and increases velocity of development cycles.
• Positive CX: Considering previous behaviour, AI predicts how users interact with software, enabling customised QA with more effective prioritisation of test scenarios.
• Better precision: AI can minimise the risk of human error that may arise in monotonous manual testing and more accurately detect issues, anomalies, and their causes.
• Wider coverage: With the help of AI, QA engineers can find software areas with concealed issues that can be missed by manual testing.
• Financial improvement: AI optimises QA costs by swift test executions and handling increasing QA needs without allocating additional resources.

Top 8 beneficial AI use cases in QA

Below are some use cases on how AI can be applied in software testing and test automation:

1. Support with identifying priorities: AI helps automatically analyse data sets and make decisions on which tests to execute first, considering risks. QA engineers ensure quality more accurately, address issues earlier, handle frequent changes common for Agile-driven projects, and work on critical areas for business.
2. Faster creation of test cases: QA engineers can leverage AI to evaluate written demands for software, code, test data, previously identified issues, to automatically create test cases and maintain them with functionality accretion, speeding up QA. AI quickly analyses huge data sets, so more edge cases that might be missed by people can be considered by such test cases.
3. Better for visual testing: QA engineers can accurately and swiftly catch minor visual inconsistencies between images, videos or texts during a comparative analysis with automation. QA teams can boost UX and increase the chances that the software will resonate with end users.
4. Foreseeing software issues: AI learns from previously processed information, so QA engineers get access to data about possible issues in the future and the parts of the IT products where such errors may occur. AI can scrutinise code to detect code smells — the signs of deep matters in the codebase — that further cause problems, which saves efforts for rectifying glitches.
5. Improved reporting opportunities: Testers can leverage test automation frameworks that have built-in AI elements. It assists in defining unsuccessful tests and creating test reports with interactive charts, which speeds up the analysis of causes of setbacks and eases processing of results for stakeholders. AI customises reports depending on who they are intended for. QA managers find KPIs and test breadth, while testers — technical aspects.
6. More effective performance testing: QA engineers can use AI to increase the precision of determining and mimicking real-world scenariosand enable dynamic adjustment of the load during performance testing. It helps boost testing accuracy and detect more issues that manifest themselves only under high load. AI can assist QA teams in fulfilling continuous monitoring of vital metrics (RT, 90th percentile, ALT), boosting velocity and minimizing manual activities due to its automatic nature.
7. Enhanced regression testing: Regression testing gradually expands. Considering modifications in the software interface, tests must adapt to these alterations and fix themselves, especially regarding visual elements. ML possesses self-healing capabilities, thus automatically adapting to changes, accelerating testing cycles and boosting stability of automated checks.
8. Automated test runs: AI in test automation gives opportunities for executing more tests in less time, decreasing the amounts of manual intervention, freeing up extra time for testers, and allowing them to focus on other important activities.

Advice on embedding AI into QA

AI introduction is challenging, so I recommend:

• Educate teams: Create practical-oriented training courses to provide specifics of AI-powered quality control and lay foundation for further successful QA.
• Choose the toolkit: Research AI-driven solutions considering budget, project duration, infrastructure to select best options that contribute to project effectiveness.
• Supervise the process: Control its usage by regularly requesting feedback from team members leveraging it to ensure AI boosts testing efficiency.

Final thoughts

AI-driven QA contributes to cost-effectiveness, better productivity, accuracy, time-to-market, CX, and coverage. Although its implementation is challenging, it’s beneficial for diverse QA directions — from test cases generation to improving regression testing capabilities.

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