In today’s digitally evolving business landscape, just testing your software is not enough to ensure its quality. AI has taken considerable leaps in the past few years to ensure that every software need is delivered speedily while not losing out on top-notch quality. These two key factors - agility and expedited CI - drive the integration of intelligent technologies with Quality Management, offering multiple benefits while streamlining quality assurance. Indulging in-house IT teams or outsourcing test automation services can prove to be a crucial decision in ensuring the success of your automation strategy, as well.
This article delves into the role of AI-powered test automation in quality assurance, the benefits of AI-enabled quality solutions, and popular AI-automated quality assurance tools.
How Does AI-powered Test Automation Overcome Traditional QA Challenges?
Challenges in Traditional Quality Assurance
How AI-powered Test Automation Helps in QA
AI-based Automated Test Cases for QA
Conclusion
In conclusion, AI is transforming the face of the business landscape by streamlining one of the most crucial tasks of the SDLC – testing. Be it enhanced performance, improved bug detection, increased test coverage, or an inclusive user experience, AI-powered test automation is bringing new QA capabilities into the fold.
To explore all potential uses of AI in streamlining your business processes, you can contact one of the market leaders for all your business needs.
This article delves into the role of AI-powered test automation in quality assurance, the benefits of AI-enabled quality solutions, and popular AI-automated quality assurance tools.
How Does AI-powered Test Automation Overcome Traditional QA Challenges?
Challenges in Traditional Quality Assurance
- Inflexibility in Adaptation: The current digital environment offers a multitude of dynamic functionalities making it harder for manual testing methods to adapt quickly.
- Application Complexity: There is a constant rise in software complexity to deal with the challenges facing digital business adoption. This makes it difficult for manual testing to keep up with the increasing application complexities.
- Maintenance: Manually up-keeping these complex systems can prove deteriorating to the smooth functioning of software systems.
How AI-powered Test Automation Helps in QA
- Efficient Test Build: AI in QA helps companies analyze successful apps to understand market requirements. New test cases can be developed based on this understanding to ensure glitch-free performance in achieving specific goals.
- Predictive Analytics: AI automation in quality assurance analyzes customer data to determine evolving user needs and browsing practices. This helps testers, designers, and developers offer better quality support. Machine learning improves the AI platform with analyzed user behavior, providing accurate predictions.
- Improved Bug Detection: Traditional manual testing often overlooks blind spots in the test scripts making the test incomprehensive and prone to bugs. AI enhances the bug detection capabilities of test scripts while exposing any hidden system issues. Hence, AI helps in building comprehensive test coverage.
AI-based Automated Test Cases for QA
- Automated Visual Validation through UI Testing: AI is enhancing CX by ensuring that all the UI elements appear as intended. As opposed to the traditional belief that UI testing equals visual testing, visual testing is responsible for checking the overall visual feel of the interface rather than determining the intended functioning of all the UI elements.
- AI Spidering: Spidering (web crawling/scraping) is used to automatically extract information from web pages through crawlers. These crawlers are popular for gathering data insights from the web, following automated scripts. Integrating AI/ML to spidering can largely help businesses in gathering intelligent, usable insights.
- Automating API Testing: Automated API testing examines the layers beneath the user interface to confirm that databases are communicating correctly with each other. This leads to better coverage, as all system components are tested comprehensively. This, in turn, results in higher-quality software and greater satisfaction among users.
- Only Repeat Necessary Tests: As changes are introduced in software, repeating all manual tests to cross-check for proper functionality is a routine protocol. However, are you sure you need to repeat every single test? Ai-powered frameworks not only automate these repetitive tests but also determine (through intelligent analytics) which tests can be skipped altogether.
Conclusion
In conclusion, AI is transforming the face of the business landscape by streamlining one of the most crucial tasks of the SDLC – testing. Be it enhanced performance, improved bug detection, increased test coverage, or an inclusive user experience, AI-powered test automation is bringing new QA capabilities into the fold.
To explore all potential uses of AI in streamlining your business processes, you can contact one of the market leaders for all your business needs.