May 02

Artificial Intelligence-driven Quality Assurance and Automation


Enterprises are embracing the latest technologies in their software development journey to build a strong data engineering foundation and fuel innovation. The biggest trend in QE space for the next decade will be Artificial Intelligence (AI). Tech Giants such as Facebook, Amazon, Google, and Microsoft invest billions on AI and Machine Learning initiatives and integrate AI into their applications they are developing. Organizations must devise a strategy around AI for enhanced software development speed and to achieve a successful digital transformation.

Features of AI-Driven QA/Test Tools
Adoption of AI coupled with test automation enables to get up to speed on the state of QE and helps in the following manner:

Reuse Test Cases
The complex process of developing well-written test cases and re-using them for future is made simpler through AI-based test automation tools.  AI-based testing tool inspects the application and gathers vital data through screenshots to measure load time, analyze basic UI elements and produce compelling test case suite.

Intelligent Requirement Gathering
During requirements gathering, numerous manual functions like reporting, managing the impact of change and tracking approvals, impacts the project deliverables. AI-powered requirement gathering understands the limitations of complex systems and tracks key metrics and KPIs that can be used to predict quality and performance issues proactively.

Simplified Exploratory Testing
Testing can be made more effective with fewer resources & codes and maximize the device coverage in minimal time by using the AI-engineered approach. AI identifies scenarios easily through intelligent assistants that capture test data and user performances by steering through an application or system and record default test cases.

AI-Enabled Error Identification
Leverage the AI-led QA to unearth defects beforehand, optimize testing and predict failure points, thus reducing the overall cost and achieving high customer satisfaction. Al-enabled performance analysis guarantees security of an application or system by identifying any potential attempts of unauthorized access to the system.

AI-powered Visual UI Testing & Monitoring
Artificial Intelligence amplifies test platform with Visual Testing proficiencies to support dev teams release visually perfect web apps with incomparable speed and quality. With the AI-powered monitoring techniques, analyzing outcomes of regression testing is simple, effective and effortless.

Maximum Code Coverage in Limited Time
Maximum test coverage is achievable when test plan, test plan, test strategy test cases etc., are planned at an optimum level. The tests could be based on real-time interactions, user flows, keyword, interactions and data-driven approaches. Measuring test coverage with AI-based tools enables the businesses to achieve 100% code coverage.

Faster Decisions with AI
AI can get into the depths of data and detect the root cause of issues impacting business performance. It enables analyzing customer data for optimal efficiency, improved innovative capabilities, fine-tune products and services, or better opportunities, hence significantly contributing towards streamlining decisions for better processes.

Conclusion
Quality Assurance Driven by Artificial Intelligence
AI-based automation testing solutions will help streamline release cycles, upsurge the efficiency of IT operations, decrease the production troubleshooting time and enhance the future of software testing in multiple aspects. Artificial Intelligence has entered our day-to-day living, enabling organizations deliver high quality results with minimal or zero manual intervention. Knowledge in various facets of quality assurance is key to build and implement an efficient AI-based QA platform.

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