Agivant has strategic partnerships with Test Rigor, Data Dog, and Splunk to accelerate AI-driven test automation.


Efficiency with testRigor


Increase Testing Efficiency By 40% Using Artificial Intelligence.
Test Automation in Agile development is critical to delivering consistent quality that meets users’ expectations across various configurations and environments. It involves using automated tools and frameworks to execute tests, validate platform behavior, and ensure the platform functions correctly across different devices, operating systems, configurations, and browsers.
Artificial Intelligence usage in testing strategies drives excellence in test coverage, improves compatibility, identifies performance issues, and delivers a more reliable and robust software platform across various devices, operating systems, and configurations.

- Test Case Generation:Â Generative AI provides a new perspective on development test cases. Machine learning techniques can analyze existing test cases and generate new ones that cover diverse scenarios and edge cases, improving test coverage.
- Intelligent Test Prioritization:Â Analyze the platform's usage data, user feedback, and defect patterns to prioritize tests based on their impact and likelihood of failure. This ensures that critical areas are thoroughly tested, optimizing resource allocation.
- Compatibility Testing:Â Analyze platform configurations, device specifications, and compatibility matrices to identify potential compatibility issues. This helps determine the optimal combinations for testing and ensures the platform functions seamlessly across different devices, operating systems, and browsers.


- Performance Testing:Â Simulate user behavior, generate realistic load patterns, and analyze performance metrics to identify performance bottlenecks and areas of optimization. AI-powered performance testing tools can automatically detect anomalies and provide insights for performance improvement.
- Error Detection and Analysis:Â AI techniques, such as machine learning, can analyze test results, system logs, and other test artifacts to detect patterns and anomalies that may indicate defects. This helps in early defect detection and reduces the time and effort required for debugging.
- Predictive Analytics:Â Leverage historical data and machine learning techniques to predict potential platform-specific issues, performance bottlenecks, or compatibility challenges. This enables proactive testing, optimization, and early mitigation of potential risks.
- Log Analysis and Defect Triage:Â Analyze platform logs, error messages, and other test artifacts to identify relevant information and prioritize defects. AI-powered tools can help automate the defect triage process and improve defect resolution efficiency.
- Test Optimization:Â Optimize the test execution process by identifying redundant or overlapping test cases and removing them. This reduces duplication, improves testing efficiency, and ensures optimal test coverage.
- Natural Language Processing (NLP):Â Understand and process natural language test artifacts, such as requirements and documentation. This improves traceability, enables automatic test case generation, and enhances stakeholder communication.