Quality Intelligence Powered by AI
Quality Intelligence Powered by AI
Gravity’s primary function is to produce Quality Intelligence by processing the ingested data through machine learning algorithms. This involves translating raw data into meaningful insights using techniques such as pattern recognition, trend and correlation analysis, anomaly and outlier detection, and more. The analytics, insights, and recommendations generated by Gravity aim to improve testing efficiency and coverage, lower maintenance overhead, optimize resource allocation and reduce costs by concentrating testing efforts on the most critical parts of the application based on the frequency of use in production.
In-depth analytics into real-world user behavior in production
Gravity delivers advanced quality analytics obtained from production monitoring, aiming to provide testing teams with visibility into how real-world users utilize the production environment. This understanding facilitates the identification of usage patterns, common user journeys, and frequently accessed features, effectively addressing gaps resulting from potentially incomplete, poorly defined, or ambiguous requirements. By grounding itself in real data, Gravity helps optimize test planning for improved efficiency and agility by eliminating assumptions, guesswork, and human errors.
Comprehensive test gap analysis
Gravity’s ability to monitor production and testing environments enables it to conduct a comprehensive test gap analysis. By comparing the paths taken by real user interactions in live production with the tests executed in testing environments, Gravity generates actionable insights and recommendations powered by AI (Artificial Intelligence). These insights enable testing teams to identify gaps in test coverage, recognize features that are either over-tested or under-tested, and pinpoint redundant testing efforts in less critical areas.
Amplifying Testing Diversity
Gravity empowers teams to replicate a wide range of real-world scenarios encountered by users in live production environments, encompassing diverse personas, behaviors, and preferences. It uncovers not only typical user interactions but also the less common and unique paths users may take, including edge and corner cases that are often overlooked, potentially due to gaps in written requirements.
Test Case Generation
Gravity utilizes pattern recognition and AI (Artificial Intelligence) to automatically generate test cases for areas lacking test coverage, whether they are manual tests or automated scripts for test automation tools like Cypress, Playwright, and others. It assists in extending the coverage of regression tests for crucial end-to-end user journeys. This feature not only reduces the burden of test case creation but also results in a decrease in costly and time-consuming maintenance overhead.
Test Optimization & Prioritization
Gravity offers an AI-Powered test case Weighting & Scoring engine that helps optimize existing test suites by prioritizing test cases based on their frequency of use in the tested areas covered by these test cases. This enables a data-driven test case prioritization, focusing test coverage on high-impact areas that directly affect the end-user experience. This also reduces test execution time by helping concentrating and prioritizing efforts on critical areas, enabling faster feedback and accelerating software delivery.
Compatibility Coverage Tracking
Gravity continuously monitors production usage, tracking the broad spectrum of configurations utilized by real-world users such as various operating systems, browsers, and devices. This data is then compared with tests executed in testing environments to create a Compatibility Matrix. It empowers testing teams to prioritize their efforts toward the most commonly used configurations, identify emerging trends, and pinpoint any gaps in compatibility coverage with precision.