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Combining Usage and Requirement Coverage for Higher Quality

Coverage requirements

Requirement Coverage in a Nutshell

Requirement coverage is a crucial metric in software testing that gauges the thoroughness of the testing process concerning the specified software requirements. It revolves around the idea of creating and executing test cases to ensure that each individual requirement outlined in the software specifications is thoroughly tested.

The primary objective is to verify that the implemented system aligns with the documented requirements, known as functional correctness. This validation process enhances confidence in the overall reliability and performance of the software.

Several techniques and approaches are used to measure, calculate, and track the requirement coverage to prevent or minimize coverage gaps. These methods help ensure that the testing efforts sufficiently address the defined requirements. 

Here are some common techniques:

  • Requirement Coverage Metrics: Coverage metrics quantify the extent to which requirements have been tested. Metrics can be calculated by dividing the number of tested requirements by the total number of requirements. For example, coverage might be expressed as a percentage, indicating the proportion of requirements covered by testing.
  • Requirement Traceability Matrix (RTM): A Requirement Traceability Matrix is a table that links requirements to corresponding test cases. Each requirement is mapped to one or more test cases, enabling a clear and visual representation of the coverage. The RTM helps track which requirements have associated test cases and vice versa.
  • Model-Based Testing: Model-based testing uses visual models to represent requirements and generate test cases. Models, such as state diagrams or flowcharts, are used to create test scenarios directly from requirements. This ensures that testing aligns closely with the specified functionalities.
Requirement Coverage

Requirement coverage blind spots

Quite frequently, requirements coverage only considers documented functionalities. Disregarding potential edge cases and unexpected user interactions that could lead to undiscovered problems. Additionally, unstated assumptions in the requirements might not be translated into test cases, creating blind spots for potential issues.

Requirements are often subject to interpretation, and their quality may vary. Incomplete, unclear, or changing requirements pose challenges for testing, such as:

  • Ambiguity: Vague or unclear requirements make it difficult to determine what exactly needs to be tested, leading to inaccurate coverage estimates. 
  • Incompleteness: Missing functionalities or user stories in the requirements document will leave corresponding gaps in test coverage.
  • Constant changes:  Requirements change frequently throughout the development cycle. Keeping tests updated to reflect these changes is a continuous effort, making achieving and maintaining full coverage a moving target.
  • Misalignment with user behavior and usage patterns: Written requirements don’t necessarily reflect how users will interact with the software in real-world scenarios. Potentially leading to unexpected issues after deployment.

In light of this, testing teams need to avoid over-reliance on the written requirements as their only way to assess the effectiveness of the test coverage or release readiness. While important, high-requirement coverage shouldn’t be the sole indicator of software quality. 

Embracing usage coverage to improve quality

Usage coverage, unlike requirement coverage, focuses on how real users interact with the software in a production environment. It aims to understand which features, functionalities, and user flows are actually used by the users in real-world scenarios. 

It emphasizes monitoring and extracting insights from the actual ways in which users engage with the software in production. To measure the usage coverage in practice, it is crucial to set up production monitoring. This means putting in place tools to actively watch, and analyze how users behave when interacting with your live application.

You’ll need to work with raw, unstructured data and thoroughly analyze it. The goal is to slice and dice the data to gain insights into how users are engaging with your application. Look for usage patterns, find out which features are used most frequently, and spot trends in important areas.

One of the key objectives of tracking the usage coverage is to bridge the gap between formal requirements and the dynamic reality of real-life users. It recognizes that explicit requirements, even when well-defined, might not capture the full spectrum of user interactions, behaviors, and preferences.

By combining both usage with requirement coverage approaches, teams can enhance test coverage, paving the way for a more comprehensive and user-centric testing process.

Outlined in the list below are the key benefits of adopting the usage coverage approach:

  • High-Impact Area Identification: This method employs meticulous data collection and analysis to pinpoint the high-impact areas within the application. These key areas often encompass frequently accessed features, essential functionalities, and critical user journeys, ensuring a targeted approach to testing.
  • Data-Driven Test Case Selection and Prioritization: By closely aligning test cases with actual user interactions and key scenarios. This approach significantly improves the selection and prioritization of test cases. As a result, it refines test coverage, placing a primary emphasis on critical user journeys.
  • Uncovering Implicit Use Cases: This aids in bringing to light implicit use cases and scenarios that might not have been explicitly documented in the initial requirements, thereby ensuring a more comprehensive test coverage. These undiscovered use cases can lead to critical defects in production.
  • Scenario Diversity: It helps replicating a diverse range of real-world scenarios encountered by users. Including various user personas and their preferences, edge and corner cases, devices, and environmental conditions.

Tracking usage coverage with Gravity

Gravity is a unified platform designed to help testing teams monitor and leverage insights from both production and testing environments, enhancing the efficiency of the test strategy. It consolidates key data and insights into a single solution for easy access and analysis.

Its 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.

Gravity’s ability to monitor production and testing environments allows 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 insights to enable testing teams to spot gaps in coverage, identify features that are either over-tested or under-tested, and recognize redundant testing efforts in less critical areas. 

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. This feature not only reduces the burden of test case creation but also leads to a decrease in maintenance overhead.

Since it relies on real usage data collected from production environments, this enables data-driven test case prioritization, focusing test coverage on high-impact areas that directly affect the end user experience. 

By bridging gaps from requirements with real-world usage insights, Gravity helps in optimizing test coverage for improved efficiency and agility.


The adoption of usage coverage tracking contributes to placing users at the forefront of the testing process. It involves continuous monitoring, analysis, and adaptation to real-world user behaviors in production environment for a more comprehensive testing strategy.

It’s essential to highlight that while usage coverage is valuable, it should not be a substitute for other testing approaches or methods aimed at tracking coverage. Its main goal is to expand test coverage, reveal hidden test scenarios, and amplify the discovery of new types of defects.

Author: Cristiano Caetano Head of Growth at Smartesting

Software testing authority with two decades of expertise in the field. Brazilian native who has called London home for the past six years. I am the proud founder of Zephir Scale, the leading Test Management application in the Atlassian ecosystem. Over the last ten years, my role has been pivotal in guiding testing companies to build and launch innovative testing tools into the market. Currently, I hold the position of Head of Growth at Smartesting, a testing company committed to the development of AI-Powered testing tools.

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