Examples of Quality Metrics for Product Quality Assurance (QA)

Tracking and analyzing quality metrics is an essential part of any Product Quality Assurance (QA) process. These metrics help QA teams measure the effectiveness of their testing efforts, identifying areas for improvement, and ensure the final product meets customer expectations.

This article explores what quality metrics are and review key QA metric types with examples.

What are Quality Metrics?

Quality metrics are measurable values used to assess the effectiveness, efficiency, and overall performance of the quality assurance process. They help QA teams evaluate how well the software meets defined standards, user expectations, and business goals.

By tracking these metrics, teams can identify issues early, monitor progress, and make data-driven decisions to improve product quality throughout the software development lifecycle.

Types of Quality Metrics

Quality metrics can be grouped into several categories based on what they measure. Each type provides insights into different aspects of the QA process:

• Cost-Related Metrics: These metrics measure the financial impact of quality efforts, including the cost of software testing, fixing bugs, and potential losses due to defects.
• Defect Metrics: These metrics focus on identifying and analyzing software defects, helping teams understand their frequency, severit, and impact.
• Test Execution & Efficiency Metrics: These metrics evaluate the effectiveness and productivity of the testing process, including test coverage, execution rates, and review quality.
• Customer-Centric Metrics: These metrics track quality from the end user’s perspective, such as post-release defects and user complaints.

If you’re looking to monitor these metrics seamlessly across your QA lifecycle, BrowserStack QEI provides centralized visibility, real-time dashboards, and actionable insights, empowering teams to track performance, reduce defects, and continuously improve product quality.

Examples of Quality Metrics and KPIs

Quality metrics and key performance indicators (KPIs) provide clear and measurable insights into different areas of the QA process. Below are some common examples, grouped by category:

Cost-Related Metrics

Cost-related metrics help teams understand the financial impact of their QA efforts. They provide visibility into how much is being spent to ensure quality and what it may cost if quality assurance is insufficient. Below are examples of cost-related metrics:

• Cost of Quality (CoQ): The total cost spent on quality-related activities, including prevention, detection and fixing failures. It shows the investment needed to deliver a high-quality product.
• Cost per Bug Fix: Calculates the average cost to detect, diagnose, and fix a single bug. This metric helps estimate the economic efficiency of defect resolution.
• Cost of Not Testing: Represents potential losses; financial or reputational, caused by skipping or underperforming testing. It highlights the risks of releasing untested or poorly tested software.
• Test Cost: Measures the total resources such as time, tools, and personnel, spent on test planning, execution, and reporting. It helps evaluate the return on investment in QA processes.

Defect Metrics

Defect metrics provide insights into the number, distribution, and handling of software defects. These metrics help assess code stability and identify problem areas in the development lifecycle. Below are examples of data metrics:

• Defects per Requirement: Shows how many defects are linked to individual requirements. A high number may indicate unclear or complex requirements.
• Defects per Software Change: Measures how frequently new code changes introduce defects. It helps monitor the impact of updates and code quality.
• Defect Distribution Over Time: Tracks when defects are reported during the project lifecycle. Patterns can indicate areas where testing or development needs improvement.
• Defect Age: Refers to how long a defect remains unresolved. Older defects suggest delays in triaging or fixing bugs.
• Defect Leakage: Measures the number of defects found after a product is released. This indicates gaps in pre-release testing.
• Defect Resolution Percentage: The proportion of reported defects that have been fixed. It reflects the QA team’s ability to address issues efficiently.
• Bugs Found vs. Bugs Fixed: Compares the total number of bugs discovered with how many have been resolved. A large gap may point to resource or process issues.

Test Execution & Efficiency Metrics

These metrics assess how well testing activities are being carried out. They highlight the speed, effectiveness, and reliability of the overall QA process. Below are examples of test execution and efficiency metrics:

• Test Effort: Tracks how much time and how many resources are dedicated to testing. It helps in planning and resource allocation.
• Time to Test: Measures the duration of the testing phase in a development cycle. Shorter test cycles with consistent quality are typically preferred.
• Test Completion Status: Compares the number of tests planned versus those actually executed. This helps measure progress and identify testing gaps.
• Test Execution Status: Breaks down test outcomes into passed, failed, blocked or skipped. It provides real-time view of testing effectiveness.
• Test Case Effectiveness: Evaluates how many defects are detected by test cases. High effectiveness indicates good test design.
• Test Case Productivity: Measures the number of test cases created against the effort spent. It helps determine how efficiently test cases are being written.
• Test Review Efficiency: Assesses the quality and outcome of test and code reviews. It ensures early detection of issues and better team collaboration.
• Test Reliability: Indicates the rate of false positives or negatives in test results. More reliable tests lead to better confidence in releases.

Customer-Centric Metrics

Customer-centric metrics indicate how users experience and respond to the quality of the product after release. These indicators help identify real-world issues and measure the impact on user satisfaction.

• Customer Complaints and Returns: Tracks user-reported issues due to quality problems. A high rate can indicate inadequate testing or overlooked defects.
• Production Defects: The number of defects reported by end-users in the live environment. It highlights the gap between QA testing and actual usage conditions.

Why use BrowserStack to track QA Metrics?

Tracking QA metrics effectively requires more than just collecting data, it demands the right tools to analyze trends, surface insights, and drive smarter decisions across the software development lifecycle.

BrowserStack Quality Engineering Insights (QEI) is purpose-built to help QA teams monitor, manage, and improve product quality at scale.

Here’s why QEI stands out:

• Unified Quality View: Consolidate data from multiple QA, CI/CD, and issue-tracking tools into a single, centralized dashboard for a complete view of product quality.
• Net Quality Score (NQS): Leverage the Net Quality Score (NQS) to assess overall delivery health across key dimensions, coverage, quality, automation, and velocity—in one unified metric.
• Data-Driven Insights: Gain clear, actionable intelligence through “Key Wins” highlighting improvements and “Focus Areas” pinpointing issues requiring attention.
• Customizable Reporting: Configure dashboards to reflect your organization’s priorities, track progress against defined goals, and analyze performance at the team or project level.
• Seamless Integration: Integrate effortlessly with your existing toolchain, ensuring quality metrics remain accurate, consistent, and up to date.

By using BrowserStack QEI, QA leaders gain the visibility they need to reduce risks, streamline processes, and deliver higher-quality software with confidence.

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Conclusion

Tracking the right quality metrics is important for ensuring effective QA processes and delivering a reliable product. By focusing on cost, defect trends, test execution, and user feedback, teams can make informed decisions, identify issues early, and continuously improve product quality.

BrowserStack QEI makes this process seamless by providing real-time visibility into key QA metrics, customizable dashboards, and actionable insights, empowering teams to optimize quality at every stage of the development lifecycle.