KPIs also help in identifying areas for improvement, facilitating continuous enhancement of product quality and QA efficiency. By measuring aspects such as defect density, test case coverage, and time to resolution, KPIs offer insights into the product's reliability and the QA team's performance. Furthermore, they serve as a communication tool that aligns stakeholders across different departments, promoting transparency and accountability within the product development lifecycle.
KPI |
Definition
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Business Insights [?]
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Measurement Approach
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Standard Formula
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Accessibility Compliance Rate More Details |
The percentage of product features that comply with accessibility standards, ensuring usability for all users, including those with disabilities.
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Reveals the inclusivity of products and highlights areas for improvement to cater to users with disabilities.
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Measures the percentage of project elements that meet accessibility standards.
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(Number of Accessible Elements / Total Number of Elements) * 100
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- An increasing accessibility compliance rate may indicate a proactive approach to inclusive design and development.
- A decreasing rate could signal neglect of accessibility standards or a lack of awareness about the needs of all users.
- Are there specific product features that consistently fail to meet accessibility standards?
- How does our accessibility compliance rate compare with industry benchmarks or legal requirements?
- Train product teams on accessibility guidelines and best practices for design and development.
- Conduct regular accessibility audits and usability testing with individuals with disabilities.
- Implement accessibility-focused design and development tools and technologies.
Visualization Suggestions [?]
- Line charts showing the trend of accessibility compliance rate over time.
- Stacked bar charts comparing compliance rates across different product features or releases.
- Low accessibility compliance rates can lead to legal liabilities and damage to brand reputation.
- Ignoring accessibility standards can result in exclusion of potential user segments and loss of market opportunities.
- Accessibility testing tools like Axe, WAVE, or JAWS for evaluating product features.
- Integrated development environments (IDEs) with built-in accessibility checkers and validators.
- Integrate accessibility compliance tracking with product development workflows to ensure early detection and resolution of issues.
- Link accessibility compliance data with user feedback and support systems to prioritize improvements based on user impact.
- Improving accessibility compliance can enhance user satisfaction and loyalty, contributing to long-term customer value.
- Conversely, neglecting accessibility standards can lead to user frustration and negative perception of the brand, affecting overall product success.
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Automated Test Coverage More Details |
The proportion of the codebase that is covered by automated testing, reflecting the robustness of quality assurance.
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Assesses the extent to which automated testing is used, indicating potential gaps in testing and areas to reduce manual testing efforts.
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Percentage of source code or software functionalities covered by automated tests.
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(Number of Items Covered by Automated Tests / Total Number of Items to Be Tested) * 100
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- Increasing automated test coverage may indicate a growing emphasis on quality and reliability within the development process.
- Decreasing coverage could signal a lack of focus on quality assurance or challenges in maintaining test suites as the codebase grows.
- Are there specific modules or components with consistently low test coverage?
- How does the automated test coverage align with the frequency and severity of reported bugs or issues?
- Implement code review processes that include test coverage checks to ensure new code is adequately tested.
- Regularly review and update test suites to remove redundant or obsolete tests and add coverage for new features or changes.
- Consider using code analysis and coverage tools to identify areas of the codebase that lack sufficient testing.
Visualization Suggestions [?]
- Line charts showing the trend of automated test coverage over time.
- Stacked bar charts comparing test coverage by module or functional area.
- Low automated test coverage can lead to higher bug counts, longer debugging cycles, and increased risk of releasing faulty software.
- Over-reliance on automated tests without sufficient coverage can create a false sense of security and lead to undetected issues in critical areas.
- Test automation frameworks such as Selenium, Cypress, or JUnit for creating and managing automated tests.
- Code coverage tools like JaCoCo, Istanbul, or Emma to measure the extent of automated test coverage.
- Integrate automated test coverage metrics with continuous integration/continuous deployment (CI/CD) pipelines to enforce coverage thresholds before allowing code to be merged or deployed.
- Link test coverage data with defect tracking systems to prioritize testing efforts based on the impact of uncovered areas.
- Improving automated test coverage can lead to higher initial development effort but ultimately reduce the time and resources spent on debugging and fixing issues.
- Conversely, a decline in test coverage can result in increased maintenance costs and a higher likelihood of delivering subpar products to customers.
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Beta Testing Feedback More Details |
Quantitative and qualitative feedback from beta testers that indicates areas of success and improvement before full-scale release.
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Provides insight into user satisfaction, potential issues, and areas for improvement before the full release.
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The volume and nature of feedback from users during beta testing.
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No standard formula, qualitative in nature.
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- Increasing beta testing feedback may indicate a growing user base or heightened interest in the product.
- Decreasing feedback could signal user disengagement or dissatisfaction with the product.
- Are there specific features or functionalities that consistently receive positive or negative feedback?
- How does the beta testing feedback align with the product roadmap and development goals?
- Implement a structured feedback collection process to ensure all areas of the product are thoroughly tested.
- Provide clear guidelines and prompts for beta testers to encourage detailed and actionable feedback.
- Regularly communicate with beta testers to address their feedback and keep them engaged in the testing process.
Visualization Suggestions [?]
- Line charts showing the trend of overall feedback volume over time.
- Word clouds or sentiment analysis graphs to visualize the qualitative nature of the feedback.
- Ignoring beta testing feedback may result in a product that does not meet user needs or expectations.
- Over-reliance on beta testing feedback without considering other factors may lead to tunnel vision in product development.
- Feedback management platforms like UserVoice or Zendesk to centralize and analyze beta testing feedback.
- User behavior analytics tools to correlate beta testing feedback with actual user interactions and usage patterns.
- Integrate beta testing feedback with product requirement documents to ensure that user input directly influences product development.
- Link beta testing feedback with customer support systems to address reported issues and provide timely solutions.
- Improving beta testing feedback can lead to a more user-centric product but may require additional development resources.
- Ignoring or dismissing beta testing feedback can result in a product that fails to gain traction in the market.
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CORE BENEFITS
- 59 KPIs under Quality Assurance (QA)
- 15,468 total KPIs (and growing)
- 328 total KPI groups
- 75 industry-specific KPI groups
- 12 attributes per KPI
- Full access (no viewing limits or restrictions)
FlevyPro and Stream subscribers also receive access to the KPI Library. You can login to Flevy here.
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Build Stability Index More Details |
The stability of software builds over time, indicated by the success rate of builds passing all tests.
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Reflects the stability and reliability of the build process, indicating the maturity of the development environment.
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The frequency of successful builds versus failed builds.
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(Number of Successful Builds / Total Number of Builds) * 100
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- Increasing build stability index may indicate improvements in the development and testing processes.
- Decreasing stability index could signal issues with code quality, testing coverage, or integration problems.
- Are there specific modules or components that consistently fail tests?
- How does the build stability index compare with industry benchmarks or with previous releases?
- Implement automated testing and continuous integration to catch issues early in the development process.
- Invest in code review processes and tools to improve overall code quality.
- Encourage collaboration between development and QA teams to identify and address common failure points.
Visualization Suggestions [?]
- Line charts showing the trend of build stability index over time.
- Stacked bar charts comparing the success rate of builds by different testing phases.
- Low build stability index can lead to delayed releases and customer dissatisfaction.
- Frequent build failures may indicate deeper issues in the development and testing processes that need to be addressed.
- Continuous integration tools like Jenkins, Travis CI, or CircleCI for automated build and testing processes.
- Code quality and testing tools like SonarQube, JUnit, or Selenium for identifying and addressing issues.
- Integrate build stability index tracking with project management systems to prioritize and address failing components.
- Link with release management systems to ensure that only stable builds are promoted to production environments.
- Improving build stability can lead to faster release cycles and improved customer satisfaction.
- However, investing in additional testing and development resources may increase operational costs.
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Change Impact Awareness More Details |
The awareness of the impact that code changes have on the system, measured by the accuracy of impact assessments.
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Helps in preparing for testing and mitigating risks by understanding the potential effects of changes.
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Tracks the scope of impact caused by changes in the codebase.
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No standard formula, often qualitative or based on a complexity assessment.
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- An increasing accuracy of impact assessments may indicate improved understanding of the system and its dependencies.
- A decreasing accuracy could signal a lack of awareness or thoroughness in assessing the impact of code changes.
- Are impact assessments being conducted consistently for all code changes?
- How are the results of impact assessments being used to inform development and deployment decisions?
- Implement a standardized process for conducting impact assessments for all code changes.
- Provide training and resources for developers to improve their ability to assess the impact of their code changes.
- Establish clear communication channels between development and other teams to gather input for impact assessments.
Visualization Suggestions [?]
- Line charts showing the trend of accuracy in impact assessments over time.
- Stacked bar charts comparing the accuracy of impact assessments for different types of code changes.
- Inaccurate impact assessments can lead to unexpected system failures or disruptions.
- Overly conservative impact assessments may result in slower development and deployment cycles.
- Integrated development environments (IDEs) with built-in impact analysis tools.
- Automated testing and deployment tools that can provide insights into the potential impact of code changes.
- Integrate impact assessment results with project management and release planning tools to inform decision-making.
- Link impact assessment data with incident and problem management systems to track the impact of code changes on system stability.
- Improving the accuracy of impact assessments can lead to more efficient development and deployment processes.
- Conversely, inaccurate impact assessments can result in increased rework and system downtime.
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Code Quality Metrics More Details |
A collection of metrics such as cyclomatic complexity, code duplication, and adherence to coding standards that measure the overall quality of the codebase.
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Provides an overview of the codebase's health and helps maintain high-quality code standards.
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Combines various measures like complexity, maintainability, and coding standards adherence.
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No single standard formula, comprises multiple individual metrics.
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- Increasing cyclomatic complexity may indicate more convoluted code and potential maintenance challenges.
- Rising code duplication could lead to inconsistencies and errors in the codebase.
- Adherence to coding standards trending downwards may result in decreased code quality and readability.
- Are there specific modules or components with consistently high cyclomatic complexity or code duplication?
- How does our codebase adherence to coding standards compare with industry best practices or benchmarks?
- Regularly conduct code reviews and refactoring to address high complexity and duplication.
- Enforce coding standards through automated tools and regular training for developers.
- Implement static code analysis tools to identify and address code quality issues early in the development process.
Visualization Suggestions [?]
- Line charts showing trends in cyclomatic complexity and code duplication over time.
- Bar graphs comparing adherence to coding standards across different modules or teams.
- High cyclomatic complexity and code duplication can lead to increased maintenance efforts and higher chances of introducing bugs.
- Poor adherence to coding standards may result in code that is difficult to understand and maintain.
- Code quality analysis tools like SonarQube or CodeClimate to track and improve code quality metrics.
- Version control systems with built-in code review capabilities such as GitLab or Bitbucket.
- Integrate code quality metrics with continuous integration/continuous deployment (CI/CD) pipelines to catch and address issues early in the development process.
- Link code quality metrics with project management tools to prioritize and track improvements in code quality.
- Improving code quality metrics can lead to more maintainable code and reduced technical debt, but may require additional time and resources for refactoring.
- Poor code quality can impact overall product stability and performance, affecting user experience and customer satisfaction.
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In selecting the most appropriate Quality Assurance (QA) KPIs from our KPI Library for your organizational situation, keep in mind the following guiding principles:
It is also important to remember that the only constant is change—strategies evolve, markets experience disruptions, and organizational environments also change over time. Thus, in an ever-evolving business landscape, what was relevant yesterday may not be today, and this principle applies directly to KPIs. We should follow these guiding principles to ensure our KPIs are maintained properly:
By systematically reviewing and adjusting our Quality Assurance (QA) KPIs, we can ensure that your organization's decision-making is always supported by the most relevant and actionable data, keeping the organization agile and aligned with its evolving strategic objectives.