For instance, KPIs related to code quality, such as bug frequency or mean time to resolution, help ensure that software is robust and reliable. Similarly, KPIs for project management, like sprint velocity or feature delivery timelines, enable teams to optimize their workflows and improve productivity. Ultimately, KPIs act as a navigational tool, aligning the technical objectives with the business goals, ensuring that software delivery is not only swift but also meets the desired standards of quality.
KPI |
Definition
|
Business Insights [?]
|
Measurement Approach
|
Standard Formula
|
Accessibility Compliance Rate More Details |
The percentage of the product that adheres to accessibility standards and guidelines.
|
Indicates how well the product adheres to accessibility standards, which is important for inclusivity and reaching a wider audience.
|
Considers the percentage of product features that meet specified accessibility standards.
|
(Number of Accessible Features / Total Number of Features) * 100
|
- Increasing accessibility compliance rate may indicate a proactive approach to inclusive design and development.
- A decreasing rate could signal neglect of accessibility standards or challenges in implementing accessible features.
- Are there specific features or components that consistently fail accessibility checks?
- How does our accessibility compliance rate compare with industry benchmarks or legal requirements?
- Train development teams on accessibility best practices and guidelines.
- Conduct regular accessibility audits and usability testing with individuals with disabilities.
- Implement automated accessibility testing tools to catch issues early in the development process.
Visualization Suggestions [?]
- Line charts showing the trend of accessibility compliance rate over time.
- Stacked bar charts comparing accessibility compliance across different product features or modules.
- Low accessibility compliance rates can lead to legal liabilities and damage to brand reputation.
- Ignoring accessibility standards can result in exclusion of potential users and customers with disabilities.
- Accessibility testing tools like Axe, WAVE, or pa11y for automated accessibility checks.
- Screen readers and other assistive technologies to experience the product from the perspective of users with disabilities.
- Integrate accessibility compliance tracking with the software development lifecycle to catch and address issues early.
- Link accessibility compliance data with user feedback and support tickets to prioritize accessibility improvements.
- Improving accessibility compliance can enhance user experience and expand the potential user base.
- However, it may require additional resources and time for development and testing.
|
Automated Test Success Rate More Details |
The percentage of automated tests that pass successfully on the first run.
|
Provides insight into the stability and reliability of the software, indicating the quality of the codebase.
|
Measures the percentage of automated tests that pass successfully.
|
(Number of Automated Tests Passed / Total Number of Automated Tests) * 100
|
- An increasing automated test success rate may indicate improvements in code quality or test coverage.
- A decreasing rate could signal issues with test stability, code changes, or environmental factors impacting test execution.
- Are there specific types of tests that consistently fail on the first run?
- How does the automated test success rate compare with industry benchmarks or historical data?
- Regularly review and update automated test scripts to ensure they reflect changes in the application code.
- Invest in infrastructure and tools to minimize environmental variability in test execution.
- Implement a robust test data management strategy to ensure consistent and reliable test results.
Visualization Suggestions [?]
- Line charts showing the trend of automated test success rate over time.
- Pie charts illustrating the distribution of test failures by test type or application module.
- A consistently low automated test success rate can lead to a lack of confidence in the software's quality and reliability.
- High variability in the success rate may indicate instability in the testing environment or infrastructure.
- Test automation frameworks like Selenium or Appium for creating and managing automated test scripts.
- Continuous integration and delivery (CI/CD) tools such as Jenkins or Travis CI to automate test execution and reporting.
- Integrate automated test success rate with build and deployment pipelines to prevent the release of unstable code.
- Link with defect tracking systems to prioritize and address issues identified by failed tests.
- Improving the automated test success rate can lead to faster and more reliable software releases, enhancing overall development efficiency.
- Conversely, a declining success rate may result in increased time and effort spent on debugging and fixing failed tests, impacting project timelines and costs.
|
Backlog Size More Details |
The total number of items waiting to be addressed in the product backlog.
|
Highlights the workload and prioritization effectiveness, indicating potential bottlenecks or resource needs.
|
Tracks the number of items, such as user stories or bugs, waiting to be addressed in the product backlog.
|
Total Number of Backlog Items
|
- An increasing backlog size may indicate a growing number of unresolved issues or a slowdown in development and delivery processes.
- A decreasing backlog size can signal improved efficiency in addressing and resolving items in the product backlog.
- Are there specific types of items that consistently remain in the backlog for extended periods?
- How does the backlog size correlate with the team's capacity and velocity?
- Regularly prioritize and refine the product backlog to ensure that high-value items are addressed promptly.
- Implement agile methodologies to break down large items into smaller, more manageable tasks.
- Allocate dedicated time for backlog grooming and backlog refinement activities.
Visualization Suggestions [?]
- Line charts showing the backlog size over time to identify trends and patterns.
- Stacked bar charts to visualize the distribution of backlog items by priority or type.
- A consistently large backlog may lead to delays in addressing critical issues and delivering value to customers.
- An excessively small backlog may indicate a lack of long-term planning and strategic vision for the product.
- Use project management tools like Jira, Trello, or Asana to track and manage the product backlog effectively.
- Utilize backlog management features in agile development platforms such as Azure DevOps or Rally.
- Integrate backlog size tracking with sprint planning and release management processes to ensure alignment with development efforts.
- Link backlog size with customer feedback and support systems to prioritize items based on user impact and feedback.
- A decreasing backlog size may lead to faster delivery of features and improvements, enhancing customer satisfaction and time-to-market.
- However, a significantly reduced backlog size may also indicate a lack of long-term planning and strategic vision for the product, potentially impacting its competitiveness and sustainability.
|
CORE BENEFITS
- 45 KPIs under Software Engineering and Quality Assurance
- 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.
|
IMPORTANT: 16 days left until the annual price is increased from $99 to $149.
$99/year
Build Stability More Details |
The frequency at which the software build passes all tests without errors or failures.
|
Reveals the health of the software development process, showing how often changes lead to successful deployments.
|
Considers the frequency of successful builds versus failed builds.
|
(Number of Successful Builds / Total Number of Builds) * 100
|
- Build stability may improve over time as development processes mature and testing becomes more robust.
- A declining trend in build stability could indicate increasing complexity in the software, inadequate testing coverage, or issues in the development pipeline.
- Are there specific modules or components that consistently fail tests, and if so, what are the common characteristics?
- How has the frequency of build failures changed over time, and what factors may have contributed to these shifts?
- Implement automated testing and continuous integration to catch errors early in the development process.
- Invest in code review processes and quality gates to ensure that only stable and tested code is included in the build.
- Regularly review and update testing strategies to align with changes in software architecture and functionality.
Visualization Suggestions [?]
- Line charts showing the build stability trend over time.
- Stacked bar charts comparing the frequency of build failures across different modules or components.
- Consistently unstable builds can lead to project delays, increased rework, and decreased team morale.
- Failure to address declining build stability may result in a higher number of production issues and customer dissatisfaction.
- Testing frameworks like JUnit, Selenium, or TestNG for automated unit and integration testing.
- Continuous integration tools such as Jenkins, Travis CI, or CircleCI to automate the build and testing process.
- Integrate build stability metrics with project management systems to track the impact of build issues on project timelines and resource allocation.
- Link build stability data with defect tracking systems to identify patterns and root causes of build failures.
- Improving build stability can lead to faster delivery of features, reduced rework, and increased overall software quality.
- Conversely, declining build stability may result in project delays, increased technical debt, and decreased customer satisfaction.
|
Change Failure Rate More Details |
The percentage of changes to the codebase that result in degraded service or a production failure.
|
Helps to evaluate the risk management and quality of the deployment processes.
|
Measures the percentage of changes that result in failure in production.
|
(Number of Failed Deployments / Total Number of Deployments) * 100
|
- An increasing change failure rate may indicate a decline in code quality or inadequate testing procedures.
- A decreasing rate can signal improvements in the development and testing processes or better change management practices.
- Are there specific types of changes (e.g., bug fixes, feature enhancements) that contribute more to the failure rate?
- How does our change failure rate compare with industry benchmarks or similar organizations?
- Implement more comprehensive testing protocols, including unit tests, integration tests, and end-to-end tests.
- Establish a robust change management process that includes thorough code reviews and risk assessments before deployment.
- Invest in automated deployment and rollback mechanisms to minimize the impact of failed changes.
Visualization Suggestions [?]
- Line charts showing the change failure rate over time to identify trends and patterns.
- Pareto charts to identify the most common types of changes that result in failures.
- High change failure rates can lead to service disruptions, customer dissatisfaction, and increased operational costs.
- Chronic failures may indicate systemic issues in the development and deployment processes that need to be addressed.
- Continuous integration and continuous deployment (CI/CD) tools like Jenkins, Travis CI, or CircleCI to automate testing and deployment processes.
- Error monitoring and logging platforms such as Sentry or Splunk to quickly identify and address issues in production.
- Integrate change failure rate tracking with incident management systems to streamline the response to production failures.
- Link with project management tools to correlate changes with specific development tasks and teams.
- Reducing the change failure rate can lead to improved service reliability, customer satisfaction, and overall operational efficiency.
- However, overly conservative measures to reduce failures may slow down the pace of innovation and development.
|
Code Churn More Details |
The percentage of a codebase that changes over a given period, indicating the stability or volatility of the development effort.
|
Indicates the stability and maturity of the codebase, as well as developer productivity and efficiency.
|
Tracks the number of lines of code added, modified, or deleted over a period.
|
Sum of Added + Modified + Deleted Lines of Code in a Period
|
- Code churn tends to increase during periods of rapid feature development or bug fixing.
- A decreasing code churn may indicate stabilization of the codebase or a shift towards more structured development processes.
- What specific areas of the codebase are experiencing the most frequent changes?
- Are there particular development practices or team dynamics that correlate with higher code churn?
- Implement code review processes to catch and address potential sources of instability early.
- Invest in automated testing and continuous integration to catch regressions and bugs before they impact the codebase.
- Encourage modular and decoupled code architecture to minimize the ripple effects of changes.
Visualization Suggestions [?]
- Line charts showing code churn over time, broken down by modules or components.
- Stacked bar charts comparing code churn across different development teams or projects.
- High code churn can lead to increased technical debt and maintenance costs.
- Excessive volatility in the codebase may indicate a lack of design or architectural stability.
- Version control systems like Git or SVN to track changes and facilitate collaboration.
- Code analysis tools such as SonarQube or CodeClimate to identify areas of the codebase that are prone to frequent changes.
- Integrate code churn data with project management tools to correlate changes with specific development tasks or user stories.
- Link code churn metrics with defect tracking systems to identify potential sources of instability.
- Reducing code churn can lead to more predictable release cycles and improved overall software quality.
- However, overly strict controls on code churn may stifle innovation and slow down development efforts.
|
In selecting the most appropriate Software Engineering and Quality Assurance 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 Software Engineering and Quality Assurance 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.