The QA Manager's Power Play
Unleashing KPIs & Data to Become an Indispensable Leader
Table of content
Introduction
- Translate QA into business: Aligning the work of the testing team with stakeholders’ goals.
- Become a data-driven leader: Leverage KPIs to make informed decisions, optimize processes, and track progress.
- Gain management buy-in: Showcase the ROI of your QA efforts and secure the resources needed for a high-performing team.
- Elevate your career: Position yourself as a strategic leader who drives business success and fosters a culture of quality.
It’s time to embrace the power of data and propel your QA career to new heights!
Objective 1:
Enhance QA Team Efficiency for Faster Delivery and Higher Quality
Goal:
Reduce Testing Cycles by X%
Increase Bug Detection by Y%
Target Audience:
QA Director, QA Manager, QA Team Lead, QA Team
Overview: In this objective, we spotlight our testing team to optimize QA performance. By improving our QA team’s efficiency, we aim to complete testing cycles faster, identify severe bugs earlier, allocate time effectively, and ultimately release higher-quality software with confidence
Business Contribution:
- Faster Time-to-Market: With shorter yet effective testing cycles, companies can deliver new features and prioritize critical bug fixes, giving you a competitive edge.
- Improved Software Quality: Early identification and resolution of defects lead to higher-quality releases, minimizing the chances of escaping defects and enhancing customer satisfaction.
- Resource Optimization: Efficient QA teams allocate resources and time better, focus on high-risk areas, reduce unnecessary tests, and utilize automation effectively.
KPI 1.1
KPI 1.2
KPI 1.3
KPI 1.4
KPI 1.1:
Planned Vs Executed TESTING PROGRESS
Formula:
Planned vs Executed Tests = (Number of Executed Tests / Number of Planned Tests) * 100%
Planned vs Executed Test Runs
Desired Outcome:
Aim for a high percentage of executed tests relative to planned tests to ensure comprehensive test coverage. Typically, a target of 90% or higher is considered optimal.
Example:
- Planned = 100
- Executed = 95
- Review Test Planning: Ensure test cases are well-defined and prioritized based on risks and project requirements.
- Optimize Resource Allocation: Distribute tests strategically among team members based on their skills and the tests' importance.
- Strengthen Communication: Foster open communication among team members and stakeholders. Also, implement feedback sessions to learn from each testing cycle and allow staff members to suggest improvements.
KPI 1.2:
Rejected Defects Percentage
Impact: A high Rejected Defects Percentage indicates potential misalignment or communication gaps between QA and development. This can lead to wasted time and resources for both teams and potentially missed real defects.
Formula:
Rejected Defects Percentage = (Number of Rejected Defects / Total Number of Reported Defects) * 100%
Defects by status
Desired Outcome:
Aim for a low Rejected Defects Percentage. A common target is less than 10%.
Example:
- Total Number of Defects = 100
- Rejected Defects = 12
Rejected Defects Percentage = (12 / 100) * 100% = 12%
Result: A score of 12% indicates a higher-than-desired rejection rate. This suggests potential issues with defect reporting or misalignment between QA and development.
- Defect Reporting Criteria: Ensure that testers understand the criteria for defect reporting and provide clear guidelines for documenting issues.
- Improve Communication: Improve communication for better alignment between QA and development teams to clarify requirements, expectations, and defect resolution processes.
- Implement Review Process: Consider establishing a review process for reported defects. This could involve a senior QA member or a dedicated role verifying the reproducibility and severity of issues before they reach development.
- Improve the understanding in specific problematic areas: High defect rejection rates in specific product areas require investigation. We can improve by focusing on better team understanding, more reliable test oracles, or clearer communication.
KPI 1.3:
Automated Test Coverage
Impact: Tracking Automated Test Coverage evaluates the efficiency of automation and identifies areas to improve testing and reduce manual efforts.
Formula:
Automated Test Coverage = (Number of Automated Test Cases / Total Number of Test Cases) * 100%
tests by category
Desired Outcome:
The goal will depend on the organization, team, and software being tested. Although a high automation percentage represents higher efficiency, it’s important to remember that 100% automation is not an achievable goal, as there are some tests that automation can not replace human judgment.
Example:
- Total Number of Test Cases = 2000
- Number of Automated Test Cases = 1600
Automated Test Coverage = (1600 / 2000) * 100% = 80%
Result: A score of 80% indicates a strong level of test automation coverage. This allows the QA team to focus their manual testing efforts on more complex areas or exploratory testing, maximizing overall efficiency.
- 1. Identify Automation Opportunities: Analyze areas of the application that currently rely on manual testing and assess their suitability for automation.
- 2. Continuous Automation Maintainable: Develop and maintain high-quality, well-documented automated test scripts. Regularly review and update them to ensure they remain aligned with evolving application functionality.
KPI 1.4:
Test effectiveness
Definition: Automated Test Coverage measures the percentage of test cases covered by automated tests compared to the total number of test cases. It helps assess the efficiency of test automation and reduces the manual testing burden.
Formula:
Test Effectiveness = (Number of Defects Detected / Total Number of Test Cases Executed) * 100%
Desired Outcome:
Aim for a high Test Effectiveness percentage. While it may be difficult to reach 100%, try to get as close as possible.
Example:
- Executed Test = 250
- Defects = 40
Test Effectiveness = (40 / 250) * 100% = 16%
Result: A score of 16% indicates that 16% of executed test cases identified defects.
- 1. Review and Refine Test Cases: Regularly evaluate and refine your test cases to ensure they cover a wide range of scenarios, including edge cases and potential user behavior.
- 2. Combine Exploratory Testing: Use exploratory testing sessions as part of your testing efforts. This allows your QA team to identify unexpected defects and uncover areas that traditional test cases might miss.
Objective 2:
Increase Cross-Team Efficiency, collaboration, and coordination
Goal:
Increase velocity by X%
Increase bugs detected earlier by Y%
Target Audience:
QA Director, QA Manager, QA Team Lead, QA Team
Overview: This objective aims to foster seamless collaboration between QA, Development, and Product. By enhancing cross-team efficiency and minimizing misalignments, defects can be identified earlier, ensuring higher-quality software delivery to end-users.
- Early Issue Detection: Aligning teams with quality standards enables early bug detection during unit testing, reducing the risk of critical bugs slipping into later stages and resolve issues more quickly, reducing the time to market.
- Enhanced Communication and Collaboration: Better communication and collaboration foster a deeper understanding of requirements, minimizing misunderstandings and delays.
- Increased Velocity: Helping to release more functionality faster to the field.
KPI 2.1
KPI 2.2
KPI 2.3
KPI 2.4
KPI 2.5
KPI 2.1:
Shift-left testing percentage
Definition: Shift-Left Testing Percentage measures the portion of your testing activities conducted in early development stages, such as unit testing, integration testing, and nightly build testing.
- Planned = 100
- Executed = 95
Impact: A high Test Effectiveness percentage indicates well-designed test cases that catch defects early, leading to higher-quality releases and fewer post-release issues.
Formula:
Shift-Left Testing Percentage =
(Early Stage Tests / Total Tests) * 100%
tests by test Type
Desired Outcome:
A higher Shift-Left Testing Percentage reflects a more proactive QA strategy. Note that industry benchmarks can vary significantly, 15%-30% on average.
Example:
- Total Number of Defects = 100
- Rejected Defects = 12
Rejected Defects Percentage = (12 / 100) * 100% = 12%
- 1. Use Test-Driven Development (TDD): Encourage developers to write automated unit tests before coding. This ensures code meets requirements, is testable from the start, and catches defects early.
- 2. Implement Continuous Integration (CI): Integrate automated testing into the early development process. Run automated tests on every code change to identify defects as soon as possible.
- 3. Invest in Early Test Automation: Prioritize automating tests for critical functionalities and user journeys. Automating tests early reduces manual testing effort later, allowing testers to focus on exploratory testing and complex scenarios.
KPI 2.2:
Internal defect leakage
Formula:
Internal Defect Leakage = (Defects Missed in Early Stages / Total Number of Defects) * 100%
Desired Outcome:
A higher Shift-Left Testing Percentage reflects a more proactive QA strategy. Note that industry benchmarks can vary significantly, 15%-30% on average.
Example:
- Total Number of Defects: 100
- Defects Missed in Early Stages: 10 (should have been found in unit testing)
Calculation:
Internal Defect Leakage = (Defects Missed in Early Stages / Total Number of Defects) * 100% = (10 / 100) * 100% = 10%
Result: This project has a 10% Internal Defect Leakage rate. This means 10% of the total defects bypassed early detection and were identified by the QA team, highlighting potential areas for improvement in unit testing.
- 1. Feedback Loop: Conduct regular feedback loops between QA and development teams. Share insights on missed unit testing defects, reasons for slippage, and strategies for improved test coverage in future iterations.
- 2. Enhance Unit Testing Practices: Regularly review and improve automation scripts, utilize code coverage tools, write high-quality unit tests, and train developers on best practices for unit test design and development.
- 3. Promote Shift-Left Testing: Increase collaboration between QA and developers for early defect identification, prioritize core functionality test automation, and investigate the feasibility of implementing test-driven development (TDD).
- 4. Enhance Defect Tracking and Reporting: Standardize defect classification for unit testing misses, analyze defect leakage trends, provide actionable feedback to developers with clear descriptions, steps to reproduce, and potential root causes, and encourage developer participation in the defect resolution process.
KPI 2.3:
defect density
Formula:
Defect density
| 1SHOWSTOPPER | 2HIGH | 3NORMAL | 4LOW | TOTAL | |
|---|---|---|---|---|---|
| Application server | 0 | 5 | 5 | 1 | 11 |
| Client Side | 0 | 2 | 4 | 2 | 8 |
| Database Server | 0 | 1 | 0 | 0 | 1 |
| Plugins | 1 | 2 | 1 | 2 | 6 |
| Web Client | 0 | 0 | 3 | 1 | 4 |
| TOTAL | 1 | 10 | 13 | 6 | 30 |
Example:
In a project, if a specific module has a high number of defects despite minimal recent changes, this module should be reviewed for underlying issues.
Conversely, if defects are concentrated in modules that have undergone significant changes during the sprint, this aligns with expected results and indicates focused testing.
- 1. Comprehensive Code Reviews: Implement thorough code reviews involving senior developers to identify and address potential defects early in the development process.
- 2. Targeted Training: Based on defect analysis, provide targeted training to developers on areas where knowledge gaps or coding practices might be contributing to defects.
- 3. Pair Programming: Encourage pair programming sessions, where two developers work together on a single task. This allows for knowledge sharing and real-time identification of potential issues.
- 4. Improved Risk Assessment: Prioritize testing of unstable areas, regardless of current development activity.
KPI 2.4:
risk management efficiency
Definition: Risk Management Efficiency measures how effectively the team evaluates high-risk areas with critical defects in their risk assessment process. This metric is closely related to Defect Density.
Formula:
Risk Management Efficiency = Severe Bugs Found in High-Risk Areas/ Total Severe Bugs * 100%
Risk Assessment
Desired Outcome:
A high Risk Management Efficiency percentage indicates a well-defined risk assessment process. Ideally, aim for a score above 80%, signifying effective identification and prioritization of critical defects.
Example:
- High-Risk Areas Identified = 5
- Critical Defects in Identified High-risk Areas = 20
- Total Critical Defects = 40
Calculation:
Risk Management Efficiency = (20 / 40) * 100% = 50%
- 1. Enhance Risk Identification: Analyze past defects to identify high-risk areas. Conduct brainstorming sessions with developers and QA testers to pinpoint potential risks, and use threat modeling to assess security vulnerabilities.
- 2. Refine Risk Prioritization: Establish criteria for evaluating and prioritizing risks based on severity, likelihood, and impact. Allocate more resources to high-risk areas to catch severe bugs early, rather than focusing on minor defects during broader testing.
KPI 2.5:
Bug Reopen rate
Formula:
Bug Reopen Rate = (Number of Times Bugs Were Reopened / Total Number of Bugs Resolved) * 100%
Desired Outcome:
Aim for a Bug Reopen Rate as close to zero as possible. Aim for a rate below 5%, signifying a high level of effectiveness in resolving defects and minimizing rework.
Example:
- Total Number of Bugs Resolved: 100
- Number of Times Bugs Were Reopened: 10
Bug Reopen Rate = (10 / 100) * 100% = 10%
Result: This project has a Bug Reopen Rate of 10%, which indicates a need for improvement in the bug-fixing process to reduce rework and ensure long-term resolution.
- 1. Enhance Risk Identification: Analyze past defects to identify high-risk areas. Conduct brainstorming sessions with developers and QA testers to pinpoint potential risks, and use threat modeling to assess security vulnerabilities.
- 2. Refine Risk Prioritization: Establish criteria for evaluating and prioritizing risks based on severity, likelihood, and impact. Allocate more resources to high-risk areas to catch severe bugs early, rather than focusing on minor defects during broader testing.
Objective 3:
Maximize the Business Impact of the QA team
Goal:
Increase customer satisfaction by X%
Increase usage of feature/product by X%
Target Audience:
Leadership Team, Business Stakeholders, QA Director, VP R&D, Product Manager
Overview: This objective emphasizes the strategic role of QA in maximizing business impact. By aligning testing efforts with customer satisfaction, we ensure a high-quality user experience that translates to increased customer retention and loyalty. This contributes directly to achieving the business’s strategic goals.
- Higher-Quality Product: QA directly influences the quality of the final product that is released to our customers
- Customer Satisfaction: High-quality products will increase customer satisfaction, resulting in fewer customer complaints and higher customer retention, ultimately contributing to the business's reputation and growth.
KPI 3.1
KPI 3.2
KPI 3.3
KPI 3.4
KPI 3.1:
ESCAPING DEFECTS RATE
- 1. Severity Focus: The primary focus should be on addressing severe defects reported by end-users that were missed during QA.
- 2. Intentional Non-Fix: Include defects reported by the QA team but intentionally left unfixed before release. This helps in analyzing the decision-making process regarding which bugs to fix before release.
- 3. Bug Categorization: Differentiate between bugs reported that will be fixed in future releases and those that are high-impact and require immediate hotfixes.
Impact: A high Escaping Defects Rate reveals SDLC weaknesses, leading to poor user experience and increased support costs. Reducing this rate ensures a higher quality product and boosts customer satisfaction.
Formula:
Escaping Defects Rate = (Number of Defects Found in Production / Total Number of Defects) × 100%
Escaping Defect rate
| 1SHOWSTOPPER | 2HIGH | 3NORMAL | 4LOW | TOTAL | |
|---|---|---|---|---|---|
| Product A | 5 | 2 | 2 | 1 | 10 |
| Product B | 0 | 6 | 12 | 4 | 22 |
| Product C | 0 | 1 | 4 | 1 | 6 |
| Total | 1 | 10 | 13 | 6 | 30 |
Desired Outcome: Achieving zero Escaping Defects is impractical. Instead, focus on minimizing severe defects that evade detection. Aim to keep escaped severe defects under 5% for a strong SDLC emphasizing critical issues and user experience quality. Note, acceptable levels of escaping defects vary by industry and product.
Example:
- Total Defects Found: 100 (pre-release and post-release)
- Post-Release Defects Found: 20
- Critical (showstoppers): 4
- Previously Identified by QA: 8
Calculation:
- Total Escaping Defects = (20 / 100) * 100% Escaping Defects = 20%
- Critical Escaping Defects (4 / 20) * 100% = 20%
- Known Defects = (8 / 20) * 100% = 40%
- Escaping Defects Rate: 20% of defects were found after release, indicating areas for improvement in pre-release testing.
- Severity: 20% of escaping defects are critical (showstoppers), highlighting the potential impact of missed issues.
- Previously Identified Defects: 40% (8 out of 20) of escaping defects were previously known by QA, suggesting potential gaps in resolution or prioritization.
Actionable Guidance:
- 1. Enhance Pre-Release Testing: Implement more rigorous pre-release testing processes, including regression testing and exploratory testing, to catch more defects before they reach production.
- 2. Root Cause Analysis: Perform a root cause analysis on defects found in production to identify why they were missed during QA and improve future testing processes.
- 3. Continuous Improvement: Foster a culture of continuous improvement within the QA team, encouraging regular reviews of testing practices and updates to testing strategies based on feedback and new learnings.
- 4. Improved Customer Advocacy: Especially around previously identified defects, understand if we need to improve the criteria by which we decide whether to fix a found bug or defer it to a future version
KPI 3.2:
User Story IMpact
Formula:
User Story Impact = (Post-Release Metric – Baseline Metric) / Baseline Metric * 100%
Desired Outcome:
Strive to demonstrate a positive impact on user experience. This can be measured through a quantifiable increase in relevant user actions associated with the user story. Ideally, this increase should be aligned with the specific goals outlined for the user story during its creation.
Example:
- Baseline metric= 1000 Actions
- Post Release = 1,300 Actions
Calculation:
User Story Impact = (1300 actions – 1000 actions) / 1000 actions * 100% = 30%
Result: The User Story Impact for this feature is 30%, indicating a positive influence on user engagement in the targeted product area. This increase aligns with the intended outcome of the feature.
- 1. Align Team Understanding: Align product managers, developers, and testers on the feature's purpose, intended user behavior, and alignment with product goals.
- 2. Establish Clear baseline Metrics: Establish metrics aligned with the user story's objectives to measure its impact effectively.
- 3. Gather User Feedback: Gather user feedback post-release to pinpoint improvement areas and enhance user satisfaction. Implement changes based on insights gained to optimize the user experience.
- 4. Continuous Monitoring: Continuously track user interactions with the feature to gauge its long-term impact and identify opportunities for refinement. Use data-driven insights to inform future iterations.
KPI 3.3:
Post-release support Tickets
Formula:
Post-Release Support Tickets = (Benchmark – Actual) / Benchmark * 100%
Post Release support tickets
Desired Outcome:
The optimal result is a decrease in support tickets after a version release. However, maintaining ticket numbers around the defined benchmark is also acceptable.
Example:
- Benchmark = 100 tickets (2 Weeks Post Release)
- Actual= 150 tickets (2 Weeks Post Release)
Result: A 50% increase in support tickets indicates significant issues with the release that need to be investigated and resolved.
- 1. Focus on User Journeys: Map key user journeys and prioritize testing efforts based on user behavior data to ensure comprehensive coverage of core functionalities and frequently used features.
- 2. Analyze Customer Feedback: Establish communication channels for customers to report issues. Regularly review and analyze these tickets to enhance testing efforts and prevent similar issues in future releases.
- 3. Track Impact: After implementing changes and improvements, monitor the percentage decrease in customer-reported defects to assess the effectiveness of actions taken and ensure no new issues arise.
KPI 3.4:
Net Promoter score
- Promoters (9-10): Highly satisfied customers likely to recommend the product.
- Passives (7-8): Neutral customers who might not be vocal advocates.
- Detractors (0-6): Dissatisfied customers who could potentially harm the brand’s reputation.
Formula:
NPS = % Promoters – % Detractors
how likely are you to recommend us to a friend?
NPS = Promoters (25%) – Detractors (60%)
Desired Outcome:
The NPS scale ranges from -100 to 100. Aim for an NPS above 0 to indicate more positive responses than negative. Ideally, strive for a score above 40.
Example:
- Promoters: 50
- Detractors: 120
Calculation:
- % Promoters = (50 / 200) * 100 = 25%
- % Detractors = (120 / 200) * 100 = 60%
- NPS = 25 – 60 = -35
Result: The NPS for this survey is -35, indicating that there are more detractors than promoters. This suggests a need to address customer concerns and improve product quality to enhance customer satisfaction.
- 1. Customer Feedback Analysis: Regularly review customer feedback to identify common pain points and areas for improvement.
- 2. Enhance User Experience Based on Feedback: Implement changes in your product based on feedback to improve the user experience and customer satisfaction.
- 3. Proactive Engagement: Engage with detractors to understand their issues and turn their experiences around, while also maintaining strong relationships with promoters to ensure continued satisfaction.
Conclusion:
- Enhance QA Team Efficiency: Streamlined testing cycles, early bug detection, and enhanced automation will lead to more efficient QA operations, ultimately delivering higher-quality software.
- Improved Cross-Team Efficiency: Strengthened communication and collaboration between QA, Development, and Product teams will minimize errors, reduce bottlenecks, and create a more cohesive workflow, ensuring that quality is embedded throughout the development process.
- Maximize Business Impact: By closely monitoring customer feedback and product performance through targeted KPIs, we can ensure that our software meets and exceeds user expectations, leading to greater customer satisfaction and loyalty.
By adhering to these objectives and KPIs, you can ensure our QA processes are aligned with business objectives and will make a meaningful impact on product quality and customer satisfaction, ultimately contributing to organizational success.
QA Manager’s KPI cheat sheet
| OBJECTIVE | KPI | DEFINITION | FORMULA | DESIRED OUTCOME |
|---|---|---|---|---|
| ENHANCE QA TEAM EFFICIENCY | Planned vs Executed Testing Progress | Measures the alignment between planned and executed tests | (Number of Executed Tests / Number of Planned Tests) * 100% | Higher than 90% |
| Rejected Defects Percentage | Measures the percentage of defects reported by the QA team that are rejected by the development team | (Number of Rejected Defects / Total Number of Reported Defects) * 100% | Less than 10% | |
| Automated Test Coverage | Measures the percentage of test cases covered by automated tests | (Number of Automated Test Cases / Total Number of Test Cases) * 100% | It depends on the organization, team, and software being tested | |
| Test Effectiveness | Measures the ability of test cases to uncover defects in the software | (Number of Defects Detected / Total Number of Test Cases Executed) * 100% | As close as possible to 100% |
QA Manager’s KPI cheat sheet
| OBJECTIVE | KPI | DEFINITION | FORMULA | DESIRED OUTCOME |
|---|---|---|---|---|
| INCREASE CROSS-TEAM EFFICIENCY, COLLABORATION, AND COORDINATION | Shift-Left Testing Percentage | Measures the percentage of tests executed in early phases of development | (Early Stage Tests / Total Tests) * 100% | Between 15%-30% |
| Internal Defect Leakage | Measures the number of defects detected during QA that should have been found in early tests. | (Defects Missed in Early Stages / Total Number of Defects) * 100% | Below 5% | |
| Defect Density | Measures the concentration of defect within a software element (module, Unit, feature, etc.) | Heat map of Defect by categories | Areas with few changes but many issues, should be flagged as problematic. | |
| Risk Management Efficiency | Measures the effectiveness of identifying and addressing high-risk areas in the software. | (Severe Bugs found in High Risk Areas / Total Number of Severe Bugs) * 100% | Aim for a score above 80% | |
| Bug Reopen Rate | Measures the percentage of bugs that are reopened after being marked as fixed. | (Number of Times Bugs Were Reopened / Total Number of Bugs Resolved) * 100% | 5% or below |
QA Manager’s KPI cheat sheet
| OBJECTIVE | KPI | DEFINITION | FORMULA | DESIRED OUTCOME |
|---|---|---|---|---|
| MAXIMIZE THE BUSINESS IMPACT OF THE QA TEAM | Escaping Defects Rate | Measures the percentage of defects that escape the QA process and are found in production. | (Number of Defects Found in Production / Total Number of Defects) * 100% | Under 5% of escaped severe defects |
| User Story Impact | Measures the effect of a user story on specific areas/features of the product after release. | (Post-Release Metric - Baseline Metric) / Baseline Metric * 100% | Increase in relevant user actions associated with the user story | |
| Post-Release Support Tickets | Measures the decrease in customer-reported defects after the QA team's involvement. | (Benchmark - Actual) / Benchmark * 100% | At least 20% reduction in reported tickets | |
| Net Promoter Score | Measures customer satisfaction and likelihood to recommend the product. | % Promoters - % Detractors | Above 0, and seek to increase to around 40. |
About practitest
PractiTest is an end-to-end test management platform designed to simplify complex and robust testing processes. PractiTest centralizes all your QA work, teams, and tools into one platform to bridge silos, unify communication, and enable one source of truth across your organization. With PractiTest you can make informed data-driven decisions based on end-to-end visibility provided by customizable reports, real-time dashboards, and dynamic filter views.