Attention all software developers and quality assurance professionals!
Are you looking for a comprehensive tool to improve your code coverage and ensure the highest quality of your products? Look no further than our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit!
With 1501 prioritized requirements, our kit provides you with the knowledge base to ask the most important questions and get results by urgency and scope.
Our dataset includes solutions, benefits, and real-world examples, making it the most detailed and useful tool on the market.
But how does our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit compare to competitors and alternatives? Simply put, it outshines them all.
Our product is designed specifically for professionals like you, with a user-friendly interface and easy-to-use features.
It′s the perfect DIY and affordable alternative to expensive and complex options.
Our product is also suitable for businesses of all sizes, making it a must-have for any company looking to improve their code coverage and overall product quality.
And at a cost that won′t break the bank, it′s a no-brainer investment for your business.
So what exactly does our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit do? It helps you accurately measure and track your code coverage, identify potential areas for improvement, and ultimately improve the quality of your code.
With our product, you′ll save time and effort while ensuring the best possible outcome for your projects.
Don′t just take our word for it - do your own research on the benefits and effectiveness of our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit.
We are confident that you′ll see the value and advantages it offers compared to other similar products.
Upgrade your code coverage game today with our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit.
Don′t miss out on this essential tool for professionals like you.
Order now and see the results for yourself!
How does gcov handle synchronization primitives such as mutexes, locks, and condition variables in a multi-threaded application, and what impact do these have on the accuracy of code coverage data collection? Can gcov be used to analyze code coverage for code that incorporates GPUs or other specialized hardware for accelerating computer vision and image processing tasks, and what are the requirements for instrumentation and data collection in such scenarios? Can gcov be used with compilers that generate code for non-native architectures, such as cross-compilers, and if so, what additional considerations or modifications are required to ensure accurate code coverage data collection?
Coverage Data Collection
Gcov ignores synchronization primitives, potentially leading to inaccurate code coverage data due to thread interference and synchronization effects.
Here are the solutions and their benefits:
**Solutions:**
1. Gcov uses thread-safe counters to collect coverage data.
2. Gcov uses atomic operations to update counters.
3. Gcov instruments synchronization primitives to account for thread interactions.
**Benefits:**
1. Ensures accurate coverage data despite thread interleaving.
2. Prevents counter corruption due to concurrent updates.
3. Provides comprehensive coverage data for multi-threaded code.
CONTROL QUESTION: How does gcov handle synchronization primitives such as mutexes, locks, and condition variables in a multi-threaded application, and what impact do these have on the accuracy of code coverage data collection?
Big Hairy Audacious Goal (BHAG) for 10 years from now: What a great question!
**Big Hairy Audacious Goal (BHAG) for 10 years from now:**
**Unified Code Coverage Analysis for Multi-Threaded Systems**
By 2033, develop a Coverage Data Collection framework that can accurately measure code coverage for complex, multi-threaded applications, including those utilizing various synchronization primitives such as mutexes, locks, and condition variables, with an error margin of less than 1% for 95% of all use cases, thereby empowering developers to confidently optimize and debug their concurrent systems.
**Why this goal is important:**
1. **Increasing complexity**: As software systems become more parallel and distributed, understanding the behavior of multi-threaded code is crucial for ensuring reliability, performance, and security.
2. **Growing importance of concurrency**: With the proliferation of multi-core processors and distributed systems, concurrent programming is becoming the norm, making it essential to develop tools that can efficiently analyze and optimize such systems.
3. **Current limitations**: Existing coverage analysis tools, including gcov, struggle to accurately measure code coverage in multi-threaded applications, leading to incomplete or misleading data that can hinder debugging and optimization efforts.
**Key Challenges to Address:**
1. **Inter-thread interference**: Developing a framework that can accurately capture the interactions between threads and synchronization primitives, without introducing artificial dependencies or race conditions.
2. **Thread scheduling variability**: Handling the variability in thread scheduling, which can affect the accuracy of coverage data, by incorporating scheduling-aware analysis techniques.
3. **Complexity of synchronization primitives**: Supporting a wide range of synchronization primitives, including custom implementations, and understanding their impact on code coverage data.
4. **Scalability and performance**: Ensuring the framework can handle large, complex codebases and high-traffic systems without introducing significant performance overhead.
**Potential Approaches:**
1. **Instrumentation-based analysis**: Developing instrumentation techniques that can accurately capture the execution path of each thread, including synchronization events, without introducing significant performance overhead.
2. **Static analysis integration**: Integrating static analysis techniques to identify potential synchronization issues and optimize instrumentation placement.
3. **Dynamic analysis of synchronization primitives**: Developing dynamic analysis techniques to understand the behavior of synchronization primitives and their impact on code coverage data.
4. **AI-assisted coverage analysis**: Utilizing machine learning and AI techniques to improve the accuracy of coverage analysis and identify potential issues in multi-threaded systems.
**Key Milestones:**
1. **Year 2-3**: Develop a basic framework for analyzing code coverage in simple multi-threaded applications, with an error margin of 5% or less.
2. **Year 4-5**: Expand the framework to support common synchronization primitives, such as mutexes and locks, with an error margin of 2% or less.
3. **Year 6-7**: Integrate support for more complex synchronization primitives, such as condition variables, and reduce the error margin to 1% or less.
4. **Year 8-10**: Achieve the BHAG by developing a comprehensive, unified code coverage analysis framework for multi-threaded systems, with an error margin of less than 1% for 95% of all use cases.
By achieving this BHAG, the Coverage Data Collection community will have taken a significant step towards empowering developers to create more reliable, efficient, and secure concurrent systems.
"Since using this dataset, my customers are finding the products they need faster and are more likely to buy them. My average order value has increased significantly."
**Client Situation:**
Our client, a leading software development company, specializes in creating high-performance, multi-threaded applications for various industries. As part of their testing strategy, they rely on code coverage analysis to ensure comprehensive testing of their software. However, they faced challenges in collecting accurate code coverage data in their multi-threaded applications, particularly when it came to synchronization primitives such as mutexes, locks, and condition variables.
**Consulting Methodology:**
To address our client′s concerns, we employed a comprehensive consulting methodology that involved:
1. ** literature review**: We conducted an in-depth review of academic papers, market research reports, and consulting whitepapers to gain a deep understanding of code coverage analysis in multi-threaded applications.
2. **system analysis**: We analyzed our client′s system architecture, identifying the synchronization primitives used in their multi-threaded applications and how they impacted code coverage data collection.
3. **gcov configuration and customization**: We configured and customized gcov, a popular code coverage analysis tool, to suit our client′s specific needs and system architecture.
4. **data collection and analysis**: We collected code coverage data using gcov and analyzed the results to identify areas of concern and opportunities for improvement.
5. **reporting and recommendations**: We presented our findings and recommendations to our client, highlighting the impact of synchronization primitives on code coverage data collection and providing guidance on how to improve accuracy.
**Deliverables:**
Our consulting methodology yielded the following deliverables:
1. A comprehensive report detailing the impact of synchronization primitives on code coverage data collection in our client′s multi-threaded applications.
2. A customized gcov configuration tailored to our client′s system architecture and synchronization primitives.
3. A detailed analysis of code coverage data, highlighting areas of concern and opportunities for improvement.
4. Recommendations for improving the accuracy of code coverage data collection, including best practices for using synchronization primitives in multi-threaded applications.
**Implementation Challenges:**
During our consulting engagement, we encountered several implementation challenges, including:
1. **Thread safety**: Ensuring that gcov was thread-safe and could accurately collect code coverage data in a multi-threaded environment.
2. **Synchronization primitive interference**: Managing the interference between synchronization primitives and gcov′s data collection mechanisms.
3. **Code complexity**: Navigating the complexities of our client′s codebase, including intricate synchronization logic and multiple threads.
**KPIs:**
To measure the success of our consulting engagement, we tracked the following key performance indicators (KPIs):
1. **Code coverage accuracy**: The percentage of code coverage data that accurately reflected the behavior of our client′s multi-threaded application.
2. **Data collection efficiency**: The time and resources required to collect code coverage data using gcov.
3. **Testing effectiveness**: The number of defects detected and fixed as a result of improved code coverage data collection.
**Management Considerations:**
In managing this consulting engagement, we considered the following factors:
1. **Stakeholder buy-in**: Ensuring that all stakeholders, including developers, quality assurance teams, and management, were aligned with our methodology and objectives.
2. **Resource allocation**: Allocating sufficient resources, including personnel and infrastructure, to support our consulting engagement.
3. **Communication**: Maintaining open and transparent communication with our client throughout the engagement, ensuring that all parties were informed of progress and any challenges encountered.
**References:**
1. **Code Coverage Analysis in Multi-Threaded Applications** by J. Singh et al. (Journal of Software Engineering Research and Development, 2019)
2. **Synchronization Primitives in Multi-Threaded Applications: A Survey** by A. Kumar et al. (ACM Computing Surveys, 2020)
3. **Improving Code Coverage Analysis in Multi-Threaded Applications using gcov** by G. Li et al. ( IEEE Transactions on Software Engineering, 2018)
4. **Best Practices for Code Coverage Analysis in Multi-Threaded Applications** by K. Lee et al. (Consulting Whitepaper, 2020)
5. **Market Research Report: Code Coverage Analysis Tools** by MarketsandMarkets (2020)
By employing a comprehensive consulting methodology and addressing the unique challenges of code coverage data collection in multi-threaded applications, we were able to deliver accurate and actionable insights to our client, improving the effectiveness of their testing strategy and the overall quality of their software.
Are you looking for a comprehensive tool to improve your code coverage and ensure the highest quality of your products? Look no further than our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit!
With 1501 prioritized requirements, our kit provides you with the knowledge base to ask the most important questions and get results by urgency and scope.
Our dataset includes solutions, benefits, and real-world examples, making it the most detailed and useful tool on the market.
But how does our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit compare to competitors and alternatives? Simply put, it outshines them all.
Our product is designed specifically for professionals like you, with a user-friendly interface and easy-to-use features.
It′s the perfect DIY and affordable alternative to expensive and complex options.
Our product is also suitable for businesses of all sizes, making it a must-have for any company looking to improve their code coverage and overall product quality.
And at a cost that won′t break the bank, it′s a no-brainer investment for your business.
So what exactly does our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit do? It helps you accurately measure and track your code coverage, identify potential areas for improvement, and ultimately improve the quality of your code.
With our product, you′ll save time and effort while ensuring the best possible outcome for your projects.
Don′t just take our word for it - do your own research on the benefits and effectiveness of our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit.
We are confident that you′ll see the value and advantages it offers compared to other similar products.
Upgrade your code coverage game today with our Coverage Data Collection and Code Coverage Tool; The gcov Tool Qualification Kit.
Don′t miss out on this essential tool for professionals like you.
Order now and see the results for yourself!
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1501 prioritized Coverage Data Collection requirements. - Extensive coverage of 104 Coverage Data Collection topic scopes.
- In-depth analysis of 104 Coverage Data Collection step-by-step solutions, benefits, BHAGs.
- Detailed examination of 104 Coverage Data Collection case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Gcov User Feedback, Gcov Integration APIs, Code Coverage In Integration Testing, Risk Based Testing, Code Coverage Tool; The gcov Tool Qualification Kit, Code Coverage Standards, Gcov Integration With IDE, Gcov Integration With Jenkins, Tool Usage Guidelines, Code Coverage Importance In Testing, Behavior Driven Development, System Testing Methodologies, Gcov Test Coverage Analysis, Test Data Management Tools, Graphical User Interface, Qualification Kit Purpose, Code Coverage In Agile Testing, Test Case Development, Gcov Tool Features, Code Coverage In Agile, Code Coverage Reporting Tools, Gcov Data Analysis, IDE Integration Tools, Condition Coverage Metrics, Code Execution Paths, Gcov Features And Benefits, Gcov Output Analysis, Gcov Data Visualization, Class Coverage Metrics, Testing KPI Metrics, Code Coverage In Continuous Integration, Gcov Data Mining, Gcov Tool Roadmap, Code Coverage In DevOps, Code Coverage Analysis, Gcov Tool Customization, Gcov Performance Optimization, Continuous Integration Pipelines, Code Coverage Thresholds, Coverage Data Filtering, Resource Utilization Analysis, Gcov GUI Components, Gcov Data Visualization Best Practices, Code Coverage Adoption, Test Data Management, Test Data Validation, Code Coverage In Behavior Driven Development, Gcov Code Review Process, Line Coverage Metrics, Code Complexity Metrics, Gcov Configuration Options, Function Coverage Metrics, Code Coverage Metrics Interpretation, Code Review Process, Code Coverage Research, Performance Bottleneck Detection, Code Coverage Importance, Gcov Command Line Options, Method Coverage Metrics, Coverage Data Collection, Automated Testing Workflows, Industry Compliance Regulations, Integration Testing Tools, Code Coverage Certification, Testing Coverage Metrics, Gcov Tool Limitations, Code Coverage Goals, Data File Analysis, Test Data Quality Metrics, Code Coverage In System Testing, Test Data Quality Control, Test Case Execution, Compiler Integration, Code Coverage Best Practices, Code Instrumentation Techniques, Command Line Interface, Code Coverage Support, User Manuals And Guides, Gcov Integration Plugins, Gcov Report Customization, Code Coverage Goals Setting, Test Environment Setup, Gcov Data Mining Techniques, Test Process Improvement, Software Testing Techniques, Gcov Report Generation, Decision Coverage Metrics, Code Optimization Techniques, Code Coverage In Software Testing Life Cycle, Code Coverage Dashboards, Test Case Prioritization, Code Quality Metrics, Gcov Data Visualization Tools, Code Coverage Training, Code Coverage Metrics Calculation, Regulatory Compliance Requirements, Custom Coverage Metrics, Code Coverage Metrics Analysis, Code Coverage In Unit Testing, Code Coverage Trends, Gcov Output Formats, Gcov Data Analysis Techniques, Code Coverage Standards Compliance, Code Coverage Best Practices Framework
Coverage Data Collection Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Coverage Data Collection
Gcov ignores synchronization primitives, potentially leading to inaccurate code coverage data due to thread interference and synchronization effects.
Here are the solutions and their benefits:
**Solutions:**
1. Gcov uses thread-safe counters to collect coverage data.
2. Gcov uses atomic operations to update counters.
3. Gcov instruments synchronization primitives to account for thread interactions.
**Benefits:**
1. Ensures accurate coverage data despite thread interleaving.
2. Prevents counter corruption due to concurrent updates.
3. Provides comprehensive coverage data for multi-threaded code.
CONTROL QUESTION: How does gcov handle synchronization primitives such as mutexes, locks, and condition variables in a multi-threaded application, and what impact do these have on the accuracy of code coverage data collection?
Big Hairy Audacious Goal (BHAG) for 10 years from now: What a great question!
**Big Hairy Audacious Goal (BHAG) for 10 years from now:**
**Unified Code Coverage Analysis for Multi-Threaded Systems**
By 2033, develop a Coverage Data Collection framework that can accurately measure code coverage for complex, multi-threaded applications, including those utilizing various synchronization primitives such as mutexes, locks, and condition variables, with an error margin of less than 1% for 95% of all use cases, thereby empowering developers to confidently optimize and debug their concurrent systems.
**Why this goal is important:**
1. **Increasing complexity**: As software systems become more parallel and distributed, understanding the behavior of multi-threaded code is crucial for ensuring reliability, performance, and security.
2. **Growing importance of concurrency**: With the proliferation of multi-core processors and distributed systems, concurrent programming is becoming the norm, making it essential to develop tools that can efficiently analyze and optimize such systems.
3. **Current limitations**: Existing coverage analysis tools, including gcov, struggle to accurately measure code coverage in multi-threaded applications, leading to incomplete or misleading data that can hinder debugging and optimization efforts.
**Key Challenges to Address:**
1. **Inter-thread interference**: Developing a framework that can accurately capture the interactions between threads and synchronization primitives, without introducing artificial dependencies or race conditions.
2. **Thread scheduling variability**: Handling the variability in thread scheduling, which can affect the accuracy of coverage data, by incorporating scheduling-aware analysis techniques.
3. **Complexity of synchronization primitives**: Supporting a wide range of synchronization primitives, including custom implementations, and understanding their impact on code coverage data.
4. **Scalability and performance**: Ensuring the framework can handle large, complex codebases and high-traffic systems without introducing significant performance overhead.
**Potential Approaches:**
1. **Instrumentation-based analysis**: Developing instrumentation techniques that can accurately capture the execution path of each thread, including synchronization events, without introducing significant performance overhead.
2. **Static analysis integration**: Integrating static analysis techniques to identify potential synchronization issues and optimize instrumentation placement.
3. **Dynamic analysis of synchronization primitives**: Developing dynamic analysis techniques to understand the behavior of synchronization primitives and their impact on code coverage data.
4. **AI-assisted coverage analysis**: Utilizing machine learning and AI techniques to improve the accuracy of coverage analysis and identify potential issues in multi-threaded systems.
**Key Milestones:**
1. **Year 2-3**: Develop a basic framework for analyzing code coverage in simple multi-threaded applications, with an error margin of 5% or less.
2. **Year 4-5**: Expand the framework to support common synchronization primitives, such as mutexes and locks, with an error margin of 2% or less.
3. **Year 6-7**: Integrate support for more complex synchronization primitives, such as condition variables, and reduce the error margin to 1% or less.
4. **Year 8-10**: Achieve the BHAG by developing a comprehensive, unified code coverage analysis framework for multi-threaded systems, with an error margin of less than 1% for 95% of all use cases.
By achieving this BHAG, the Coverage Data Collection community will have taken a significant step towards empowering developers to create more reliable, efficient, and secure concurrent systems.
Customer Testimonials:
"Since using this dataset, my customers are finding the products they need faster and are more likely to buy them. My average order value has increased significantly."
"As a business owner, I was drowning in data. This dataset provided me with actionable insights and prioritized recommendations that I could implement immediately. It`s given me a clear direction for growth."
"I can`t imagine working on my projects without this dataset. The prioritized recommendations are spot-on, and the ease of integration into existing systems is a huge plus. Highly satisfied with my purchase!"
Coverage Data Collection Case Study/Use Case example - How to use:
**Case Study: Coverage Data Collection in a Multi-Threaded Application****Client Situation:**
Our client, a leading software development company, specializes in creating high-performance, multi-threaded applications for various industries. As part of their testing strategy, they rely on code coverage analysis to ensure comprehensive testing of their software. However, they faced challenges in collecting accurate code coverage data in their multi-threaded applications, particularly when it came to synchronization primitives such as mutexes, locks, and condition variables.
**Consulting Methodology:**
To address our client′s concerns, we employed a comprehensive consulting methodology that involved:
1. ** literature review**: We conducted an in-depth review of academic papers, market research reports, and consulting whitepapers to gain a deep understanding of code coverage analysis in multi-threaded applications.
2. **system analysis**: We analyzed our client′s system architecture, identifying the synchronization primitives used in their multi-threaded applications and how they impacted code coverage data collection.
3. **gcov configuration and customization**: We configured and customized gcov, a popular code coverage analysis tool, to suit our client′s specific needs and system architecture.
4. **data collection and analysis**: We collected code coverage data using gcov and analyzed the results to identify areas of concern and opportunities for improvement.
5. **reporting and recommendations**: We presented our findings and recommendations to our client, highlighting the impact of synchronization primitives on code coverage data collection and providing guidance on how to improve accuracy.
**Deliverables:**
Our consulting methodology yielded the following deliverables:
1. A comprehensive report detailing the impact of synchronization primitives on code coverage data collection in our client′s multi-threaded applications.
2. A customized gcov configuration tailored to our client′s system architecture and synchronization primitives.
3. A detailed analysis of code coverage data, highlighting areas of concern and opportunities for improvement.
4. Recommendations for improving the accuracy of code coverage data collection, including best practices for using synchronization primitives in multi-threaded applications.
**Implementation Challenges:**
During our consulting engagement, we encountered several implementation challenges, including:
1. **Thread safety**: Ensuring that gcov was thread-safe and could accurately collect code coverage data in a multi-threaded environment.
2. **Synchronization primitive interference**: Managing the interference between synchronization primitives and gcov′s data collection mechanisms.
3. **Code complexity**: Navigating the complexities of our client′s codebase, including intricate synchronization logic and multiple threads.
**KPIs:**
To measure the success of our consulting engagement, we tracked the following key performance indicators (KPIs):
1. **Code coverage accuracy**: The percentage of code coverage data that accurately reflected the behavior of our client′s multi-threaded application.
2. **Data collection efficiency**: The time and resources required to collect code coverage data using gcov.
3. **Testing effectiveness**: The number of defects detected and fixed as a result of improved code coverage data collection.
**Management Considerations:**
In managing this consulting engagement, we considered the following factors:
1. **Stakeholder buy-in**: Ensuring that all stakeholders, including developers, quality assurance teams, and management, were aligned with our methodology and objectives.
2. **Resource allocation**: Allocating sufficient resources, including personnel and infrastructure, to support our consulting engagement.
3. **Communication**: Maintaining open and transparent communication with our client throughout the engagement, ensuring that all parties were informed of progress and any challenges encountered.
**References:**
1. **Code Coverage Analysis in Multi-Threaded Applications** by J. Singh et al. (Journal of Software Engineering Research and Development, 2019)
2. **Synchronization Primitives in Multi-Threaded Applications: A Survey** by A. Kumar et al. (ACM Computing Surveys, 2020)
3. **Improving Code Coverage Analysis in Multi-Threaded Applications using gcov** by G. Li et al. ( IEEE Transactions on Software Engineering, 2018)
4. **Best Practices for Code Coverage Analysis in Multi-Threaded Applications** by K. Lee et al. (Consulting Whitepaper, 2020)
5. **Market Research Report: Code Coverage Analysis Tools** by MarketsandMarkets (2020)
By employing a comprehensive consulting methodology and addressing the unique challenges of code coverage data collection in multi-threaded applications, we were able to deliver accurate and actionable insights to our client, improving the effectiveness of their testing strategy and the overall quality of their software.
Security and Trust:
- Secure checkout with SSL encryption Visa, Mastercard, Apple Pay, Google Pay, Stripe, Paypal
- Money-back guarantee for 30 days
- Our team is available 24/7 to assist you - support@theartofservice.com
About the Authors: Unleashing Excellence: The Mastery of Service Accredited by the Scientific Community
Immerse yourself in the pinnacle of operational wisdom through The Art of Service`s Excellence, now distinguished with esteemed accreditation from the scientific community. With an impressive 1000+ citations, The Art of Service stands as a beacon of reliability and authority in the field.Our dedication to excellence is highlighted by meticulous scrutiny and validation from the scientific community, evidenced by the 1000+ citations spanning various disciplines. Each citation attests to the profound impact and scholarly recognition of The Art of Service`s contributions.
Embark on a journey of unparalleled expertise, fortified by a wealth of research and acknowledgment from scholars globally. Join the community that not only recognizes but endorses the brilliance encapsulated in The Art of Service`s Excellence. Enhance your understanding, strategy, and implementation with a resource acknowledged and embraced by the scientific community.
Embrace excellence. Embrace The Art of Service.
Your trust in us aligns you with prestigious company; boasting over 1000 academic citations, our work ranks in the top 1% of the most cited globally. Explore our scholarly contributions at: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=blokdyk
About The Art of Service:
Our clients seek confidence in making risk management and compliance decisions based on accurate data. However, navigating compliance can be complex, and sometimes, the unknowns are even more challenging.
We empathize with the frustrations of senior executives and business owners after decades in the industry. That`s why The Art of Service has developed Self-Assessment and implementation tools, trusted by over 100,000 professionals worldwide, empowering you to take control of your compliance assessments. With over 1000 academic citations, our work stands in the top 1% of the most cited globally, reflecting our commitment to helping businesses thrive.
Founders:
Gerard Blokdyk
LinkedIn: https://www.linkedin.com/in/gerardblokdijk/
Ivanka Menken
LinkedIn: https://www.linkedin.com/in/ivankamenken/
