Batch Regression Testing
Batch
Regression Testing
Automated Testing
Parallet Testing
Test Reports
Automated large-scale testing, true value comparison, batch parallel testing, and automatic evaluation of test reports. With intelligent automation, we ensure both the quality and efficiency of testing.
Automated large-scale testing, true value comparison, batch parallel testing, and automatic evaluation of test reports. With intelligent automation, we ensure both the quality and efficiency of testing.
Automated large-scale testing, true value comparison, batch parallel testing, and automatic evaluation of test reports. With intelligent automation, we ensure both the quality and efficiency of testing.
Customer Challenges
Customer Challenges
Lack of Data-Integrated Regression Testing Tools
Scenario Data Management
Test Case Management and Version Evaluation History
Lack of Data-Integrated Regression Testing Tools
Automation Challenges
No dedicated automated testing tools designed for the robotics and autonomous driving industries.
Concurrency Issues
In-house tools often lack scalability, leading to low testing efficiency.
Lack of an Efficient Testing System
Basic data management exists, but manual testing by engineers is time-consuming.
01
Lack of Data-Integrated Regression Testing Tools
Scenario Data Management
Test Case Management and Version Evaluation History
Lack of Data-Integrated Regression Testing Tools
Automation Challenges
No dedicated automated testing tools designed for the robotics and autonomous driving industries.
Concurrency Issues
In-house tools often lack scalability, leading to low testing efficiency.
Lack of an Efficient Testing System
Basic data management exists, but manual testing by engineers is time-consuming.
01
Ensure Testing Quality and Efficiency
Automated Testing
Automated Reproduction
Testing scripts can automatically reproduce historical data and scenarios across various test environments, adapting to diverse testing needs.
Ground Truth Comparison
Recomputes historical data and compares it with labeled ground truth to ensure algorithm improvements without regressions.
Parallel Testing
Batch Efficiency
Parallel execution significantly reduces testing time while handling massive datasets.
Dynamic Resource Optimization
Dynamically allocates testing resources to enhance parallel execution efficiency and maximize resource utilization.
Test Reports
Automated Evaluation and Analysis
Automation reduces human errors and improves testing accuracy.
Visualized Test Results
Custom visual interfaces provide intuitive displays of key testing metrics.
Real-Time Feedback and Trend Analysis
Delivers real-time test results with trend analysis to facilitate rapid issue identification.
Customer Benefits
Test Data Asset Construction
Build comprehensive test scenario sets to establish a solid foundation for continuous optimization.
Cost Reduction and Efficiency Improvement
Minimize labor and computing resource consumption, achieving efficient data management and testing at a lower cost.
Rapid Delivery
Significantly accelerate testing and product delivery, enhancing market responsiveness.
Enhanced Algorithm Quality
Ensure algorithm stability through rigorous testing, reducing robot failure rates.
Customer Benefits
Test Data Asset Construction
Build comprehensive test scenario sets to establish a solid foundation for continuous optimization.
Cost Reduction and Efficiency Improvement
Minimize labor and computing resource consumption, achieving efficient data management and testing at a lower cost.
Rapid Delivery
Significantly accelerate testing and product delivery, enhancing market responsiveness.
Enhanced Algorithm Quality
Ensure algorithm stability through rigorous testing, reducing robot failure rates.
Customer Benefits
Test Data Asset Construction
Build comprehensive test scenario sets to establish a solid foundation for continuous optimization.
Cost Reduction and Efficiency Improvement
Minimize labor and computing resource consumption, achieving efficient data management and testing at a lower cost.
Rapid Delivery
Significantly accelerate testing and product delivery, enhancing market responsiveness.
Enhanced Algorithm Quality
Ensure algorithm stability through rigorous testing, reducing robot failure rates.
Why Choose coScene？
When it comes to flexibility in data processing and storage, choosing the right deployment approach is crucial. coScene offers multiple deployment options, including fully managed, multi-tenant, single-tenant, hybrid, and on-premises solutions to meet diverse customer needs.
Whether you're a startup, an SME, or a large enterprise, we provide flexible, secure, and reliable deployment solutions.
Why Choose coScene？
When it comes to flexibility in data processing and storage, choosing the right deployment approach is crucial. coScene offers multiple deployment options, including fully managed, multi-tenant, single-tenant, hybrid, and on-premises solutions to meet diverse customer needs.
Whether you're a startup, an SME, or a large enterprise, we provide flexible, secure, and reliable deployment solutions.
Why Choose coScene？
When it comes to flexibility in data processing and storage, choosing the right deployment approach is crucial. coScene offers multiple deployment options, including fully managed, multi-tenant, single-tenant, hybrid, and on-premises solutions to meet diverse customer needs.
Whether you're a startup, an SME, or a large enterprise, we provide flexible, secure, and reliable deployment solutions.
Unlock Data Potential, Build the Data Flywheel
Unlock Data Potential, Build the Data Flywheel
Unlock Data Potential
Customer Challenges
Lack of Data-Integrated Regression Testing Tools
01
Automation Challenges
No dedicated automated testing tools designed for the robotics and autonomous driving industries.
Concurrency Issues
In-house tools often lack scalability, leading to low testing efficiency.
Lack of an Efficient Testing System
Basic data management exists, but manual testing by engineers is time-consuming.
Challenges in Scenario Data Management
02
Insufficient Data Infrastructure
Lack of foundational data infrastructure prevents effective data accumulation. Existing data and testing systems are decoupled from storage and computing, leading to inefficient scheduling and usage. Testing systems lack elasticity and are not cloud-native.
Disorganized Data
Accumulated data exists but cannot be effectively utilized, and historical data is poorly managed.
Challenges in Scenario Data Management
03
Difficult to Track Historical Version Performance
Without version comparisons, identifying critical version issues and rollback points is challenging.
Difficult to Track Test Case Results
Inability to assess software version test coverage makes it hard to determine optimization directions.
Challenging to Identify Trends
Hinders issue localization and resolution, making it difficult to detect long-term problems and proactively warn against emerging issues.
Lack of Data-Integrated Regression Testing Tools
01
Automation Challenges
No dedicated automated testing tools designed for the robotics and autonomous driving industries.
Concurrency Issues
In-house tools often lack scalability, leading to low testing efficiency.
Lack of an Efficient Testing System
Basic data management exists, but manual testing by engineers is time-consuming.
Challenges in Scenario Data Management
02
Insufficient Data Infrastructure
Lack of foundational data infrastructure prevents effective data accumulation. Existing data and testing systems are decoupled from storage and computing, leading to inefficient scheduling and usage. Testing systems lack elasticity and are not cloud-native.
Disorganized Data
Accumulated data exists but cannot be effectively utilized, and historical data is poorly managed.
Challenges in Scenario Data Management
03
Difficult to Track Historical Version Performance
Without version comparisons, identifying critical version issues and rollback points is challenging.
Difficult to Track Test Case Results
Inability to assess software version test coverage makes it hard to determine optimization directions.
Challenging to Identify Trends
Hinders issue localization and resolution, making it difficult to detect long-term problems and proactively warn against emerging issues.
Lack of Data-Integrated Regression Testing Tools
01
Automation Challenges
No dedicated automated testing tools designed for the robotics and autonomous driving industries.
Concurrency Issues
In-house tools often lack scalability, leading to low testing efficiency.
Lack of an Efficient Testing System
Basic data management exists, but manual testing by engineers is time-consuming.
Challenges in Scenario Data Management
02
Insufficient Data Infrastructure
Lack of foundational data infrastructure prevents effective data accumulation. Existing data and testing systems are decoupled from storage and computing, leading to inefficient scheduling and usage. Testing systems lack elasticity and are not cloud-native.
Disorganized Data
Accumulated data exists but cannot be effectively utilized, and historical data is poorly managed.
Challenges in Scenario Data Management
03
Difficult to Track Historical Version Performance
Without version comparisons, identifying critical version issues and rollback points is challenging.
Difficult to Track Test Case Results
Inability to assess software version test coverage makes it hard to determine optimization directions.
Challenging to Identify Trends
Hinders issue localization and resolution, making it difficult to detect long-term problems and proactively warn against emerging issues.
Lack of Data-Integrated Regression Testing Tools
01
Automation Challenges
No dedicated automated testing tools designed for the robotics and autonomous driving industries.
Concurrency Issues
In-house tools often lack scalability, leading to low testing efficiency.
Lack of an Efficient Testing System
Basic data management exists, but manual testing by engineers is time-consuming.
Challenges in Scenario Data Management
02
Insufficient Data Infrastructure
Lack of foundational data infrastructure prevents effective data accumulation. Existing data and testing systems are decoupled from storage and computing, leading to inefficient scheduling and usage. Testing systems lack elasticity and are not cloud-native.
Disorganized Data
Accumulated data exists but cannot be effectively utilized, and historical data is poorly managed.
Challenges in Scenario Data Management
03
Difficult to Track Historical Version Performance
Without version comparisons, identifying critical version issues and rollback points is challenging.
Difficult to Track Test Case Results
Inability to assess software version test coverage makes it hard to determine optimization directions.
Challenging to Identify Trends
Hinders issue localization and resolution, making it difficult to detect long-term problems and proactively warn against emerging issues.
Ensure Testing Quality and Efficiency
Automated Testing
Automated Reproduction
Testing scripts can automatically reproduce historical data and scenarios across various test environments, adapting to diverse testing needs.
Ground Truth Comparison
Recomputes historical data and compares it with labeled ground truth to ensure algorithm improvements without regressions.
Parallel Testing
Batch Efficiency
Parallel execution significantly reduces testing time while handling massive datasets.
Dynamic Resource Optimization
Dynamically allocates testing resources to enhance parallel execution efficiency and maximize resource utilization.
Test Reports
Automated Evaluation and Analysis
Automation reduces human errors and improves testing accuracy.
Visualized Test Results
Custom visual interfaces provide intuitive displays of key testing metrics.
Real-Time Feedback and Trend Analysis
Delivers real-time test results with trend analysis to facilitate rapid issue identification.
Ensure Testing Quality and Efficiency
Automated Testing
Automated Reproduction
Testing scripts can automatically reproduce historical data and scenarios across various test environments, adapting to diverse testing needs.
Ground Truth Comparison
Recomputes historical data and compares it with labeled ground truth to ensure algorithm improvements without regressions.
Parallel Testing
Batch Efficiency
Parallel execution significantly reduces testing time while handling massive datasets.
Dynamic Resource Optimization
Dynamically allocates testing resources to enhance parallel execution efficiency and maximize resource utilization.
Test Reports
Automated Evaluation and Analysis
Automation reduces human errors and improves testing accuracy.
Visualized Test Results
Custom visual interfaces provide intuitive displays of key testing metrics.
Real-Time Feedback and Trend Analysis
Delivers real-time test results with trend analysis to facilitate rapid issue identification.
contact@coscene.io
© 2025 coScene
contact@coscene.io
© 2025 coScene
contact@coscene.io
© 2025 coScene