End-to-end end-to-end stress testing and performance analysis methods

Abstract

This invention discloses an end-to-end full-link stress testing and performance analysis method, specifically relating to the field of end-to-end full-link stress testing technology. It includes a test planning module for collecting user requirements, designing test scenarios, and providing scenario templates. The test planning module is electrically connected to a user simulation module for performing behavioral simulation based on user profiles to improve the realism of the test. Through the collaboration of these modules, this invention achieves a deep understanding of user needs, optimizes load and request management, and monitors system performance in real time, thereby ensuring the system maintains high efficiency under various loads to improve user experience. Furthermore, the combination of real-time error detection and security testing effectively reduces system failures and security risks, ensuring the system's health. The cloud collaboration and analysis reporting functions promote information sharing within and outside the team, enhancing transparency.

Description

Technical Field

[0001] This invention relates to the field of end-to-end end-to-end stress testing technology, and in particular to end-to-end end-to-end stress testing and performance analysis methods. Background Technology

[0002] With the rapid advancement of information technology, the complexity of various applications and services is constantly increasing, leading to an increasingly diversified trend in system architecture. In this context, ensuring system performance and stability has become a critical challenge for enterprises, especially when facing scenarios with high-concurrency user access and big data processing. Issues such as single points of failure, performance bottlenecks, and low resource utilization often have a serious negative impact on user experience and the continuous operation of business. As the number of users increases, many enterprises' original system architectures are easily overwhelmed when facing high-concurrency access, resulting in prolonged response times and decreased service quality. If the system architecture fails to implement reasonable load balancing or scaling, some nodes may become overloaded and crash, causing single points of failure. This not only prevents some users from accessing the service but may also affect the experience of a large number of users in a short period of time, thereby damaging the reputation of the enterprise's brand. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies. When faced with scenarios involving high-concurrency user access and big data processing, issues such as single-point failures, performance bottlenecks, and low resource utilization often have a serious negative impact on user experience and the continuous operation of business. As the number of users increases, many enterprises find their original system architecture under pressure when facing high-concurrency access, leading to extended response times and decreased service quality. Therefore, this invention proposes an end-to-end full-link stress testing and performance analysis method.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: An end-to-end full-link stress testing and performance analysis method includes a test planning module for collecting user requirements, designing test scenarios, and providing scenario templates. The test planning module is electrically connected to a user simulation module for simulating behavior based on user profiles to improve the realism of the test. The user simulation module is electrically connected to a load generation module for dynamically adjusting the number of virtual users and request frequency based on real-time monitoring data. The load generation module is electrically connected to a performance monitoring module for integrating real-time resource monitoring and historical data comparison analysis to provide real-time performance status feedback. The performance monitoring module is electrically connected to an error detection module for real-time monitoring of request success rate and error type, providing error classification reports. The error detection module is electrically connected to an analysis report module for supporting intelligent visualization interfaces and diverse report formats. The analysis report module is electrically connected to a security testing module for simulating various attack modes to evaluate the system's security performance. The security testing module is electrically connected to a cloud collaboration module for allowing multiple users to share test results in real-time and supporting version control. The test planning module is electrically connected to a request management module for requesting authorization from the user simulation module and setting parameters to improve the realism of the request. The request management module is electrically connected to a load determination module for determining whether the load generation module can handle the current load. The load determination module is electrically connected to a performance determination module for determining whether the performance monitoring module meets business requirements. The performance determination module is electrically connected to a data collection module for collecting detection data from the error detection module. The data collection module is electrically connected to a result verification module for verifying the accuracy of the data. The result verification module is electrically connected to a result backtracking module for backtracking and analyzing historical test data to identify trends and performance bottlenecks. The result backtracking module is electrically connected to a cross-region module for supporting cloud collaboration modules.

[0005] In one preferred embodiment, the load generation module continuously optimizes the load generation strategy through machine learning algorithms to adapt to environmental changes, and the performance monitoring module can automatically adjust performance monitoring indicators and dynamically change them according to different testing stages.

[0006] In a preferred embodiment, the error detection module uses artificial intelligence algorithms to perform fault mode analysis, thereby improving fault diagnosis efficiency. The cross-region module supports intelligent adjustments based on geographical location and current network latency.

[0007] As a preferred implementation, the result backtracking module provides timeline-based visualization analysis to help users identify trends and anomalies, and the cloud collaboration module supports cross-team collaboration and allows users to set different access permissions.

[0008] In one preferred embodiment, the security testing module includes multiple security testing mechanisms for DDoS, SQL injection, and cross-site scripting. The result verification module can be integrated with external verification tools to improve the verifiability and reliability of the system results. The user behavior simulation module supports API-based dynamic behavior analysis.

[0009] In one preferred implementation, the test planning module collects user requirements, designs test scenarios, and provides scenario templates. The user simulation module constructs realistic user request behaviors based on collected user data. The request management module constructs dynamic requests and sets parameters. The load generation module dynamically adjusts the number of virtual users and request frequency based on real-time monitoring data. The performance monitoring module monitors the system's runtime performance indicators and compares them with historical data. In the load assessment module's determination mechanism, real-time performance indicators are compared with historical benchmarks to determine whether the system can withstand the current load. In the performance assessment module's mechanism, performance indicators are monitored in real-time and compared with set performance thresholds to determine whether the system meets business requirements.

[0010] In a preferred embodiment, the error detection module monitors the success rate and error type of requests in real time and generates an error classification report. The data collection module collects test results and system logs and supports storage in multiple data formats. The analysis report module generates a visual report of the test results, offers diverse report formats, and provides intelligent analysis suggestions.

[0011] In a preferred embodiment, the result verification module executes an automated test verification process to ensure the reliability of the test results, and the cloud collaboration module facilitates real-time sharing of test results among multiple users and supports version control and permission management.

[0012] In a preferred implementation, the security testing module simulates various attack modes to evaluate the security performance of the system, and the result backtracking module performs backtracking analysis on historical test data to identify system trends and performance bottlenecks.

[0013] As a preferred implementation, the cross-regional load distribution module intelligently distributes load in the cloud environment to optimize resource utilization.

[0014] The beneficial effects of this invention are as follows: This invention enables a deep understanding of user needs through the collaboration of various modules, optimizes load and request management, and monitors system performance in real time. This ensures that the system maintains high efficiency under various loads, thereby improving user experience. In addition, the combination of real-time error detection and security testing effectively reduces system failures and security risks, ensuring the system's health. The cloud collaboration and analysis reporting functions promote information sharing within and outside the team, enhancing transparency. Through automatically generated reports and intelligent analysis suggestions, the team can quickly identify problems and make effective decisions, thereby accelerating continuous optimization and process adjustment. The linkage between the results backtracking and performance monitoring modules allows the team to uncover trends and bottlenecks based on historical data, providing a solid basis for long-term system improvement and achieving refined management. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the end-to-end full-link stress testing and performance analysis method in this invention. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0017] like Figure 1 As shown, the end-to-end full-link stress testing and performance analysis method includes a test planning module for collecting user requirements, designing test scenarios, and providing scenario templates. The test planning module is electrically connected to a user simulation module for simulating behavior based on user profiles to improve the realism of the test. The user simulation module is electrically connected to a load generation module for dynamically adjusting the number of virtual users and request frequency based on real-time monitoring data. The load generation module is electrically connected to a performance monitoring module for integrating real-time resource monitoring and historical data comparison and analysis to provide real-time performance status feedback. The performance monitoring module is electrically connected to an error detection module for real-time monitoring of request success rate and error type and providing error classification reports. The error detection module is electrically connected to an analysis report module for supporting intelligent visualization interfaces and diverse report formats. The analysis report module is electrically connected to a security testing module for simulating various attack modes to evaluate the security performance of the system. The security testing module is electrically connected to a cloud collaboration module for allowing multiple users to share test results in real time and supporting version control. The test planning module is electrically connected to a request management module for requesting authorization from the user simulation module and setting parameters to improve the realism of the request. The request management module is electrically connected to a load determination module for determining whether the load generation module can handle the current load. The load determination module is electrically connected to a performance determination module for determining whether the performance monitoring module meets business requirements. The performance determination module is electrically connected to a data collection module for collecting detection data from the error detection module. The data collection module is electrically connected to a result verification module for verifying the accuracy of the data. The result verification module is electrically connected to a result backtracking module for backtracking and analyzing test history data to identify trends and performance bottlenecks. The result backtracking module is electrically connected to a cross-region module for supporting cloud collaboration modules. Through the above embodiments, by collecting user needs and designing test scenarios, the relevance and effectiveness of testing are improved, ensuring that the results reflect the true expectations of users. Behavioral simulation based on user profiles enhances the realism and accuracy of testing, better simulating the operating habits of different users. Dynamically adjusting the number of virtual users and request frequency through real-time data monitoring enables flexible responses to changing demands, thereby improving system stability. Integrating real-time resource monitoring with historical data analysis allows for timely understanding of system performance status, providing a scientific basis for optimization decisions. Real-time monitoring of request success rates and error types ensures continuous monitoring of system health, guaranteeing rapid response to potential problems. Generating diverse report formats and intelligent analysis suggestions helps... This system helps users at different levels better understand test results and facilitates decision-making. By simulating various attack modes, it comprehensively evaluates and enhances the security performance of the system, thereby reducing potential risks. By supporting real-time sharing of test results and version control among multiple users, it improves team collaboration efficiency and enhances information transparency. Through dynamic request building and parameterized settings, it achieves more granular control over requests to ensure the stability of tests in complex environments. Through automated test verification processes, it ensures the reliability of test results, reduces decision-making risks, and provides credibility support for subsequent system optimization. Through retrospective analysis of test historical data, it has the ability to identify long-term trends and performance bottlenecks, helping to quickly adjust optimization strategies. Through intelligent load distribution, it optimizes resource utilization, thereby improving system processing capacity and response speed. The load generation module continuously optimizes the load generation strategy through machine learning algorithms to adapt to environmental changes, and the performance monitoring module can automatically adjust performance monitoring indicators and dynamically change them according to different test stages. The error detection module uses artificial intelligence algorithms to analyze fault modes, improving fault diagnosis efficiency; the cross-region module supports intelligent adjustments based on geographical location and current network latency. The results backtracking module provides timeline-based visualization analysis to help users identify trends and anomalies, while the cloud collaboration module supports cross-team collaboration and allows users to set different access permissions. The security testing module includes various security testing mechanisms such as DDoS, SQL injection, and cross-site scripting. The result verification module can be integrated with external verification tools to improve the verifiability and reliability of system results. The user behavior simulation module supports dynamic behavior analysis based on APIs. The test planning module collects user requirements, designs test scenarios, and provides scenario templates. The user simulation module constructs realistic user request behaviors based on collected user data. The request management module constructs dynamic requests and sets parameters. The load generation module dynamically adjusts the number of virtual users and request frequency based on real-time monitoring data. The performance monitoring module monitors the system's runtime performance indicators and compares them with historical data. The load assessment module's judgment mechanism compares real-time performance indicators with historical benchmarks to determine whether the system can withstand the current load. The performance assessment module's mechanism monitors performance indicators in real time and compares them with set performance thresholds to determine whether the system meets business requirements. The error detection module monitors the success rate and error type of requests in real time and generates error classification reports. The data collection module collects test results and system logs and supports storage in multiple data formats. The analysis report module generates visual reports of test results, offers diverse report formats, and provides intelligent analysis suggestions. The results verification module executes an automated test verification process to ensure the reliability of test results. The cloud collaboration module facilitates real-time sharing of test results among multiple users and supports version control and permission management. In the security testing module, various attack modes are simulated to evaluate the security performance of the system. In the results backtracking module, historical test data is backtracked and analyzed to identify system trends and performance bottlenecks. The cross-region load balancing module intelligently distributes load in the cloud environment to optimize resource utilization. Through the above embodiments, the combination of the test planning module and the user simulation module can accurately reflect user needs in real-world scenarios, improving user experience. The collaboration between the load generation module and the performance monitoring module allows the system to automatically adjust the load to adapt to the current performance state, thus maintaining stability under high load. The linkage between the error detection module and the result verification module enables rapid problem location and verification of repair effectiveness, thereby reducing system failure and recovery time. The combination of the cloud collaboration module and the analysis reporting module achieves real-time sharing of test results and diversified reporting, making data sharing within and outside the team more efficient and transparent. The security testing module and the performance monitoring module work together to monitor the system's security status in real time during stress testing, thereby improving overall security. The combination of robust performance protection capabilities, result backtracking modules, and performance monitoring modules enables the team to identify long-term trends and performance bottlenecks, providing strong support for subsequent system optimization strategies. The collaboration between the request management module and the load generation module allows for flexible handling of complex load scenarios, enabling more refined performance testing. The linkage between the error detection module and the analysis report module, through automated generation of error classification reports, enables rapid troubleshooting and improves the efficiency of fault analysis. The cooperation between the cross-region module and the load generation module can optimize resource allocation in different geographical locations, improving the system's global responsiveness and processing speed. Testing data sharing and analysis between various modules enables intelligent analysis suggestions to support decision-makers in making timely optimization and improvement measures, enhancing the scientific nature and timeliness of decision-making. Through the collaboration of various modules, we can gain a deep understanding of user needs, optimize load and request management, and monitor system performance in real time. This ensures that the system can maintain high efficiency under various loads, thereby improving user experience. In addition, the combination of real-time error detection and security testing effectively reduces system failures and security risks, ensuring the health of the system. The cloud collaboration and analysis reporting functions promote information sharing within and outside the team and enhance transparency. Through automatically generated reports and intelligent analysis suggestions, the team can quickly identify problems and make effective decisions, thereby accelerating continuous optimization and process adjustment. The linkage between the results backtracking and performance monitoring modules enables the team to dig out trends and bottlenecks based on historical data, providing a solid basis for long-term system improvement and achieving refined management. Working Principle: In use, this invention first collects user requirements through a test planning module. These requirements may involve performance, load, request type, security requirements, etc. Based on the collected requirements, the test planning module designs test scenarios and provides scenario templates for users to choose from. Users can select appropriate templates according to their actual situation. The user simulation module uses user profiles to simulate user behavior based on user requirements and historical behavior data, thereby improving the realism of the test. The design of test scenarios can reflect the system's load by simulating real user behavior, thus constructing more realistic test scenarios. The request management module generates dynamic requests based on the test scenarios and sets parameters. This module also dynamically adjusts request settings based on user authorization to enhance the realism of test requests. The load generation module dynamically adjusts the number of virtual users and request frequency based on user behavior simulation module and real-time monitoring data to ensure... The system can withstand a certain load and undergo stress testing. It automatically adjusts the generated load based on real-time monitoring data to test its responsiveness under different loads. The performance monitoring module monitors system performance metrics in real time and compares them with historical data. This monitoring data provides a basis for performance judgment, ensuring that the tested performance metrics meet preset standards. The load judgment module determines whether the current load exceeds the system's capacity based on real-time monitoring data and historical benchmarks, and makes timely adjustments. The performance judgment module monitors performance metrics in real time and compares them with set performance thresholds to determine whether the system meets business requirements. If the threshold is exceeded, adjustments are made. The error detection module monitors request success rate and error types in real time, generating detailed error classification reports to help the team identify and analyze faults. The error detection module uses artificial intelligence algorithms for fault mode analysis to improve fault diagnosis efficiency. Data collection and result verification are also included. The data collection module is responsible for collecting test results and system logs, and supports storage in various data formats according to different needs. The result verification module executes an automated test verification process to ensure the accuracy and reliability of test results, and integrates with external verification tools. The security testing module evaluates the system's security performance by simulating various attack modes. The security testing module collaborates with the performance monitoring module to ensure the system's stability and security under different attack modes. The analysis and reporting module generates detailed visual reports based on collected data and monitoring results, helping users clearly understand system performance. The reports offer diverse formats, user-customizable styles, and intelligent analysis suggestions to help users optimize the system. The result backtracking module provides time-line-based visual analysis, helping users trace historical data, identify trends, anomalies, and performance bottlenecks. Through backtracking analysis, users can clearly see the system's performance changes over time, facilitating further optimization. The cloud collaboration module supports cross-team collaboration, allowing users to share test results in real time, and supports version control and permission management. The system intelligently adjusts based on geographical location and network latency.To optimize performance, the cross-regional load balancing module utilizes intelligent algorithms to distribute load across the cloud environment, thereby optimizing resource utilization and ensuring stable system performance in all regions.

[0018] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An end-to-end full-link stress testing and performance analysis method, characterized in that, The system includes a test planning module for collecting user requirements, designing test scenarios, and providing scenario templates. The test planning module is electrically connected to a user simulation module for simulating behavior based on user profiles to improve the realism of the test. The user simulation module is electrically connected to a load generation module for dynamically adjusting the number of virtual users and request frequency based on real-time monitoring data. The load generation module is electrically connected to a performance monitoring module for integrating real-time resource monitoring and historical data comparison and analysis to provide real-time performance status feedback. The performance monitoring module is electrically connected to an error detection module for real-time monitoring of request success rate and error type, providing error classification reports. The error detection module is electrically connected to an analysis report module for supporting intelligent visualization interfaces and diverse report formats. The analysis report module is electrically connected to a security testing module for simulating various attack modes to evaluate the system's security performance. The security testing module is electrically connected to a cloud collaboration module that allows multiple users to share test results in real time and supports version control. The test planning module is electrically connected to a request management module for requesting authorization from the user simulation module and setting parameters to improve the realism of the request. The request management module is electrically connected to a load determination module for determining whether the load generation module can handle the current load. The load determination module is electrically connected to a performance determination module for determining whether the performance monitoring module meets business requirements. The performance determination module is electrically connected to a data collection module for collecting detection data from the error detection module. The data collection module is electrically connected to a result verification module for verifying the accuracy of the data. The result verification module is electrically connected to a result backtracking module for backtracking and analyzing historical test data to identify trends and performance bottlenecks. The result backtracking module is electrically connected to a cross-region module for supporting cloud collaboration modules.

2. The end-to-end full-link stress testing and performance analysis method according to claim 1, characterized in that, The load generation module continuously optimizes the load generation strategy through machine learning algorithms to adapt to environmental changes, and the performance monitoring module can automatically adjust performance monitoring indicators and dynamically change them according to different testing stages.

3. The end-to-end full-link stress testing and performance analysis method according to claim 1, characterized in that, The error detection module uses artificial intelligence algorithms to analyze fault modes and improve fault diagnosis efficiency. The cross-region module supports intelligent adjustment based on geographical location and current network latency.

4. The end-to-end full-link stress testing and performance analysis method according to claim 1, characterized in that, The results backtracking module provides timeline-based visualization analysis to help users identify trends and anomalies, while the cloud collaboration module supports cross-team collaboration and allows users to set different access permissions.

5. The end-to-end full-link stress testing and performance analysis method according to claim 1, characterized in that, The security testing module includes multiple security testing mechanisms for DDoS, SQL injection, and cross-site scripting. The result verification module can be integrated with external verification tools to improve the verifiability and reliability of system results. The user behavior simulation module supports dynamic behavior analysis based on APIs.

6. The end-to-end full-link stress testing and performance analysis method according to claim 1, characterized in that, The test planning module collects user requirements, designs test scenarios, and provides scenario templates. The user simulation module constructs realistic user request behaviors based on collected user data. The request management module constructs dynamic requests and sets parameters. The load generation module dynamically adjusts the number of virtual users and request frequency based on real-time monitoring data. The performance monitoring module monitors the system's runtime performance indicators and compares them with historical data. The load assessment module's judgment mechanism compares real-time performance indicators with historical benchmarks to determine whether the system can withstand the current load. The performance assessment module's mechanism monitors performance indicators in real time and compares them with set performance thresholds to determine whether the system meets business requirements.

7. The end-to-end full-link stress testing and performance analysis method according to claim 1, characterized in that, The error detection module monitors the success rate and error type of requests in real time and generates an error classification report. The data collection module collects test results and system logs and supports storage in multiple data formats. The analysis report module generates a visual report of the test results, offers diverse report formats, and provides intelligent analysis suggestions.

8. The end-to-end full-link stress testing and performance analysis method according to claim 1, characterized in that, The result verification module executes an automated test verification process to ensure the reliability of test results. The cloud collaboration module facilitates real-time sharing of test results among multiple users and supports version control and permission management.

9. The end-to-end full-link stress testing and performance analysis method according to claim 1, characterized in that, The security testing module simulates various attack modes to evaluate the system's security performance, while the result backtracking module performs backtracking analysis on historical test data to identify system trends and performance bottlenecks.

10. The end-to-end full-link stress testing and performance analysis method according to claim 1, characterized in that, The cross-regional load balancing module intelligently distributes load in the cloud environment to optimize resource utilization.

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