A full-link stress testing method and system based on artificial intelligence

Abstract

The present application provides a full-link stress testing method and system based on artificial intelligence, the method comprising: determining the data type based on a test target defined by the user; generating simulated traffic based on the data type and the business type traffic ratio obtained based on historical access logs; generating stress testing execution information based on the simulated traffic and a preset fuzzy control model; when the stress testing execution information is in execution, obtaining the performance indicators corresponding to the stress testing execution information in the execution process; generating node topology information based on the performance indicators, the stress testing execution information and preset hierarchical fuse conditions; determining the full-link stress testing plan based on the node topology information and preset stress testing indicators. Build a test environment consistent with the production environment to ensure that the test results truly reflect the performance of the production environment; generate large-scale test data for real scenarios, simulate real business scenarios, and improve the reliability of test results.

Description

Technical Field

[0001] This application relates to the field of software testing, and in particular to an artificial intelligence-based full-link stress testing method and system. Background Art

[0002] Full-link stress testing is an important means of verifying a system's overall performance and stability in high-concurrency, high-load scenarios. Existing techniques typically involve building a test environment, generating test data, and executing stress tests. However, existing techniques have numerous shortcomings, making it difficult to truly reflect the system's performance in a production environment.

[0003] First, there are differences between the test environment and the production environment, such as inconsistencies in hardware resources, network topology, and middleware versions, which result in the test results being unable to truly reflect the performance of the production environment. Secondly, there is a lack of large-scale test data for real scenarios, including data volume, data diversity, data access patterns, etc., which leads to distorted test results. In addition, existing technologies often only focus on core business links, ignoring edge scenarios or relying on systems such as third-party interfaces, message queues, caches, etc., which pose potential risks. On the other hand, resource monitoring is not comprehensive or the analysis capabilities are insufficient, making it difficult to accurately locate performance bottlenecks. Only focusing on surface indicators such as CPU and memory, there is a lack of in-depth exploration of root causes, such as slow SQL, cache breakdown, lock contention, or downstream service throttling. Finally, full-link stress testing has high requirements for hardware and labor costs, insufficient automation, and low efficiency in repeated testing.

[0004] In summary, existing full-link stress testing technology has many problems, such as inconsistency between the test environment and the production environment, insufficient data preparation, incomplete link coverage, misjudgment of performance bottlenecks, lack of in-depth analysis of test results, and resource and cost limitations. A new full-link stress testing method is urgently needed to solve the above problems. Summary of the Invention

[0005] In view of the above problems, the present application is proposed to provide an artificial intelligence-based full-link stress testing method and system that overcomes the above problems or at least partially solves the above problems, including:

[0006] An artificial intelligence-based full-link stress testing method, the method comprising:

[0007] Determining a data type based on a user-defined test objective, wherein the data type includes stress testing data and / or real data;

[0008] Generate simulated traffic based on the data type and the traffic ratio of the business type obtained based on historical access logs;

[0009] Generate stress test execution information based on the simulated traffic and a preset fuzzy control model;

[0010] When the stress test execution information is being executed, obtaining a performance indicator corresponding to the stress test execution information during the execution process;

[0011] Generate node topology information based on the performance indicators, the stress test execution information, and preset hierarchical circuit breaking conditions;

[0012] Determine the full-link stress testing plan based on the node topology information and preset stress testing indicators.

[0013] Furthermore, the step of determining a data type based on a user-defined test target, wherein the data type includes stress testing data and / or real data, includes:

[0014] Generate stress testing tasks based on the test objectives;

[0015] Determine the target link node based on the stress testing task;

[0016] Determining parameter information based on the stress testing task and the target link node;

[0017] The data type is determined according to the parameter information and the preset image production library, wherein the data type includes stress testing data and / or real data.

[0018] Furthermore, the step of generating simulated traffic based on the data type and the traffic ratio of the business type obtained based on the historical access log includes:

[0019] determining target conditions based on the data type;

[0020] Determining stress testing rules based on the target conditions and the data type;

[0021] Desensitizing the real data according to the stress testing rules to generate a stress testing data set;

[0022] Generate simulated traffic based on the stress testing data set and the business type traffic ratio obtained based on historical access logs.

[0023] Furthermore, the step of generating stress testing execution information based on the simulated flow and the preset fuzzy control model includes:

[0024] Determining weight values of various flows according to the simulated flow and a preset fuzzy control model;

[0025] Generate a weight label list according to each of the traffic weight values;

[0026] Generate a full-link node based on the weight label list and each distributed node;

[0027] The stress test execution information is generated by covering the full-link node according to the simulated traffic.

[0028] Furthermore, the step of generating node topology information based on the performance indicators, the stress test execution information, and preset hierarchical circuit breaking conditions includes:

[0029] Determining a processing mechanism based on the performance indicators and the preset graded fuse conditions;

[0030] Generate node topology information based on the processing mechanism and the stress test execution information;

[0031] Generate a stress test result based on the node topology information and the performance indicator.

[0032] Furthermore, the step of determining a processing mechanism based on the performance indicator and the preset hierarchical fuse conditions, wherein the preset hierarchical fuse conditions include a first-level fuse condition and / or a second-level fuse condition, includes:

[0033] Determining an indicator attribute of the performance indicator, wherein the indicator attribute includes a call success rate or a latency;

[0034] When the indicator attribute is the call success rate, and the call success rate is less than the first preset call threshold, and / or when the indicator attribute is the delay, and the delay is greater than the first preset delay threshold, then determining that the current preset hierarchical fuse condition is the first-level fuse condition, and reducing the current stress testing flow to a preset percentage range of the original flow is determined as the processing mechanism; and / or,

[0035] When the indicator attribute is the call success rate, and the call success rate is less than the second preset call threshold, wherein the second preset call threshold is less than the first preset call threshold, and / or when the indicator attribute is the delay, and the delay is greater than the second preset delay threshold, wherein the second preset delay threshold is greater than the first preset delay threshold, then the current preset hierarchical fuse condition is determined to be the second-level fuse condition, and isolation between problem nodes is determined as the processing mechanism.

[0036] Furthermore, the step of determining a full-link stress testing solution based on the node topology information and preset stress testing indicators includes:

[0037] Generate stress testing results based on the node topology information and the performance indicators;

[0038] Generate an optimization plan based on the stress testing results and preset stress testing indicators;

[0039] Determine a full-link stress testing solution based on the stress testing results and the optimization solution.

[0040] An embodiment of the present application further discloses an artificial intelligence-based full-link stress testing system, the system comprising:

[0041] A first determining module is configured to determine a data type based on a user-defined test objective, wherein the data type includes stress testing data and / or real data;

[0042] A first generating module is used to generate simulated traffic according to the data type and the traffic ratio of the business type obtained based on the historical access log;

[0043] A second generating module is used to generate stress testing execution information according to the simulated flow and a preset fuzzy control model;

[0044] an acquisition module, configured to acquire, when the stress test execution information is being executed, a performance indicator corresponding to the stress test execution information during execution;

[0045] A third generation module is configured to generate node topology information based on the performance indicator, the stress test execution information, and preset hierarchical circuit breaking conditions;

[0046] The second determination module is used to determine a full-link stress testing solution based on the node topology information and preset stress testing indicators.

[0047] An embodiment of the present application also discloses a computer device, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, the steps of the full-link stress testing method based on artificial intelligence are implemented as described above.

[0048] An embodiment of the present application also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the full-link stress testing method based on artificial intelligence are implemented as described above.

[0049] This application has the following advantages:

[0050] In an embodiment of the present application, relative to the prior art that "the existing full-link stress testing technology has the problems of inconsistency between the test environment and the production environment, insufficient data preparation, incomplete link coverage, misjudgment of performance bottlenecks, shallow analysis of test results, and resource and cost limitations", the present application provides a solution based on artificial intelligence for solving full-link stress testing, specifically: "A full-link stress testing method based on artificial intelligence, the method comprising: determining a data type based on a user-defined test target, wherein the data type includes stress testing data and / or real data; generating simulated traffic based on the data type and the business type traffic ratio obtained based on historical access logs; generating stress testing execution information based on the simulated traffic and a preset fuzzy control model; when the stress testing execution information is in execution, obtaining performance indicators corresponding to the stress testing execution information in the execution process; generating node topology information based on the performance indicators, the stress testing execution information and preset hierarchical fuse conditions; determining a full-link stress testing plan based on the node topology information and preset stress testing indicators". By "generating simulated traffic according to the data type and the business type traffic ratio obtained based on the historical access log; generating stress test execution information according to the simulated traffic and the preset fuzzy control model; when the stress test execution information is in execution, obtaining the performance indicators corresponding to the stress test execution information in the execution process; generating node topology information according to the performance indicators, the stress test execution information and the preset hierarchical fuse conditions; determining the full-link stress test plan according to the node topology information and the preset stress test indicators", it solves the problems of "the existing full-link stress test technology has problems such as inconsistency between the test environment and the production environment, insufficient data preparation, incomplete link coverage, misjudgment of performance bottlenecks, The problems of "in-depth analysis of test results and resource and cost limitations" were solved by "building a test environment consistent with the production environment to ensure that the test results truly reflect the performance of the production environment; generating large-scale test data for real scenarios, simulating real business scenarios, and improving the reliability of test results; covering core business links, edge scenarios and dependent systems, discovering potential risk points, and improving the robustness of the system; comprehensively monitoring resource indicators and accurately locating performance bottlenecks; combining logs and link tracking to locate the root causes of performance bottlenecks, and deeply analyzing and solving performance problems; automating the test process, reducing labor costs, improving test efficiency, and supporting normalized stress testing." BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the description of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0052] Figure 1This is a flowchart of the steps of an artificial intelligence-based full-link stress testing method provided in one embodiment of the present application;

[0053] Figure 2 This is a structural block diagram of an artificial intelligence-based full-link stress testing system provided in one embodiment of the present application;

[0054] Figure 3 It is a structural diagram of a computer device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, features, and advantages of this application more readily apparent, the present application is further described below in conjunction with the accompanying drawings and specific embodiments. It is apparent that the embodiments described are only a portion of the embodiments of this application, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments in this application without inventive effort are also within the scope of protection of this application.

[0056] By analyzing existing technologies, the inventors discovered that some existing technologies are currently available to address issues such as inconsistencies between the test environment and the production environment in full-link stress testing, a lack of data volume or data diversity for real scenarios, neglect of edge scenarios or reliance on systems, incomplete resource monitoring or insufficient analytical capabilities, a focus on surface indicators without deep digging into root causes, high hardware and labor costs, and insufficient automation. Stress testing processes are typically automated to save time in stress testing preparation, execution, report compilation, and analysis, supporting normalized stress testing. However, there are still issues with insufficient analysis and summarization of different application scenarios, an inability to further optimize stress testing processes and rules, and to improve testing efficiency and accuracy. There is also a lack of intelligent adjustment mechanisms, multi-dimensional analysis indicators, and a lack of visual interfaces.

[0057] In summary, the existing technology has the following deficiencies:

[0058] 1. Differences between the test environment and the production environment, such as inconsistencies in hardware resources, network topology, and middleware versions, result in test results that cannot truly reflect the performance of the production environment.

[0059] 2. The lack of large-scale test data in real scenarios, including data volume, data diversity, data access patterns, etc., leads to distorted test results.

[0060] 3. Ignoring edge scenarios or relying on systems such as third-party interfaces, message queues, and caches poses potential risks.

[0061] 4. Incomplete resource monitoring or insufficient analysis capabilities make it difficult to accurately locate performance bottlenecks, such as slow SQL, cache breakdown, lock contention, or downstream service throttling.

[0062] 5. Focusing only on surface indicators without deep dives into the root causes makes it impossible to effectively analyze and resolve performance issues.

[0063] Reference Figure 1 , shows a flowchart of the steps of an artificial intelligence-based full-link stress testing method provided by an embodiment of the present application;

[0064] An artificial intelligence-based full-link stress testing method, the method comprising:

[0065] S110: Determine a data type based on a user-defined test target, wherein the data type includes stress testing data and / or real data;

[0066] S120, generating simulated traffic according to the data type and the traffic ratio of the service type obtained based on the historical access log;

[0067] S130: Generate stress test execution information based on the simulated traffic and a preset fuzzy control model;

[0068] S140: When the stress test execution information is being executed, obtaining a performance indicator corresponding to the stress test execution information during the execution process;

[0069] S150: Generate node topology information based on the performance indicator, the stress test execution information, and preset hierarchical circuit breaking conditions;

[0070] S160: Determine a full-link stress testing solution based on the node topology information and preset stress testing indicators.

[0071] In an embodiment of the present application, relative to the prior art that "the existing full-link stress testing technology has the problems of inconsistency between the test environment and the production environment, insufficient data preparation, incomplete link coverage, misjudgment of performance bottlenecks, shallow analysis of test results, and resource and cost limitations", the present application provides a solution based on artificial intelligence for solving full-link stress testing, specifically: "A full-link stress testing method based on artificial intelligence, the method comprising: determining a data type based on a user-defined test target, wherein the data type includes stress testing data and / or real data; generating simulated traffic based on the data type and the business type traffic ratio obtained based on historical access logs; generating stress testing execution information based on the simulated traffic and a preset fuzzy control model; when the stress testing execution information is in execution, obtaining performance indicators corresponding to the stress testing execution information in the execution process; generating node topology information based on the performance indicators, the stress testing execution information and preset hierarchical fuse conditions; determining a full-link stress testing plan based on the node topology information and preset stress testing indicators". By "generating simulated traffic according to the data type and the business type traffic ratio obtained based on the historical access log; generating stress test execution information according to the simulated traffic and the preset fuzzy control model; when the stress test execution information is in execution, obtaining the performance indicators corresponding to the stress test execution information in the execution process; generating node topology information according to the performance indicators, the stress test execution information and the preset hierarchical fuse conditions; determining the full-link stress test plan according to the node topology information and the preset stress test indicators", it solves the problems of "the existing full-link stress test technology has problems such as inconsistency between the test environment and the production environment, insufficient data preparation, incomplete link coverage, misjudgment of performance bottlenecks, The problems of "in-depth analysis of test results and resource and cost limitations" were solved by "building a test environment consistent with the production environment to ensure that the test results truly reflect the performance of the production environment; generating large-scale test data for real scenarios, simulating real business scenarios, and improving the reliability of test results; covering core business links, edge scenarios and dependent systems, discovering potential risk points, and improving the robustness of the system; comprehensively monitoring resource indicators and accurately locating performance bottlenecks; combining logs and link tracking to locate the root causes of performance bottlenecks, and deeply analyzing and solving performance problems; automating the test process, reducing labor costs, improving test efficiency, and supporting normalized stress testing."

[0072] Below, an artificial intelligence-based full-link stress testing method in this exemplary embodiment will be further described.

[0073] As described in step S110 , the data type is determined based on the test target defined by the user, wherein the data type includes stress testing data and / or real data.

[0074] In one embodiment of the present invention, the specific process of "determining a data type based on a user-defined test target, wherein the data type includes stress testing data and / or real data" in step S110 can be further explained in combination with the following description.

[0075] As described in the following steps,

[0076] S210: Generate a stress testing task according to the test target;

[0077] S220: Determine a target link node according to the stress testing task;

[0078] S230: Determine parameter information according to the stress testing task and the target link node;

[0079] S240. Determine a data type based on the parameter information and a preset image production library, wherein the data type includes stress testing data and / or real data.

[0080] It should be noted that the stress testing tasks are defined according to the test objectives, and the core link nodes are determined. A shadow table mirror production library, that is, a preset mirror production library, is created to isolate the stress testing data from the real data.

[0081] As an example, the test targets include but are not limited to target traffic and business type ratio; core link nodes include but are not limited to registration centers, distributed nodes, order services, payment services, and database nodes.

[0082] In a specific implementation, the test objectives are clearly defined and determined through performance indicators, business scenarios and traffic models; performance indicators include but are not limited to defining target throughput, response time, and success rate; business scenarios are to determine the core links that need to be covered, including but not limited to e-commerce order links and payment links; traffic models are to analyze the traffic ratio of business types based on historical logs, including but not limited to query requests and write requests.

[0083] As described in step S120, simulated traffic is generated according to the data type and the traffic ratio of the business type obtained based on the historical access log.

[0084] In an embodiment of the present invention, the specific process of "generating simulated traffic according to the data type and the traffic ratio of the business type obtained based on the historical access log" in step S120 can be further explained in combination with the following description.

[0085] As described in the following steps,

[0086] S310, determining a target condition according to the data type;

[0087] S320: Determine a stress testing rule based on the target condition and the data type;

[0088] S330: Desensitize the real data according to the stress testing rules to generate a stress testing data set;

[0089] S340: Generate simulated traffic based on the stress testing data set and the business type traffic ratio obtained based on historical access logs.

[0090] It should be noted that the stress testing components are deployed to the online system according to the data type, the stress testing traffic is intercepted and routed to the shadow environment; the real data is extracted from the shadow library and desensitized to generate the stress testing data set; and the mixed traffic (that is, the real traffic and the stress testing traffic) is simulated according to the traffic ratio of the business type.

[0091] As an example, stress testing parameters can be divided into static parameters and dynamic parameters. Static parameters include but are not limited to stress testing duration and a list of core link nodes; dynamic parameters include but are not limited to stress testing traffic gradients and circuit breaker thresholds; stress testing can be performed using JMeter, LoadRunner, or self-developed stress testing engines; link tracking can integrate SkyWalking and Zipkin for core link node identification; use database tools such as MySQL's mysqldump or Oracle's Data Pump to export the production library schema and data, which are target conditions; create a shadow library, which is a preset mirror production library and prohibit production business access; create an independent topic in the message queue to isolate stress testing messages; use an independent cluster or prefix isolation for the cache; deploy it in the service gateway and identify stress testing traffic through the request header; implant routing logic in the business service layer to route requests containing stress testing identifiers to the shadow library, grayscale release stress testing components to the test environment, and verify interception and routing functions; dynamically manage stress testing switches through the configuration center; desensitize fields such as user ID and mobile phone number in the shadow library; automatically clear the shadow library after the stress test to prevent residual data from affecting subsequent tests; sample real request data from the production library and transfer it to the shadow library; use scripting tools Generate stress test data sets to cover boundary scenarios; configure parameterized requests in JMeter to dynamically replace fields such as user ID and session token; confirm the stress test traffic path through link tracing tools; verify whether the stress test mock service of the downstream system is ready; perform low-stress testing to verify the stability of the environment and the effectiveness of data isolation; check logs and monitoring to ensure that there are no production data leaks or abnormal alarms; assign independent accounts to the stress test environment and limit production environment operation permissions; encrypt stress test scripts and data storage to prevent unauthorized access; pre-set automated circuit breaker scripts to immediately terminate the stress test and trigger an alarm when a core service anomaly is monitored; arrange operation and maintenance personnel to be on duty in real time and intervene at any time to handle uncovered abnormal scenarios, that is, stress test rules.

[0092] In a specific implementation, real user data is first retrieved from the backup database, and privacy-related information is coded to create test-only data. Based on the actual visit ratio of the website in the past, real user requests and test requests are mixed together, so that the measured results are closer to the actual situation.

[0093] As described in step S130, stress testing execution information is generated according to the simulated flow and the preset fuzzy control model.

[0094] In one embodiment of the present invention, the specific process of "generating stress testing execution information according to the simulated traffic and the preset fuzzy control model" in step S130 can be further explained in combination with the following description.

[0095] As described in the following steps,

[0096] S410, determining the weight value of each flow rate according to the simulated flow rate and a preset fuzzy control model;

[0097] S420: Generate a weight label list based on each of the traffic weight values;

[0098] S430: Generate a full-link node based on the weight label list and each distributed node;

[0099] S440: Generate stress test execution information based on the simulated traffic in the full-link node.

[0100] It should be noted that the traffic information of the service instances is collected in real time, including but not limited to real-time traffic, target traffic difference and difference change, and the traffic weight value of each instance is calculated; the weight label is updated through the instance management server, and the stress test request load is dynamically allocated to the distributed nodes to avoid overloading of a single node; the stress test traffic is injected through the stress test component, and the stress test identifier is uploaded to the entire link; the stress test traffic is transmitted in the business simulation system, covering all core link nodes; multi-protocol testing is supported, and the stress machine and test cluster are coordinated through a unified control machine.

[0101] As an example, by monitoring the status of each server in real time: how many requests are currently being processed, how far away from the target it is, and whether the traffic has increased or decreased recently; stress testing components include but are not limited to Tuxedo services and HTTP servers; multi-protocol testing is also performed, but is not limited to Tuxedo domain connections and HTTP requests.

[0102] In a specific implementation, these health indicators automatically grade the capacity of each server, also known as a weight label. The task allocator assigns more test tasks to servers with high capacity while ensuring that no server is overwhelmed. Stress testing components are used to inject simulated stress testing traffic into the system. A unique stress testing flag, such as is_stress_test=true, is transparently included in the request header or protocol to isolate stress testing traffic from actual business operations. Stress testing traffic must fully cover all core nodes in the entire link. Traffic delivery paths are tracked using stress testing flags to ensure that all key nodes receive stress testing requests. Multiple protocol tests, including Tuxedo domain connections, HTTP / S, and RPC, are supported. A unified control machine coordinates the stress application machine and the test cluster to ensure synchronization and logical consistency of requests from different protocols.

[0103] As described in step S150, node topology information is generated based on the performance indicators, the stress test execution information and the preset hierarchical circuit breaking conditions.

[0104] In one embodiment of the present invention, the specific process of "generating node topology information according to the performance indicators, the stress test execution information and the preset hierarchical circuit breaking conditions" in step S150 can be further explained in combination with the following description.

[0105] As described in the following steps,

[0106] S510: Determine a processing mechanism based on the performance indicator and the preset hierarchical fusing conditions;

[0107] S520: Generate node topology information based on the processing mechanism and the stress testing execution information;

[0108] S530: Generate a stress test result based on the node topology information and the performance indicator.

[0109] As an example, a node topology diagram is generated based on the processing mechanism and stress test execution information; a full-link node topology diagram is drawn based on the stress test traffic transmission path; nodes with high latency or high error rate are identified; and resource utilization and performance indicators are associated.

[0110] In a specific implementation, the full-link node topology diagram can be, but is not limited to, order → payment → inventory → logistics; nodes with high latency or high error rate are times out when a service TPS reaches 5000; associated resource utilization and performance indicators can be, but are not limited to, a sharp increase in latency due to full CPU load.

[0111] As described in step S510, a processing mechanism is determined based on the performance indicator and the preset hierarchical fuse conditions.

[0112] In one embodiment of the present invention, the specific process of "determining the processing mechanism according to the performance indicator and the preset hierarchical fuse condition" in step S510 can be further explained in combination with the following description.

[0113] As described in the following steps,

[0114] S610: Determine an indicator attribute of the performance indicator, wherein the indicator attribute includes a call success rate or a latency;

[0115] S620: When the indicator attribute is the call success rate and the call success rate is less than the first preset call threshold, and / or when the indicator attribute is the delay and the delay is greater than the first preset delay threshold, determining that the current preset hierarchical fuse condition is the first-level fuse condition, and reducing the current stress testing flow to a preset percentage range of the original flow is determined as the processing mechanism; and / or,

[0116] S630. When the indicator attribute is the call success rate, and the call success rate is less than the second preset call threshold, wherein the second preset call threshold is less than the first preset call threshold, and / or when the indicator attribute is the delay, and the delay is greater than the second preset delay threshold, wherein the second preset delay threshold is greater than the first preset delay threshold, then the current preset hierarchical fuse condition is determined to be the second-level fuse condition, and isolation between problem nodes is determined as the processing mechanism.

[0117] It should be noted that the downstream system is monitored, and the call success rate, latency, TPS (transactions per second), and resource utilization are collected in real time; the infrastructure data of the stress testing node is reported to the monitoring platform in real time.

[0118] As an example, latency can be, but is not limited to, TP99; resource utilization can be, but is not limited to, CPU, memory, disk I / O, and network bandwidth; infrastructure data can be, but is not limited to, CPU load > 80%, memory utilization > 90%, and network packet loss rate > 1%.

[0119] In one specific implementation, if a first-level circuit breaker condition (i.e., traffic degradation) occurs, and the call success rate falls below a first preset call threshold (e.g., 95%) or the latency exceeds a first preset latency threshold (e.g., 1 second), the stress test traffic is automatically reduced to 50% of the original traffic, and the recovery of indicators is continuously monitored. If a second-level circuit breaker condition (i.e., link isolation) occurs, and the call success rate falls below a second preset call threshold (e.g., 80%) or the latency exceeds a second preset latency threshold (e.g., 3 seconds), the stress test switch on the abnormal link is disabled. Isolating the problematic node can include, but is not limited to, removing the problematic instance through a phased release to avoid impacting core services.

[0120] As described in step S160, a full-link stress testing solution is determined based on the node topology information and preset stress testing indicators.

[0121] In one embodiment of the present invention, the specific process of "determining a full-link stress testing solution based on the node topology information and preset stress testing indicators" in step S160 can be further explained in combination with the following description.

[0122] As described in the following steps,

[0123] S710: Generate a stress test result based on the node topology information and the performance indicator;

[0124] S720: Generate an optimization plan based on the stress testing results and preset stress testing indicators;

[0125] S730. Determine a full-link stress testing solution based on the stress testing result and the optimization solution.

[0126] It should be noted that the optimization plan is derived by comparing the stress test results with the preset indicators, and finally a full-link stress test report is produced.

[0127] As an example, the optimization plan can be short-term and long-term. In the short term, the optimization plan includes expanding the instance capacity based on resource bottlenecks, including but not limited to increasing the number of payment service instances; in the long term, the optimization plan includes but is not limited to reducing invalid log printing; database optimization includes but is not limited to adding indexes and sharding high-frequency query fields; and asynchronous transformation includes but is not limited to converting synchronous calls into message queues.

[0128] In a specific implementation, the full-link stress testing report includes performance bottlenecks and optimization suggestions; updated system capacity planning and emergency response plans.

[0129] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0130] Reference Figure 2 , shows a structural block diagram of an artificial intelligence-based full-link stress testing system provided by an embodiment of the present application;

[0131] An artificial intelligence-based full-link stress testing system, the system specifically comprising:

[0132] A first determining module 210 is configured to determine a data type based on a user-defined test objective, wherein the data type includes stress testing data and / or real data;

[0133] A first generating module 220 is configured to generate simulated traffic based on the data type and the traffic ratio of the service type obtained based on historical access logs;

[0134] A second generating module 230 is configured to generate stress testing execution information based on the simulated flow and a preset fuzzy control model;

[0135] An acquisition module 240 is configured to acquire, when the stress test execution information is being executed, a performance indicator corresponding to the stress test execution information during execution;

[0136] A third generating module 250 is configured to generate node topology information based on the performance indicator, the stress test execution information, and preset hierarchical fuse conditions;

[0137] The second determination module 260 is configured to determine a full-link stress testing solution based on the node topology information and preset stress testing indicators.

[0138] In one embodiment of the present invention, the first determining module 210 includes:

[0139] A first generation submodule is configured to generate a stress testing task according to the test target;

[0140] A first determination submodule is configured to determine a target link node according to the stress testing task;

[0141] A second determining submodule is configured to determine parameter information based on the stress testing task and the target link node;

[0142] The third determining submodule is configured to determine a data type according to the parameter information and a preset image production library, wherein the data type includes stress testing data and / or real data.

[0143] In one embodiment of the present invention, the first generating module 220 includes:

[0144] a fourth determining submodule, configured to determine a target condition based on the data type;

[0145] a fifth determining submodule, configured to determine a stress testing rule based on the target condition and the data type;

[0146] The second generation submodule is used to perform data desensitization on the real data according to the stress testing rules to generate a stress testing data set;

[0147] The third generation submodule is used to generate simulated traffic based on the stress testing data set and the business type traffic ratio obtained based on historical access logs.

[0148] In one embodiment of the present invention, the second generating module 230 includes:

[0149] a sixth determining submodule, configured to determine weight values of respective flows according to the simulated flow and a preset fuzzy control model;

[0150] A fourth generating submodule, configured to generate a weight label list according to each of the traffic weight values;

[0151] A fifth generation submodule, configured to generate a full-link node based on the weight label list and each distributed node;

[0152] The sixth generation submodule is used to generate stress testing execution information based on the simulated traffic in the full-link node.

[0153] In one embodiment of the present invention, the third generating module 250 includes:

[0154] a seventh determination submodule, configured to determine a processing mechanism based on the performance indicator and the preset hierarchical fuse conditions;

[0155] a seventh generating submodule, configured to generate node topology information according to the processing mechanism and the stress testing execution information;

[0156] An eighth generation submodule is configured to generate a stress test result based on the node topology information and the performance indicator.

[0157] In one embodiment of the present invention, the seventh determining submodule includes:

[0158] A determining unit, configured to determine an indicator attribute of the performance indicator, wherein the indicator attribute includes a call success rate or a latency;

[0159] A first processing unit is configured to, when the indicator attribute is the call success rate and the call success rate is less than a first preset call threshold, and / or when the indicator attribute is the delay and the delay is greater than a first preset delay threshold, determine that the current preset hierarchical fuse condition is the first-level fuse condition, and determine as the processing mechanism that the current stress testing flow rate is reduced to a preset percentage range of the original flow rate; and / or,

[0160] The second processing unit is used to determine that the current preset hierarchical fuse condition is the second-level fuse condition when the indicator attribute is the call success rate and the call success rate is less than the second preset call threshold, wherein the second preset call threshold is less than the first preset call threshold, and / or when the indicator attribute is the delay and the delay is greater than the second preset delay threshold, wherein the second preset delay threshold is greater than the first preset delay threshold, and isolate the problem nodes as the processing mechanism.

[0161] In one embodiment of the present invention, the second determining module 260 includes:

[0162] A ninth generating submodule, configured to generate a stress test result based on the node topology information and the performance indicator;

[0163] a tenth generation submodule, configured to generate an optimization plan based on the stress testing results and preset stress testing indicators;

[0164] An eighth determination submodule is configured to determine a full-link stress testing solution based on the stress testing result and the optimization solution.

[0165] Reference Figure 3 , shows a computer device of the present invention's full-link stress testing method based on artificial intelligence, which may specifically include the following:

[0166] The computer device 12 is a general-purpose computing device. The components of the computer device 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0167] The bus 18 represents one or more of several types of bus 18 structures, including a memory bus 18 or memory controller, a peripheral bus 18, an accelerated graphics port, a processor, or a local bus 18 that utilizes any of a variety of bus 18 architectures. Examples of such architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus 18, a Micro Channel Architecture (MAC) bus 18, an Enhanced ISA bus 18, an Audio Video Electronics Standards Association (VESA) local bus 18, and a Peripheral Component Interconnect (PCI) bus 18.

[0168] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0169] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be configured to read and write to non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). Although Figure 3Although not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), as well as an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) can be provided. In these cases, each drive can be connected to bus 18 via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42 configured to perform the functions of various embodiments of the present invention.

[0170] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in a memory. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules 42, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0171] The computer device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, a camera, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN)), a wide area network (WAN), and / or a public network (e.g., the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with the other modules of the computer device 12 via the bus 18. It should be understood that although Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to microcode, device drivers, redundant processing units 16, external disk drive arrays, RAID systems, tape drives, and data backup storage systems 34.

[0172] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing an artificial intelligence-based full-link stress testing method provided in an embodiment of the present invention.

[0173] That is, when the above-mentioned processing unit 16 executes the above-mentioned program, it realizes: determining the data type based on the test target defined by the user, wherein the data type includes stress test data and / or real data; generating simulated traffic based on the data type and the business type traffic ratio obtained based on the historical access log; generating stress test execution information based on the simulated traffic and the preset fuzzy control model; when the stress test execution information is in execution, obtaining the performance indicators corresponding to the stress test execution information in the execution process; generating node topology information based on the performance indicators, the stress test execution information and the preset hierarchical fuse conditions; and determining the full-link stress test plan based on the node topology information and the preset stress test indicators.

[0174] In an embodiment of the present invention, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an artificial intelligence-based full-link stress testing method as provided in all embodiments of the present application:

[0175] That is, when the program is executed by the processor, it is implemented as follows: determining the data type based on the test target defined by the user, wherein the data type includes stress test data and / or real data; generating simulated traffic based on the data type and the business type traffic ratio obtained based on the historical access log; generating stress test execution information based on the simulated traffic and the preset fuzzy control model; when the stress test execution information is in execution, obtaining the performance indicators corresponding to the stress test execution information in the execution process; generating node topology information based on the performance indicators, the stress test execution information and the preset hierarchical fuse conditions; and determining the full-link stress test plan based on the node topology information and the preset stress test indicators.

[0176] Any combination of one or more computer-readable media may be employed. A computer-readable medium may be a computer signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program for use by or in connection with an instruction execution system, apparatus, or device.

[0177] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0178] The computer program code for performing the operations of the present invention can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, via the Internet using an Internet service provider). The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referenced.

[0179] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0180] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0181] The above is a detailed introduction to the full-link stress testing method and system based on artificial intelligence provided by this application. This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method of this application and its core idea; at the same time, for general technical personnel in this field, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this application.

Claims

1. A full-link stress testing method based on artificial intelligence, characterized in that: The method comprises: Determining a data type based on a user-defined test objective, wherein the data type includes stress testing data and / or real data; Generate simulated traffic based on the data type and the traffic ratio of the business type obtained based on historical access logs; Generate stress test execution information based on the simulated traffic and a preset fuzzy control model; When the stress test execution information is being executed, obtaining a performance indicator corresponding to the stress test execution information during the execution process; Generate node topology information based on the performance indicator, the stress test execution information and the preset hierarchical fuse condition; determine a processing mechanism based on the performance indicator and the preset hierarchical fuse condition, wherein the preset hierarchical fuse condition includes a first-level fuse condition and / or a second-level fuse condition; determine an indicator attribute of the performance indicator, wherein the indicator attribute includes a call success rate or a delay; when the indicator attribute is the call success rate, and the call success rate is less than a first preset call threshold, and / or when the indicator attribute is the delay, and the delay is greater than a first preset delay threshold, determine that the current preset hierarchical fuse condition is the first-level fuse condition, and reduce the current stress test flow to the preset value of the original flow. Assume that the percentage range is determined as the processing mechanism; and / or, when the indicator attribute is the call success rate, and the call success rate is less than the second preset call threshold, wherein the second preset call threshold is less than the first preset call threshold, and / or, when the indicator attribute is the delay, and the delay is greater than the second preset delay threshold, wherein the second preset delay threshold is greater than the first preset delay threshold, then determine that the current preset hierarchical circuit breaker condition is the second-level circuit breaker condition, and isolate the problem nodes as the processing mechanism; generate node topology information based on the processing mechanism and the stress test execution information; generate stress test results based on the node topology information and the performance indicator; Determine the full-link stress testing plan based on the node topology information and preset stress testing indicators.

2. The method according to claim 1, characterized in that The step of determining a data type based on a user-defined test target, wherein the data type includes stress testing data and / or real data, includes: Generate stress testing tasks based on the test objectives; Determine the target link node based on the stress testing task; Determining parameter information based on the stress testing task and the target link node; The data type is determined according to the parameter information and the preset image production library, wherein the data type includes stress testing data and / or real data.

3. The method according to claim 1, characterized in that The step of generating simulated traffic according to the data type and the traffic ratio of the business type obtained based on the historical access log includes: determining target conditions based on the data type; Determining stress testing rules based on the target conditions and the data type; Desensitizing the real data according to the stress testing rules to generate a stress testing data set; Generate simulated traffic based on the stress testing data set and the business type traffic ratio obtained based on historical access logs.

4. The method according to claim 1, wherein The step of generating stress testing execution information based on the simulated traffic and the preset fuzzy control model includes: Determining weight values of various flows according to the simulated flow and a preset fuzzy control model; Generate a weight label list according to each of the traffic weight values; Generate a full-link node based on the weight label list and each distributed node; The stress test execution information is generated by covering the full-link node according to the simulated traffic.

5. The method according to claim 1, wherein The step of determining a full-link stress testing solution based on the node topology information and preset stress testing indicators includes: Generate stress testing results based on the node topology information and the performance indicators; Generate an optimization plan based on the stress testing results and preset stress testing indicators; Determine a full-link stress testing solution based on the stress testing results and the optimization solution.

6. A full-link stress testing system based on artificial intelligence, characterized in that: The system comprises: A first determining module is configured to determine a data type based on a user-defined test objective, wherein the data type includes stress testing data and / or real data; A first generating module is used to generate simulated traffic according to the data type and the traffic ratio of the business type obtained based on the historical access log; A second generating module is used to generate stress testing execution information according to the simulated flow and a preset fuzzy control model; an acquisition module, configured to acquire, when the stress test execution information is being executed, a performance indicator corresponding to the stress test execution information during execution; The third generation module is used to generate node topology information based on the performance indicator, the stress test execution information and the preset hierarchical fuse condition; determine the processing mechanism based on the performance indicator and the preset hierarchical fuse condition, wherein the preset hierarchical fuse condition includes a first-level fuse condition and / or a second-level fuse condition; determine the indicator attribute of the performance indicator, wherein the indicator attribute includes a call success rate or a delay; when the indicator attribute is the call success rate, and the call success rate is less than a first preset call threshold, and / or when the indicator attribute is the delay, and the delay is greater than the first preset delay threshold, then determine that the current preset hierarchical fuse condition is the first-level fuse condition, and reduce the current stress test flow to the original A preset percentage range of traffic is determined as the processing mechanism; and / or, when the indicator attribute is the call success rate, and the call success rate is less than the second preset call threshold, wherein the second preset call threshold is less than the first preset call threshold, and / or, when the indicator attribute is the delay, and the delay is greater than the second preset delay threshold, wherein the second preset delay threshold is greater than the first preset delay threshold, then the current preset hierarchical fuse condition is determined to be the secondary fuse condition, and isolation between problem nodes is determined as the processing mechanism; node topology information is generated based on the processing mechanism and the stress test execution information; and stress test results are generated based on the node topology information and the performance indicator; The second determination module is used to determine a full-link stress testing solution based on the node topology information and preset stress testing indicators.

7. A computer device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the method according to any one of claims 1 to 5 when executed by the processor.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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