Application system performance test method and device, medium and program product

Abstract

The invention discloses an application system performance testing method and device, a medium and a program product, belongs to the technical field of artificial intelligence, and particularly relates to application of a large model in the field of software testing. Under the condition of determining that the target performance test requirement is valid, inputting the target performance test requirement into the semantic generalization large model; performing semantic analysis on the target performance test requirement through a semantic generalization large model to obtain a target keyword; inputting the target keyword into a rule generation small model, and processing the target keyword through the rule generation small model to obtain a target performance test case; obtaining a target test parameter of the target application system; based on the performance test script template, generating a target performance test script according to the target performance test case and the target test parameters; and executing the target performance test script to obtain a target performance test report of the target application system.

Description

A method, apparatus, medium, and program product for testing application system performance. Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to the application of large models in the field of software testing, and particularly to an application system performance testing method, apparatus, medium, and program product. Background Technology

[0002] Performance testing plays a crucial role in ensuring the high availability of application systems. However, as its technology continues to evolve, the following problems exist: First, in the critical step of translating performance testing requirements into performance test cases, significant misunderstandings among testers regarding these requirements result in a consistently low accuracy rate, making it difficult for test cases to accurately reflect actual needs. Second, manually written performance test scripts are prone to errors, leading to a persistently high error rate. These problems severely impact the efficiency of application system performance testing.

[0003] The open-source promotion of large model technology provides a new approach to solving the above problems. Summary of the Invention

[0004] This invention provides an application system performance testing method, apparatus, medium, and program product to improve the accuracy of converting performance testing requirements into performance test cases, improve the accuracy of performance test cases, reduce the error rate of performance test scripts, and thus improve the performance testing efficiency of application systems.

[0005] According to one aspect of the present invention, an application system performance testing method is provided, the method comprising:

[0006] The effectiveness of the target performance test requirements is verified by using a dialogue-supervised small model, and if the target performance test requirements are found to be effective, they are then input into a semantic generalization large model.

[0007] Semantic analysis of the target performance testing requirements is performed using a semantic generalization model to obtain target keywords.

[0008] The target keywords are input into the rule generation mini-model, and the target keywords are processed by the rule generation mini-model to obtain the target performance test cases corresponding to the target performance test requirements.

[0009] Obtain the target test parameters for the target application system;

[0010] Based on the performance test script template, the target performance test script for the target application system is generated according to the target performance test cases and target test parameters.

[0011] Execute the target performance test script to obtain the target application system's target performance test report.

[0012] According to another aspect of the present invention, an application system performance testing apparatus is provided, the apparatus comprising:

[0013] The performance test requirement verification module is used to verify the validity of the target performance test requirement through a dialogue-supervised small model, and input the target performance test requirement into the semantic generalization large model if it is determined to be valid.

[0014] The target keyword determination module is used to perform semantic analysis on the target performance testing requirements through a semantic generalization model to obtain target keywords.

[0015] The performance test case determination module is used to input target keywords into the rule generation mini-model. The rule generation mini-model processes the target keywords to obtain the target performance test cases corresponding to the target performance test requirements.

[0016] The target test parameter acquisition module is used to acquire the target test parameters of the target application system.

[0017] The performance test script generation module is used to generate target performance test scripts for the target application system based on the performance test script template, the target performance test cases, and the target test parameters.

[0018] The performance test report determination module is used to execute the target performance test script and obtain the target performance test report of the target application system.

[0019] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0020] At least one processor; and

[0021] A memory that is communicatively connected to at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to execute the application system performance testing method of any embodiment of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the application system performance testing method of any embodiment of the present invention.

[0024] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements an application system performance testing method according to any embodiment of the present invention.

[0025] The technical solution of this invention verifies the validity of target performance test requirements through a dialogue supervision mini-model. If the target performance test requirements are deemed valid, they are input into a semantic generalization mini-model. The semantic generalization mini-model performs semantic analysis on the target performance test requirements to obtain target keywords. These target keywords are then input into a rule generation mini-model, which processes the target keywords to obtain target performance test cases corresponding to the target performance test requirements. Target test parameters for the target application system are obtained. Based on a performance test script template, a target performance test script for the target application system is generated according to the target performance test cases and target test parameters. The target performance test script is then executed to obtain a target performance test report for the target application system. The above technical solution verifies the validity of target performance test requirements through a dialogue-supervised small model, intercepting invalid requirements and avoiding waste of performance testing resources. Once the validity of the target performance test requirements is confirmed, a semantic generalization large model and a rule-based generation small model are used to automatically convert the requirements into target performance test cases, rather than manually. This avoids inaccuracies caused by misunderstandings of the requirements during manual conversion, thus improving the accuracy of the conversion. Next, based on a performance test script template, and according to the target test cases and parameters, the solution automatically generates target performance test scripts for the target application system, reducing error rates, lowering labor costs, and improving efficiency and accuracy. Finally, the target performance test scripts are automatically executed to generate a target performance test report. The entire technical solution automates the entire lifecycle of the target application system, from performance testing requirements to performance test cases, from performance test cases to performance test scripts, and from performance test scripts to performance test reports, thereby improving the performance testing efficiency of the target application system.

[0026] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 is a flowchart of an application system performance testing method according to Embodiment 1 of the present invention;

[0029] Figure 2 is a flowchart of an application system performance testing method according to Embodiment 2 of the present invention;

[0030] Figure 3 is a schematic diagram of an application system performance testing device provided according to Embodiment 3 of the present invention;

[0031] Figure 4 is a schematic diagram of the structure of an electronic device that implements the application system performance testing method of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "target" and "candidate," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Example 1

[0035] Figure 1 is a flowchart of an application system performance testing method provided in Embodiment 1 of the present invention. This embodiment is applicable to performance testing of application systems under high-concurrency scenarios. The method can be executed by an application system performance testing device, which can be implemented in hardware and / or software and can be configured in an electronic device. As shown in Figure 1, the method includes:

[0036] S101. The effectiveness of the target performance test requirements is verified by a dialogue-supervised small model, and if the target performance test requirements are found to be effective, they are input into the semantic generalization large model.

[0037] The dialogue-supervised small model refers to a pre-trained deep learning model used to detect the validity of user-inputted application system performance testing requirements. The target performance testing requirement refers to the requirement for performance testing of the target application system; specifically, the target application system refers to the application system that needs performance testing under high-concurrency scenarios. The semantic generalization large model refers to a pre-trained deep learning model used for semantic parsing from the target performance testing requirements.

[0038] Specifically, the target performance testing requirements are input into a small dialogue supervision model. This model checks whether the requirements meet the resource carrying capacity rules. If they do, the small dialogue supervision model outputs that the target performance testing requirements are valid; otherwise, it outputs that the requirements are invalid. Then, if the small dialogue supervision model outputs that the target performance testing requirements are valid, they are input into the semantic generalization model. Conversely, if the small dialogue supervision model outputs that the target performance testing requirements are invalid, a message indicating that the target performance testing requirements are invalid is sent to the testers.

[0039] Among them, the resource carrying rules refer to the rules built into the dialogue supervision mini-model, used to determine whether the application system performance testing requirements input by the user are valid; optionally, the resource carrying rules include usage scenario limiting rules and concurrent user limit rules. The usage scenario limiting rules are used to limit the usage scenarios of the semantic generalization large model; the concurrent user limit rules are used to limit the number of users performing business operations on the target application system at the same time.

[0040] Understandably, by using a dialogue-supervised mini-model to verify the validity of target performance test requirements, invalid target performance test requirements are intercepted, thus avoiding the waste of performance test resources.

[0041] S102. Semantic analysis of the target performance testing requirements is performed using a semantic generalization model to obtain target keywords.

[0042] Here, target keywords refer to the keywords needed to generate performance test cases. Rule generation mini-models refer to pre-trained models used to generate performance test cases.

[0043] Specifically, the semantic generalization model, based on its own semantic generalization capabilities, performs semantic analysis on the target performance testing requirements and outputs target keywords.

[0044] S103. Input the target keywords into the rule generation mini-model, process the target keywords through the rule generation mini-model, and obtain the target performance test cases corresponding to the target performance test requirements.

[0045] Among them, target performance test cases refer to test design documents or execution plans used to evaluate the performance of target application systems under specific stress conditions (such as response time, throughput, and resource utilization).

[0046] Specifically, the target keywords are input into the rule generation mini-model. The rule generation mini-model generates rules based on its own test cases, processes the target keywords, and obtains the target performance test cases corresponding to the target performance test requirements.

[0047] S104. Obtain the target test parameters of the target application system.

[0048] The target test parameters refer to the parameters required for performance testing of the target application system. Optionally, the target test parameters include the target concurrent users, the target load test duration, the target load test method, and the target test dataset. Specifically, the target concurrent users refer to the number of users performing business operations on the target application system simultaneously; the target load test duration refers to the duration of the load test on the target application system; the target load test method refers to the method used to perform the load test on the target application system; and the target test dataset refers to the dataset required for performance testing of the target application system.

[0049] Specifically, the target test parameters of the target application system can be obtained through a preset interface.

[0050] S105. Based on the performance test script template, generate the target performance test script for the target application system according to the target performance test cases and target test parameters.

[0051] Among them, the performance test script template refers to the template used to generate performance test scripts. The target performance test script refers to the performance test script for the target application system.

[0052] Specifically, the target performance test cases are populated into the test case module of the performance test script template. Based on the target test parameters, the parameter values ​​of the corresponding parameters in the performance test script template are updated. For example, based on the target number of concurrent users, the parameter value of the number of concurrent users in the performance test script template is updated; based on the target load test duration, the parameter value of the system load test duration in the performance test script template is updated; based on the target load test method, the parameter value of the system load test method in the performance test script template is updated; and based on the target test dataset, the parameter value of the system test dataset in the performance test script template is updated, thereby obtaining the target performance test script for the target application system.

[0053] S106. Execute the target performance test script to obtain the target performance test report of the target application system.

[0054] The target performance test report records the performance and issues of the target application system under different stress conditions in high-concurrency scenarios. It should be noted that the target performance test report includes optimization suggestions for the target application system, providing developers with directions for improvement.

[0055] The technical solution of this invention verifies the validity of target performance test requirements through a dialogue supervision mini-model. If the target performance test requirements are deemed valid, they are input into a semantic generalization mini-model. The semantic generalization mini-model performs semantic analysis on the target performance test requirements to obtain target keywords. These target keywords are then input into a rule generation mini-model, which processes the target keywords to obtain target performance test cases corresponding to the target performance test requirements. Target test parameters for the target application system are obtained. Based on a performance test script template, a target performance test script for the target application system is generated according to the target performance test cases and target test parameters. The target performance test script is then executed to obtain a target performance test report for the target application system. The above technical solution verifies the validity of target performance test requirements through a dialogue-supervised small model, intercepting invalid requirements and avoiding waste of performance testing resources. Once the validity of the target performance test requirements is confirmed, a semantic generalization large model and a rule-based generation small model are used to automatically convert the requirements into target performance test cases, rather than manually. This avoids inaccuracies caused by misunderstandings of the requirements during manual conversion, thus improving the accuracy of the conversion. Next, based on a performance test script template, and according to the target test cases and parameters, the solution automatically generates target performance test scripts for the target application system, reducing error rates, lowering labor costs, and improving efficiency and accuracy. Finally, the target performance test scripts are automatically executed to generate a target performance test report. The entire technical solution automates the entire lifecycle of the target application system, from performance testing requirements to performance test cases, from performance test cases to performance test scripts, and from performance test scripts to performance test reports, thereby improving the performance testing efficiency of the target application system.

[0056] Example 2

[0057] Figure 2 is a flowchart of an application system performance testing method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment further optimizes the step of "performing semantic analysis of target performance testing requirements through a semantic generalization model to obtain target keywords," providing an optional implementation scheme. It should be noted that parts not detailed in this embodiment can be referred to in other embodiments. As shown in Figure 2, the method includes:

[0058] S201. The effectiveness of the target performance test requirements is verified by a dialogue-supervised small model, and if the target performance test requirements are found to be effective, the target performance test requirements are input into the semantic generalization large model.

[0059] S202. The semantic generalization model encapsulates the target performance testing requirements based on its own prompt word assembly rules to obtain model prompt words.

[0060] Among them, the cue word assembly rule refers to the rule used to encapsulate the performance testing requirements of the large semantic generalization model. Model cue words are the text that guides the large semantic generalization model to output more accurate results.

[0061] Specifically, the semantic generalization model encapsulates the target performance testing requirements, background information on performance testing, and model output rules together based on its own prompt word assembly rules to obtain model prompt words.

[0062] Understandably, the prompt word assembly rules based on the semantic generalization model encapsulate the target performance testing requirements, thereby achieving secondary processing of the target performance testing requirements. This allows the semantic generalization model to better understand the target performance testing requirements, obtain more accurate output results, reduce the generalization ability of the semantic generalization model, and improve the accuracy of understanding the target performance testing requirements.

[0063] S203. The semantic generalization model matches the model prompts with the candidate keywords in the case knowledge base using word vectors, and outputs the target keywords.

[0064] The case knowledge base refers to a local database used to store knowledge related to performance test cases. Candidate keywords are the keywords stored in the case knowledge base.

[0065] Specifically, the semantic generalization model performs word vectorization on the model prompt words to obtain prompt word vectors. At the same time, the semantic generalization model performs word vectorization on each candidate keyword in the case knowledge base to obtain the word vector corresponding to each candidate keyword in the case knowledge base. Then, the semantic generalization model determines the semantic similarity score between the prompt word vectors and the word vectors corresponding to each candidate keyword in the case knowledge base based on the semantic similarity between them. Finally, the candidate keyword with the highest semantic similarity score to the prompt word vector in the case knowledge base is output as the target keyword.

[0066] S204. Input the target keywords into the rule generation mini-model, process the target keywords through the rule generation mini-model, and obtain the target performance test cases corresponding to the target performance test requirements.

[0067] Specifically, the target keywords are input into the rule generation mini-model. Based on the correspondence between candidate keywords and candidate case generation data in the case knowledge base, the rule generation mini-model extracts the target case generation data corresponding to the target keywords from the case knowledge base. Based on its own test case generation rules, the rule generation mini-model performs structured processing on the target case generation data and outputs the target performance test cases corresponding to the target performance test requirements.

[0068] Candidate test case generation data refers to the test case generation data stored in the test case knowledge base. Specifically, test case generation data refers to the data required to generate a performance test case. Optionally, test case generation data includes, but is not limited to, service name, request data message, response data message, and test method name. It should be noted that one candidate keyword in the test case knowledge base corresponds to one candidate test case generation data. Test case generation rules refer to the rules used to generate structured performance test cases. Target test case generation data refers to the test case generation data corresponding to the target keyword.

[0069] More specifically, the target keywords are input into the rule generation mini-model. Based on the correspondence between candidate keywords and candidate case generation data in the case knowledge base, the rule generation mini-model extracts the target case generation data corresponding to the target keywords from the case knowledge base, using the target keywords as the index condition. Then, based on its own test case generation rules, the rule generation mini-model transforms the target case generation data into structured case data output, thereby obtaining the target performance test cases corresponding to the target performance test requirements.

[0070] Understandably, by generating data from target keywords and case knowledge bases, and using rules to generate small models, target performance test cases corresponding to target performance test requirements can be automatically generated. This eliminates the need for manual writing of target performance test cases, thus improving the efficiency and accuracy of target performance test case generation.

[0071] S205. Obtain the target test parameters of the target application system.

[0072] S206. Based on the performance test script template, generate the target performance test script for the target application system according to the target performance test cases and target test parameters.

[0073] S207. Execute the target performance test script to obtain the target performance test report of the target application system.

[0074] The technical solution of this invention verifies the validity of target performance test requirements through a dialogue supervision mini-model. If the target performance test requirements are deemed valid, they are input into a semantic generalization mini-model. The semantic generalization mini-model encapsulates the target performance test requirements based on its own prompt word assembly rules, obtaining model prompt words. The semantic generalization mini-model performs word vector matching between the model prompt words and candidate keywords in the case knowledge base, outputting target keywords. The target keywords are input into a rule generation mini-model, which processes the target keywords to obtain target performance test cases corresponding to the target performance test requirements. Target test parameters for the target application system are obtained. Based on a performance test script template, a target performance test script for the target application system is generated according to the target performance test cases and target test parameters. The target performance test script is executed to obtain a target performance test report for the target application system. The above technical solution verifies the validity of target performance test requirements through a dialogue-supervised small model, intercepting invalid requirements and avoiding waste of performance testing resources. Once the validity of the target performance test requirements is confirmed, the requirements are encapsulated based on the prompt word assembly rules of the semantic generalization large model. This secondary processing of the requirements allows the semantic generalization large model to better understand them, obtaining more accurate target keywords. This reduces the generalization capability of the semantic generalization large model and improves the accuracy of understanding the target performance test requirements. Consequently, it improves the accuracy of the target performance test cases obtained from the rule-based small model and target keywords, avoiding inaccuracies caused by misunderstandings of the target performance test requirements during manual conversion. Subsequently, based on the performance test script template, the target performance test script for the target application system is automatically generated according to the target performance test cases and target test parameters, instead of being manually written. This reduces the error rate of the target performance test script, lowers labor costs, and improves the generation efficiency and accuracy of the target performance test script. Finally, the target performance test script is automatically executed to obtain the target performance test report. The entire technical solution automates the entire lifecycle of the target application system, from performance testing requirements to performance test cases, from performance test cases to performance test scripts, and from performance test scripts to performance test reports, thereby improving the performance testing efficiency of the target application system.

[0075] Based on the above embodiments, as an optional embodiment of the present invention, before obtaining the target test parameters of the target application system, the method further includes: detecting whether the target performance test case already exists in the local test case asset library; if the target performance test case already exists in the local test case asset library, then using the case identifier of the target performance test case as an index, obtaining the target performance test script corresponding to the target performance test case from the local test case asset library, so as to avoid the repeated generation of performance test scripts, improve the reusability of performance test scripts in the test case asset library, and improve the overall efficiency of performance testing of the target application system; then, S207 is executed; if the target performance test case does not exist in the local test case asset library, then S205, S206 and S207 are executed, and the target performance test case and the target performance test script are stored in the local test case asset library, so as to enrich the resources in the local test case asset library and improve the efficiency of subsequent application system performance testing, that is, when encountering a performance test case similar to the target performance test case, the target performance test script corresponding to the target performance test case can be directly obtained from the local test case asset library without regenerating the performance test script.

[0076] Example 3

[0077] Figure 3 is a schematic diagram of an application system performance testing device provided in Embodiment 3 of the present invention. This embodiment is applicable to performance testing of application systems under high concurrency scenarios. The device can be implemented in hardware and / or software and can be configured in an electronic device. As shown in Figure 3, the device includes:

[0078] The performance test requirement verification module 301 is used to verify the validity of the target performance test requirement through a dialogue supervision small model, and input the target performance test requirement into the semantic generalization large model if the target performance test requirement is determined to be valid.

[0079] The target keyword determination module 302 is used to perform semantic analysis on the target performance testing requirements through a semantic generalization model to obtain target keywords.

[0080] The performance test case determination module 303 is used to input target keywords into the rule generation mini-model, process the target keywords through the rule generation mini-model, and obtain the target performance test cases corresponding to the target performance test requirements.

[0081] The target test parameter acquisition module 304 is used to acquire the target test parameters of the target application system.

[0082] The performance test script generation module 305 is used to generate target performance test scripts for the target application system based on the performance test script template, the target performance test cases, and the target test parameters.

[0083] The performance test report determination module 306 is used to execute the target performance test script and obtain the target performance test report of the target application system.

[0084] The technical solution of this invention verifies the validity of target performance test requirements through a dialogue supervision mini-model. If the target performance test requirements are deemed valid, they are input into a semantic generalization mini-model. The semantic generalization mini-model performs semantic analysis on the target performance test requirements to obtain target keywords. These target keywords are then input into a rule generation mini-model, which processes the target keywords to obtain target performance test cases corresponding to the target performance test requirements. Target test parameters for the target application system are obtained. Based on a performance test script template, a target performance test script for the target application system is generated according to the target performance test cases and target test parameters. The target performance test script is then executed to obtain a target performance test report for the target application system. The above technical solution verifies the validity of target performance test requirements through a dialogue-supervised small model, intercepting invalid requirements and avoiding waste of performance testing resources. Once the validity of the target performance test requirements is confirmed, a semantic generalization large model and a rule-based generation small model are used to automatically convert the requirements into target performance test cases, rather than manually. This avoids inaccuracies caused by misunderstandings of the requirements during manual conversion, thus improving the accuracy of the conversion. Next, based on a performance test script template, and according to the target test cases and parameters, the solution automatically generates target performance test scripts for the target application system, reducing error rates, lowering labor costs, and improving efficiency and accuracy. Finally, the target performance test scripts are automatically executed to generate a target performance test report. The entire technical solution automates the entire lifecycle of the target application system, from performance testing requirements to performance test cases, from performance test cases to performance test scripts, and from performance test scripts to performance test reports, thereby improving the performance testing efficiency of the target application system.

[0085] Optional, the performance test requirement verification module 301 is specifically used for:

[0086] The target performance testing requirements are input into the dialogue supervision mini-model, and the dialogue supervision mini-model is used to detect whether the target performance testing requirements meet the resource carrying rules;

[0087] If the requirements are met, then the performance test requirements for the output target of the dialogue-supervised small model are valid.

[0088] Otherwise, the output target performance test requirement of the dialogue-supervised small model is invalid.

[0089] Optionally, resource carrying rules include usage scenario limitation rules and concurrent user limit rules.

[0090] Optionally, the target keyword determination module 302 includes:

[0091] The model prompt word determination unit is used to encapsulate the target performance test requirements based on the semantic generalization large model's own prompt word assembly rules to obtain model prompt words.

[0092] The target keyword determination unit is used by the semantic generalization model to match the model prompts with the candidate keywords in the case knowledge base using word vectors, and output the target keywords.

[0093] Optional, target keyword identification unit, specifically used for:

[0094] The semantic generalization model performs word vectorization on the model prompt words to obtain prompt word vectors;

[0095] The semantic generalization model performs word vectorization on each candidate keyword in the case knowledge base to obtain the word vector corresponding to each candidate keyword in the case knowledge base;

[0096] The semantic generalization model determines the semantic similarity score between the prompt word vector and the word vector corresponding to each candidate keyword in the case knowledge base based on the semantic similarity between the prompt word vector and the word vector corresponding to each candidate keyword in the case knowledge base.

[0097] Based on the semantic similarity score between the prompt word vector and the word vector corresponding to each candidate keyword in the case knowledge base, the candidate keyword with the highest semantic similarity score to the prompt word vector is determined from the case knowledge base and output as the target keyword.

[0098] Optional, the performance test case determination module 303 is specifically used for:

[0099] The rule generation mini-model extracts target case generation data corresponding to target keywords from the case knowledge base based on the correspondence between candidate keywords and candidate case generation data in the case knowledge base.

[0100] The rule generation mini-model performs structured processing on the target case generation data based on its own test case generation rules, and outputs target performance test cases corresponding to the target performance test requirements.

[0101] Optionally, the target test parameters include the target number of concurrent users, the target load test duration, the target load test method, and the target test dataset.

[0102] The application system performance testing device provided in this embodiment of the invention can execute the application system performance testing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing each application system performance testing method.

[0103] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0104] Example 4

[0105] Figure 4 illustrates a schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0106] As shown in Figure 4, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0107] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0108] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as application system performance testing methods.

[0109] In some embodiments, the application system performance testing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the application system performance testing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the application system performance testing method by any other suitable means (e.g., by means of firmware).

[0110] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0113] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0114] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0115] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0116] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0117] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for testing the performance of an application system, characterized in that, include: The effectiveness of the target performance test requirements is verified by a small dialogue supervision model, and if the target performance test requirements are found to be effective, they are then input into a large semantic generalization model. The semantic generalization model is used to perform semantic analysis on the target performance test requirements to obtain target keywords; the target keywords are then input into a rule generation model, and the target keywords are processed by the rule generation model to obtain the target performance test cases corresponding to the target performance test requirements. Obtain the target test parameters for the target application system; Based on the performance test script template, the target performance test script for the target application system is generated according to the target performance test case and the target test parameters. Execute the target performance test script to obtain the target performance test report of the target application system.

2. The method according to claim 1, characterized in that, The validity of the target performance test requirement is verified by using a dialogue-supervised mini-model, including: inputting the target performance test requirement into the dialogue-supervised mini-model, and using the dialogue-supervised mini-model to detect whether the target performance test requirement meets the resource carrying rules; if it does, the dialogue-supervised mini-model outputs that the target performance test requirement is valid; otherwise, the dialogue-supervised mini-model outputs that the target performance test requirement is invalid.

3. The method according to claim 2, characterized in that, The resource carrying rules include usage scenario limitation rules and concurrent user limit rules.

4. The method according to claim 1, characterized in that, The step of performing semantic analysis on the target performance testing requirements using the semantic generalization model to obtain target keywords includes: the semantic generalization model encapsulating the target performance testing requirements based on its own prompt word assembly rules to obtain model prompt words; and the semantic generalization model performing word vector matching between the model prompt words and candidate keywords in the case knowledge base to output target keywords.

5. The method according to claim 4, characterized in that, The semantic generalization model performs word vector matching between the model prompt words and candidate keywords in the case knowledge base to output target keywords. This includes: the semantic generalization model vectorizing the model prompt words to obtain prompt word vectors; the semantic generalization model vectorizing each candidate keyword in the case knowledge base to obtain a word vector corresponding to each candidate keyword; the semantic generalization model determining a semantic similarity score between the prompt word vectors and the word vectors corresponding to each candidate keyword in the case knowledge base based on the semantic similarity score; and determining the candidate keyword with the highest semantic similarity score to the prompt word vectors from the case knowledge base as the target keyword output.

6. The method according to claim 1, characterized in that, The rule-generating mini-model processes the target keywords to obtain target performance test cases corresponding to the target performance test requirements. This includes: the rule-generating mini-model extracting target case generation data corresponding to the target keywords from the case knowledge base based on the correspondence between candidate keywords and candidate case generation data in the case knowledge base; and the rule-generating mini-model performing structured processing on the target case generation data based on its own test case generation rules to output target performance test cases corresponding to the target performance test requirements.

7. The method according to claim 1, characterized in that, The target test parameters include the target number of concurrent users, the target load test duration, the target load test method, and the target test dataset.

8. An application system performance testing device, characterized in that, include: The performance test requirement verification module is used to verify the validity of the target performance test requirement through a dialogue-supervised small model, and if the target performance test requirement is determined to be valid, the target performance test requirement is input into the semantic generalization large model. The target keyword determination module is used to perform semantic analysis on the target performance testing requirements through the semantic generalization model to obtain target keywords. The performance test case determination module is used to input the target keywords into the rule generation mini-model, process the target keywords through the rule generation mini-model, and obtain the target performance test cases corresponding to the target performance test requirements. The target test parameter acquisition module is used to acquire the target test parameters of the target application system. The performance test script generation module is used to generate a target performance test script for the target application system based on the performance test script template, the target performance test case, and the target test parameters. The performance test report determination module is used to execute the target performance test script and obtain the target performance test report of the target application system.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the application system performance testing method according to any one of claims 1-7.

10. A computer program product comprising a computer program that, when executed by a processor, implements the application system performance testing method according to any one of claims 1-7.

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