Quality evaluation method and device for performance test requirements, equipment and storage medium

Abstract

The invention discloses a quality review method and device for performance test requirements, equipment and a storage medium, and the method comprises the steps: carrying out the screening and matching of a pre-created experience library based on a target performance test requirement and a text similarity model, and obtaining a similar product set; performing function point matching on the similar product set based on a data test module to obtain a similar function point set; on the basis of a preset judgment standard, comparing the similar function points with demand function points, and obtaining scores of the demand function points; and obtaining a target performance test demand score based on the demand function point priority weight according to the score of the demand function point. According to the invention, performance test requirements can be effectively reviewed, and progress lag and failure risks caused by unreasonable requirements of projects are avoided.

Description

Technical Field

[0001] The present invention relates to the field of performance testing technology, and in particular to a quality review method, device, equipment and storage medium for performance testing requirements. Background Art

[0002] In today's wave of digital transformation, the rapid rise of cloud computing and big data technologies has not only reshaped the IT architecture of enterprises, but also greatly promoted the innovation and upgrading of business systems. With the continuous expansion of business scale and the increasing complexity of user needs, the reliance of business systems on performance has reached an unprecedented level. As a key indicator for measuring system quality, user experience and business continuity, the importance of performance is self-evident. Therefore, performance testing, as an important means to ensure the stable operation of the system under high load and high concurrency environment, has become increasingly prominent.

[0003] However, in actual operation, the complexity and challenges of performance testing have also increased. On the one hand, the diversification of business system architecture and the trend of microservices have made it more difficult to identify and locate performance bottlenecks; on the other hand, the processing and analysis of large amounts of data have put forward higher requirements on the system's response time and throughput. Traditional performance testing methods and tools often cannot meet these new requirements.

[0004] In this context, the accuracy and rationality of performance testing requirements are particularly important. Unfortunately, since business requirements are often proposed by product managers or business analysts with non-technical backgrounds, they may lack a deep understanding of technical implementation, resulting in performance requirements being set too high or not in line with technical reality. Such unreasonable requirements not only bring huge pressure to the testing team, but also greatly increase the risk of project failure.

[0005] For experienced testers, they can rely on their profound professional knowledge and rich practical experience to keenly capture potential problems and risks during the requirements review stage, and through close communication with the technical team, timely adjust and optimize unreasonable requirements, thereby avoiding test failures and project delays caused by unreasonable requirements in the later stage. However, for testers who are new to the industry or have little experience, due to the lack of sufficient judgment and experience accumulation, they often find it difficult to identify unreasonable requirements at the first time, which not only affects the smooth progress of the testing work, but also may consume a lot of time and resources due to repeated modifications and testing, ultimately leading to serious delays in project progress, or even forced cancellation of the project. Summary of the invention

[0006] The main purpose of the present invention is to provide a quality review method, device, equipment and storage medium for performance test requirements, aiming to solve the technical problem of how to effectively review performance test requirements, thereby avoiding project delays and failure risks due to unreasonable requirements.

[0007] To achieve the above object, the present invention provides a quality review method for performance test requirements, the quality review method for performance test requirements comprising the following steps:

[0008] Based on the target performance test requirements and the text similarity model, the pre-created experience library is screened and matched to obtain a set of similar products;

[0009] Performing function point matching on the similar product set based on the data testing module to obtain a similar function point set;

[0010] Based on the preset evaluation criteria, the similar function points are compared with the required function points to obtain the scores of the required function points;

[0011] According to the scores of the required function points, based on the priority weights of the required function points, the target performance test requirement scores are obtained.

[0012] In one embodiment, the step of screening and matching a pre-created experience library based on the target performance test requirements and the text similarity model to obtain a similar product set includes:

[0013] Screening the experience library based on target performance test requirements to obtain an adaptation product set;

[0014] Based on the text similarity model, similarity matching is performed on the adapted product set to obtain a similar product set.

[0015] In one embodiment, the step of performing similarity matching on the adapted product set based on the text similarity model to obtain a similar product set includes:

[0016] Use the trained text similarity model to output the sentence vectors of the required text and the standard text;

[0017] Calculating sentence vector similarity between the requirement text and the standard text;

[0018] The adapted product set is screened according to the sentence vector similarity to obtain the similar product set.

[0019] In one embodiment, the step of performing function point matching on the similar product set based on the data testing module to obtain a similar function point set includes:

[0020] Perform test configuration matching on the similar product set to filter out function point related configuration sets;

[0021] Using a text similarity model to perform similarity matching on the function point related configuration set to obtain a first similar function point set;

[0022] The first similar function point set is subjected to test configuration matching and threshold judgment, and combined with preset function point weight values ​​to obtain a similar function point set.

[0023] In one embodiment, the step of comparing the similar function points with the required function points based on a preset evaluation criterion to obtain the score of the required function points includes:

[0024] Evaluate the test indicator based on the preset evaluation criteria to obtain an evaluation value of the test indicator, where the test indicator is an indicator corresponding to the required function point;

[0025] The evaluation values ​​of the test indicators are weighted averaged to obtain the scores of the required function points.

[0026] In one embodiment, after the step of performing function point matching on the similar product set based on the data testing module to obtain a similar function point set, the step further includes:

[0027] The indicator range of the indicator corresponding to the similar function point set is obtained, and the expected indicator corresponding to the required function point is compared and analyzed with the indicator range to obtain the analysis result of the expected indicator.

[0028] In one embodiment, the step of screening and matching the pre-created experience library based on the target performance test requirements and the text similarity model to obtain a similar product set further includes:

[0029] Collect product text data that needs to be adjusted, and optimize the text similarity model;

[0030] After the step of matching the function points of the similar product set based on the data testing module to obtain a similar function point set, the following step further comprises:

[0031] Collect text data of function points that need to be adjusted, and optimize the text similarity model.

[0032] In addition, to achieve the above-mentioned purpose, the present invention further provides a quality review device for performance test requirements, the quality review device for performance test requirements comprising:

[0033] The first acquisition module screens and matches the pre-created experience library based on the target performance test requirements and the text similarity model to obtain a similar product set;

[0034] A second acquisition module performs function point matching on the similar product set based on the data testing module to obtain a similar function point set;

[0035] A first scoring module compares the similar function points with the required function points based on a preset evaluation standard to obtain a score for the required function points;

[0036] The second scoring module obtains the target performance test requirement score according to the score of the required function point and based on the priority weight of the required function point.

[0037] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal device, which includes a memory, a processor, and a quality review program for performance testing requirements stored in the memory and executable on the processor, and the quality review program for performance testing requirements, when executed by the processor, implements the steps of the quality review method for performance testing requirements as described above.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a quality review program for performance testing requirements is stored. When the quality review program for performance testing requirements is executed by a processor, the steps of the quality review method for performance testing requirements as described above are implemented.

[0039] One or more technical solutions proposed in this application have at least the following technical effects:

[0040] A quality review method for performance test requirements first uses the configuration information in the experience library as a benchmark and matches a set of similar products that are similar to the target performance test requirements through a text similarity model. Subsequently, the review granularity is further refined and analyzed based on the various configuration information in the experience library, thereby accurately identifying a set of similar functional points that are highly relevant to the target requirements. This process not only improves the accuracy of the review, but also significantly enhances the comprehensiveness and objectivity of the review.

[0041] Based on the preset evaluation criteria, the similar function points are compared with the required function points to obtain the scores of the required function points, which provides strong data support for subsequent review decisions. By introducing the priority weights of function points, the overall value of the target performance test requirements can be evaluated more scientifically, ensuring that the review results meet actual needs and give priority to key function points.

[0042] Compared with the existing technology, this application realizes the transformation of the review mode from experience-driven to data-driven, significantly improving the efficiency and accuracy of the review. Through the combination of deep learning and multiple matching algorithms, it can more accurately match historical data and provide strong data support for performance testing requirements review. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a first exemplary embodiment of a quality review method for performance testing requirements of this application;

[0044] Figure 2 A flowchart of a second exemplary embodiment of a quality review method for performance testing requirements of this application;

[0045] Figure 3 A flowchart of a third exemplary embodiment of a quality review method for performance testing requirements of this application;

[0046] Figure 4 A schematic diagram of the module structure of the quality assessment device for performance testing requirements of an embodiment of the present application;

[0047] Figure 5 A schematic diagram of the device structure of the hardware operating environment involved in the quality review method for performance testing requirements in an embodiment of the present application.

[0048] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0050] The main solution of the embodiment of the present invention is: based on the target performance test requirements and the text similarity model, the pre-created experience library is screened and matched to obtain a similar product set; based on the data test module, the function points of the similar product set are matched to obtain a similar function point set; based on the preset evaluation criteria, the similar function points are compared with the demand function points to obtain the scores of the demand function points; according to the scores of the demand function points, based on the priority weights of the demand function points, the target performance test requirement scores are obtained. Compared with the traditional manual experience review method, by shifting the performance test quality review to the demand proposal stage, by summarizing the reasonable data of each module of the demand by experienced testers to generate a data set, the quality review of the collected requirements is conducted through the review method of the present invention, and the requirements that do not meet the quality review standards are sent back for rectification or cancelled, thereby cutting off the generation of unreasonable demand projects from the source.

[0051] The executor of the method of this embodiment can be a quality review device for performance testing requirements, or it can be a quality review terminal device or server for performance testing requirements. This embodiment takes the quality review device for performance testing requirements as an example. The quality review device for performance testing requirements can be integrated in terminal devices such as smart phones and tablet computers with data processing functions.

[0052] This embodiment combines the historical experience library with the performance test requirements, starting from product screening, and matching the test environment, test data, and test scope content step by step. The review algorithm involved in the entire process combines deep learning models and multiple matching algorithms to analyze the matching results, give whether there are risks in the demander's test indicators, and score the requirements, and finally form a complete review analysis result. It realizes the transformation of the review model from experience-driven to data-driven, significantly improving the efficiency and accuracy of the review. Through the combination of deep learning and multiple matching algorithms, it can match historical data more accurately, providing strong data support for performance test requirements review.

[0053] Specifically, the following are the detailed steps of an exemplary embodiment of the quality review method for performance test requirements:

[0054] Reference Figure 1 , Figure 1 This is a flow chart of the first exemplary embodiment of the quality review method for performance test requirements of this application. The quality review method for performance test requirements includes:

[0055] Step S10, based on the target performance test requirements and the text similarity model, the pre-created experience library is screened and matched to obtain a similar product set;

[0056] It should be noted that the specific purpose of pre-creating the experience database is to divide the experience database into multiple subsets P based on the product dimension, each subset Pi represents a set of historical performance test data of a specific product, and deeply analyze the data range required to be included in each product subset Pi. Specifically, it includes the test scope, test environment and test data.

[0057] The test scope includes the performance test indicators of each functional point (such as TPS, average response time, etc.), and also involves key parameter configuration. These configuration information are mainly used to understand the context of the performance test; the test environment can include instance configuration information such as component type, CPU model, memory size, etc. that each performance test case depends on. This information is used to reproduce the test scenario and evaluate the test results.

[0058] Test data may include key information such as database type, data volume, and underlying data. This information is used to understand the characteristics of test data and evaluate the accuracy of test results.

[0059] In a feasible implementation, step S10 may include steps S11 to S12:

[0060] Step S11, screening the experience library based on target performance test requirements to obtain an adaptation product set;

[0061] Specifically, first, clarify the major product category to which the target performance test requirements belong. Since the experience library has been divided according to product dimensions and stores the historical performance test data of each product, this structured data organization method can be directly used to accelerate the screening process.

[0062] Next, traverse each product subset in the experience library and compare the product name of each subset with the product category specified in the target performance test requirements. This comparison process is based on a string matching algorithm and can quickly identify product subsets that belong to the same category. During the comparison process, since there may be certain differences in product naming (such as different versions, different market names, etc.), product name matching rules can be defined in advance to ensure that all relevant product subsets can be accurately identified. Through this step, all product subsets that belong to the same category as the target performance test requirements can be successfully screened out from the experience library to obtain an adapted product set.

[0063] It should be noted that in this step of the present embodiment, detailed information of the adaptation product set can be obtained, including product name, subset number, included performance test cases, etc., so that these data can be further analyzed and utilized in subsequent steps.

[0064] Step S12: performing similarity matching on the adapted product set based on a text similarity model to obtain a similar product set.

[0065] In a feasible implementation, step S12 may include steps S121 to S124:

[0066] Step S121, using the trained text similarity model, outputting sentence vectors of the required text and the standard text;

[0067] It should be noted that the specific process of training the text similarity model is as follows: first identify the product names and functional points mentioned in the demand text, then use these key information as the basis for positive annotation, find the corresponding similar product names and functional point descriptions in the data set, and mark them as the positive text set Y = {Y1,...,Yn}. At the same time, in order to enhance the recognition ability of the model, the product names and functional point descriptions that do not belong to the same category or do not have the same functional points in the data set are also annotated as the negative text set N = {N1,...,Nn}. The SBERT (Sentence-BERT) pre-trained model is fine-tuned using the above-annotated training set. The SBERT model, with its unique twin network structure, can efficiently generate sentence vectors and capture the semantic similarity between sentences. During the training process, the positive and negative text pairs are respectively sent to the two branches of the model, deeply encoded through the BERT structure, and the token-level output is converted into a fixed-length sentence vector using the pooling layer. Subsequently, the cosine similarity between these sentence vectors is calculated, and the mean square error loss function is used as the optimization target. The model parameters are adjusted through the back propagation algorithm so that the model can more accurately distinguish between positive and negative text pairs. After multiple iterations of training, a model that can effectively evaluate text similarity can be obtained.

[0068] Step S122, calculating the sentence vector similarity between the requirement text and the standard text;

[0069] Specifically, the trained text similarity model is used to evaluate the requirement text of the target performance test requirement. Specifically, the requirement text is used as the text to be tested and compared one by one with the standard texts in the experience library (i.e., known function point descriptions or product names). Through model calculation, the sentence vector similarity between the requirement text and each standard text can be obtained. This similarity value reflects the degree of semantic proximity between them.

[0070] Step S123, screening the adapted product set according to the sentence vector similarity to obtain the similar product set;

[0071] Specifically, the set of adapted products is further screened based on the calculated sentence vector similarity. A reasonable similarity threshold can be set. Only when the similarity between the requirement text and the standard text of a product exceeds the threshold, the product is considered to be highly relevant to the target performance test requirements and is included in the set of similar products. Through such a screening process, a set of similar products that meets both the requirements of the general category and the matching of specific functional points is finally obtained, providing a more accurate target range for subsequent performance testing work.

[0072] Step S20, performing function point matching on the similar product set based on the data testing module to obtain a similar function point set;

[0073] In a feasible implementation, step S20 may include steps S21 to S23:

[0074] Step S21, performing test configuration matching on the similar product set to filter out function point related configuration sets;

[0075] Specifically, the test environment configuration of similar product sets is integrated with the test data to match the configuration set that is closely related to the function point. First, traverse each product in the similar product set and extract its test environment configuration. These configuration information include but are not limited to key elements such as hardware specifications, software versions, and operating systems. Then, based on these environment configurations, combined with the corresponding test data, especially the design indicators in the data, such as performance thresholds, response time requirements, etc., identify which test data is directly related to specific function points. In this process, the preset mapping relationship or logical rules can be used to preliminarily associate the test data with the function point, thereby constructing a configuration set related to the function point. These configuration sets not only contain detailed information about the test environment, but also associate specific test data indicators.

[0076] Step S22: using a text similarity model to perform similarity matching on the function point related configuration set to obtain a first similar function point set.

[0077] Specifically, the pre-trained text similarity model is used to compare the descriptive texts in the above configuration set one by one, and calculate their similarity with the target function point description. In this process, the model will consider factors such as the semantic relevance of words, context, and possible synonym replacement to ensure the accuracy and comprehensiveness of the similarity calculation. By setting a reasonable similarity threshold, the model can filter out those function points with similarity higher than the threshold to form the first similar function point set.

[0078] Step S23: Perform test configuration matching and threshold judgment on the first similar function point set, and obtain a similar function point set by combining the preset function point weight value.

[0079] Specifically, after obtaining the first set of similar function points, these function points are further subjected to test configuration matching and threshold judgment. This step aims to verify whether these function points are not only similar in text description, but also match the target function points in terms of specific test environment configuration and test data requirements. The system will compare the environmental configuration requirements (such as operating system version, hardware requirements, etc.) and test data requirements (such as the format and range of input data, etc.) of each function point with the corresponding requirements of the target function point one by one. At the same time, considering that the importance and priority of different function points in the test may be different, the system will combine the preset function point weight values ​​to weight the matching results. This weight value can be determined based on various factors such as historical testing experience, user feedback or expert evaluation, and is used to reflect the relative importance of each function point in the overall testing process.

[0080] Finally, a comprehensive judgment threshold is set based on the test configuration matching results and the weighted similarity score. Only when the comprehensive score of a function point is higher than this threshold, it is considered to truly meet the requirements of the target function point and is included in the final set of similar function points.

[0081] The calculation process of the function point set can be expressed mathematically as follows:

[0082]

[0083] Among them, g is a set of similar function points, e i Is the test environment configuration, w e Is the test environment configuration i The corresponding weight value, d i is the test data, w d is the test data d i The corresponding weight value, w i is the weight value, (u, v) is the sentence vector output by the text similarity model.

[0084] Step S30, based on a preset evaluation standard, comparing the similar function points with the required function points to obtain the scores of the required function points;

[0085] In a feasible implementation, step S30 may include steps S31 to S32:

[0086] Step S31, evaluating the test indicator based on the preset evaluation criteria to obtain the evaluation value of the test indicator, where the test indicator is the indicator corresponding to the required function point;

[0087] Specifically, the above-mentioned test indicators are various indicators of each function point in the target performance test requirements. These test indicators may include response time, throughput, error rate, etc., which are all set for specific environment configurations and test data. In order to accurately evaluate these test indicators, you can refer to the stress test indicators of multiple groups of similar function points under similar product sets for different environment configurations and different test data, and set a high threshold h for each stress test indicator. i , low threshold l i And range fluctuation threshold f i , and score the test indicators for each function point. The mathematical expression can be expressed as:

[0088]

[0089] Among them, a i is the input value of different configurations, h i is the high threshold, l i is the low threshold, f i is the range fluctuation threshold, w i is the weight value, n i is a collection of evaluation values ​​for different configurations;

[0090] t=1 represents that the expectation tends to a high threshold, t=2 represents that the expectation tends to a low threshold, t=3 represents that the expectation tends to a high threshold or a low threshold, that is, tends to a boundary value, and t=4 represents that the expectation tends to a range threshold.

[0091] Take the function point "order processing" as an example, assuming that its response time is used as the measurement indicator, and the high threshold h of the response time of similar function points is known. i 800 milliseconds, low threshold l i is 200 milliseconds, the range fluctuation threshold f i Set to ±50 milliseconds (i.e. the reasonable range is between 150 milliseconds and 750 milliseconds). If the expected value of the response time measurement indicator of the current "Order Processing" function point is 500 milliseconds, score it according to the preset evaluation criteria: Since 500 milliseconds is between the high threshold and the low threshold, and is not particularly close to any boundary value, but it is indeed within a reasonable range, the expected value of this measurement indicator tends to the range threshold, and the evaluation value calculated in combination with the weight value is 9w i .

[0092] Step S32, performing a weighted average operation on the evaluation values ​​of the test indicators to obtain the scores of the required function points.

[0093] Specifically, the evaluation values ​​of each test indicator obtained in the above steps are weighted averaged to calculate the overall score of the function point corresponding to the target performance test requirement. The key to this step is to determine the weight of each evaluation value in the weighted average. According to the importance of the actual application scenario and the test requirements, different weights can be assigned to different types of evaluation values, or they can be directly calculated in an equal weighted manner. By multiplying all evaluation values ​​by their respective weights and summing them, and then dividing them by the total weight (or the total number of evaluation values, in the case of equal weights), the comprehensive score of the function point can be obtained. The comprehensive score not only reflects the overall performance of the function point under specific performance test requirements, but also comprehensively considers the differences and importance of different indicators through weighted averaging, providing a strong basis for subsequent performance optimization and risk management.

[0094] Step S50, obtaining a target performance test requirement score according to the score of the requirement function point and based on the priority weight of the requirement function point.

[0095] Specifically, first clarify the test priority of each function point, which can be determined based on multiple factors such as business requirements, user feedback, system architecture, etc. Each function point is assigned a corresponding weight, which reflects its relative importance in the overall performance test requirements. The weight value can be any positive real number, where a higher value indicates that the function point has a higher priority and importance.

[0096] Finally, the final score of the target performance test requirement is calculated using a weighted average algorithm, which multiplies the score of each function point by its corresponding weight, then adds up all the products and finally divides by the sum of the weights.

[0097] In summary, this embodiment starts with the relevance annotation and model training of the requirement text, gradually screens out a set of similar products through text similarity matching, and matches the function point configuration set based on the test environment configuration and test data of these products. Then, the test indicators are scored, and weighted averaged in combination with the function point priority weights, and finally a comprehensive score of the target performance test requirements is obtained. This process not only improves the accuracy and efficiency of test evaluation, but also provides strong support for performance optimization and risk management through weight allocation and comprehensive data analysis. The entire embodiment shows the complete path from requirements to evaluation results, ensuring the systematic and scientific nature of the testing work.

[0098] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , Figure 2This is a flow chart of the second embodiment of the quality review method for performance test requirements of the present application. After step S20, the quality review method for performance test requirements further includes step S202:

[0099] Step S202, obtaining the indicator range of the indicator corresponding to the similar function point set, comparing and analyzing the expected indicator corresponding to the required function point with the indicator range, and obtaining the analysis result of the expected indicator.

[0100] Specifically, from the set e of similar function points, key indicators of each function point are collected and sorted, and for each indicator in the set e, the maximum value, minimum value, and optionally the average value or median value are calculated as the benchmark value. For example, the "response time" of the input function point is 200 milliseconds, while the maximum value of the "response time" of similar function points in the set e is 300 milliseconds, the minimum value is 100 milliseconds, and the average value is 150 milliseconds.

[0101] If an indicator of an input function point falls between the maximum and minimum values, or is close to the mean / median, the indicator can be considered to be within a reasonable range. In the above example, a response time of 200 milliseconds is within a reasonable range. If the indicator value exceeds the maximum value or is far below the minimum value, a corresponding "too high" or "too low" prompt must be given. If the response time is far more than 300 milliseconds, it indicates that there may be performance issues; if it is far below 100 milliseconds, it may mean over-optimization or other potential problems. For some indicators in set e that do not fully match the input function point, it is necessary to refer to similar cases in the experience library and give possible risk recommendations. For example, if the input function point introduces a new technology or innovation, and there is no similar precedent in set e, it is necessary to propose possible risk points and response strategies based on the application of similar technologies or innovations in the historical experience library.

[0102] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , Figure 3 This is a flow chart of the third embodiment of the quality review method for performance test requirements of the present application. After step S10, the quality review method for performance test requirements further includes step S101:

[0103] Step S101, collecting product text data that needs to be adjusted and optimizing the text similarity model;

[0104] Specifically, since the text similarity model may have errors, users are allowed to perform manual corrections. For each product data with errors, the correspondence between the correct product name provided by the user during manual correction and the original incorrectly identified product name is recorded. These correspondences are collected as new training samples. Subsequently, these newly collected samples are preprocessed, including noise removal, format standardization, etc., to ensure data quality. Next, these new training samples are used to fine-tun the existing text similarity model. During the fine-tuning process, the model adjusts its internal parameters according to the new samples to better learn the subtle differences and similar features between product names. After multiple rounds of iterative training, the recognition accuracy and generalization ability of the model will be improved, thereby reducing the error rate in the future in the stage of obtaining similar product names.

[0105] For example, suppose that in the stage of acquiring similar product names, the model mistakenly identified "Smart Bracelet 4th Generation" as "Smart Watch 4th Generation". After the user discovered this error, he manually corrected it and provided the correct product name "Smart Bracelet 4th Generation". This correction information was recorded by the system and used as a new training sample. Subsequently, the model learned the subtle differences in the names of "bracelet" and "watch" through fine-tuning, so that it can distinguish the two more accurately in future recognitions.

[0106] Step S20 may further include step S201:

[0107] Step S201, collecting text data of function points that need to be adjusted, and optimizing the text similarity model.

[0108] It should be noted that the specific implementation process of step S201 can refer to step S101 and will not be repeated here.

[0109] In addition, the present application also proposes a quality review device for performance test requirements, the quality review device for performance test requirements comprising:

[0110] The first acquisition module 10 is used to screen and match the pre-created experience library based on the target performance test requirements and the text similarity model to obtain a similar product set;

[0111] A second acquisition module 20, configured to perform function point matching on the similar product set based on the data testing module to obtain a similar function point set;

[0112] A first scoring module 30 is used to compare the similar function points with the required function points based on a preset evaluation standard to obtain a score for the required function points;

[0113] The second scoring module 40 is used to obtain the target performance test requirement score according to the score of the required function point and based on the priority weight of the required function point.

[0114] The quality review device for performance test requirements provided by the present application adopts the quality review method for performance test requirements in the above-mentioned embodiment, which can solve the technical problem of how to effectively review performance test requirements, thereby avoiding the risk of delays and failure of the project due to unreasonable requirements. Compared with the prior art, the beneficial effects of the quality review device for performance test requirements provided by the present application are the same as the beneficial effects of the quality review method for performance test requirements provided by the above-mentioned embodiment, and the other technical features in the quality review device for performance test requirements are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0115] The present application provides a quality review device for performance testing requirements, the quality review device for performance testing requirements comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the quality review method for performance testing requirements in the above-mentioned embodiment one.

[0116] Reference below Figure 5 , which shows a schematic diagram of the structure of the quality review device used to implement the performance test requirements of the embodiment of the present application. The quality review device for the performance test requirements in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The quality review equipment for the performance test requirements shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0117] like Figure 5As shown, the quality review device for performance test requirements may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the quality review device for performance test requirements are also stored in RAM1004. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the quality review device for performance test requirements to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a quality review device for performance test requirements with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0118] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0119] The quality review device for performance test requirements provided by the present application adopts the quality review method for performance test requirements in the above-mentioned embodiment, which can solve the technical problem of how to effectively review performance test requirements, thereby avoiding the risk of delays and failure of the project due to unreasonable requirements. Compared with the prior art, the beneficial effects of the quality review device for performance test requirements provided by the present application are the same as the beneficial effects of the quality review method for performance test requirements provided by the above-mentioned embodiment, and the other technical features in the quality review device for performance test requirements are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0120] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0121] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0122] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the quality review method for performance test requirements in the above-mentioned embodiment.

[0123] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0124] The computer-readable storage medium may be included in the quality review device for performance test requirements; or may exist independently without being assembled into the quality review device for performance test requirements.

[0125] Computer program code for performing the operations of the present application may 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 may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0126] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0127] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0128] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the quality review method for the above-mentioned performance test requirements, and can solve the technical problem of how to effectively review the performance test requirements, thereby avoiding the project's progress delays and failure risks due to unreasonable requirements. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the quality review method for the performance test requirements provided by the above-mentioned embodiments, and will not be repeated here.

[0129] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the quality review method required by the above-mentioned performance test.

[0130] The computer program product provided by this application can solve the technical problem of how to effectively review performance test requirements, thereby avoiding the risk of project delays and failures due to unreasonable requirements. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the quality review method for performance test requirements provided by the above-mentioned embodiment, and will not be repeated here.

[0131] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0132] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0133] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, a controlled terminal, or a network device, etc.) to execute the method of each embodiment of the present invention.

[0134] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A quality review method for performance test requirements, characterized in that: The quality review methods for the performance test requirements include: Based on the target performance test requirements and the text similarity model, the pre-created experience library is screened and matched to obtain a set of similar products; Performing function point matching on the similar product set based on the data testing module to obtain a similar function point set; Based on the preset evaluation criteria, the similar function points are compared with the required function points to obtain the scores of the required function points; According to the scores of the required function points, based on the priority weights of the required function points, the target performance test requirement scores are obtained.

2. The quality review method for performance test requirements according to claim 1, characterized in that: The step of screening and matching the pre-created experience library based on the target performance test requirements and the text similarity model to obtain a similar product set includes: Screening the experience library based on target performance test requirements to obtain an adaptation product set; The adapted product set is similarly matched based on a text similarity model to obtain a similar product set.

3. The quality review method for performance test requirements according to claim 2, characterized in that: The step of performing similarity matching on the adapted product set based on the text similarity model to obtain a similar product set comprises: Use the trained text similarity model to output the sentence vectors of the required text and the standard text; Calculating sentence vector similarity between the requirement text and the standard text; The adapted product set is screened according to the sentence vector similarity to obtain the similar product set.

4. The quality review method for performance test requirements according to claim 1, characterized in that: The step of performing function point matching on the similar product set based on the data testing module to obtain a similar function point set comprises: Perform test configuration matching on the similar product set to filter out function point related configuration sets; Using a text similarity model to perform similarity matching on the function point related configuration set to obtain a first similar function point set; The first similar function point set is subjected to test configuration matching and threshold judgment, and combined with preset function point weight values ​​to obtain a similar function point set.

5. The quality review method for performance test requirements according to claim 1, characterized in that: The step of comparing the similar function points with the required function points based on the preset evaluation criteria to obtain the scores of the required function points comprises: Evaluate the test indicator based on the preset evaluation criteria to obtain an evaluation value of the test indicator, where the test indicator is an indicator corresponding to the required function point; The evaluation values ​​of the test indicators are weighted averaged to obtain the scores of the required function points.

6. The quality review method for performance test requirements according to claim 1, characterized in that: After the step of matching the function points of the similar product set based on the data testing module to obtain a similar function point set, the following step further comprises: The indicator range of the indicator corresponding to the similar function point set is obtained, and the expected indicator corresponding to the required function point is compared and analyzed with the indicator range to obtain the analysis result of the expected indicator.

7. The quality review method for performance test requirements according to claim 1, characterized in that: After the step of screening and matching the pre-created experience library based on the target performance test requirements and the text similarity model to obtain a similar product set, the following step further comprises: Collect product text data that needs to be adjusted, and optimize the text similarity model; After the step of matching the function points of the similar product set based on the data testing module to obtain a similar function point set, the following step further comprises: Collect text data of function points that need to be adjusted, and optimize the text similarity model.

8. A quality review device for performance testing requirements, characterized in that: The quality review device required by the performance test includes: The first acquisition module screens and matches the pre-created experience library based on the target performance test requirements and the text similarity model to obtain a similar product set; A second acquisition module performs function point matching on the similar product set based on the data testing module to obtain a similar function point set; A first scoring module compares the similar function points with the required function points based on a preset evaluation standard to obtain a score for the required function points; The second scoring module obtains the target performance test requirement score according to the score of the required function point and based on the priority weight of the required function point.

9. A quality review device for performance testing requirements, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the quality review method for performance test requirements according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the quality review method for performance test requirements according to any one of claims 1 to 7 are implemented.

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