CN113868139A - Method and device for analyzing number making accuracy, electronic equipment and storage medium - Google Patents
Method and device for analyzing number making accuracy, electronic equipment and storage medium Download PDFInfo
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Abstract
The application relates to artificial intelligence, and provides a method, a device, an electronic device and a storage medium for manufacturing accuracy analysis, wherein the method is realized by the following steps: receiving test data of a target business process, wherein the test data of the target business process comprises the test data of each test interface; determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface; and inputting the grading condition of the test data of each test interface into the big data model by taking the grading condition of the test data as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data. By adopting the method of the embodiment of the application, the usability result of the test data is obtained through the big data model, and the test data is used for testing when the test data is available, so that the precision of the test data on the target business process is guaranteed, and the test efficiency of testers is improved.
Description
Technical Field
The present application relates to the field of research and development management technologies, and in particular, to a method and an apparatus for manufacturing accuracy analysis, an electronic device, and a storage medium.
Background
The manufacture, that is, the generation of test data, is an essential important link in the test work, and the accuracy of the test data plays a decisive role in the test work, if the test data is found to be not in accordance with the expected expectation only at the later stage of the test flow, the serious waste of test resources will be caused, and therefore, the importance of the manufacture accuracy is particularly obvious.
The availability result of the test data generated by the number making tool commonly used in the market at present is unstable, and is generally only suitable for a test scene with small service data volume, but for a test scene with large service data volume, such as a multi-party joint debugging test or a test scene with more test interfaces, etc., a tester needs to make the number making and then test again for many times because the test data is unavailable, so that the efficiency of the test work is low.
Disclosure of Invention
The embodiment of the application provides a method and a device for analyzing the number making accuracy, electronic equipment and a storage medium.
In a first aspect, an embodiment of the present application provides a method for manufacturing accuracy analysis, where the method includes:
receiving test data of a target business process, wherein the test data of the target business process comprises the test data of each test interface;
determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface;
and inputting the grading condition of the test data of each test interface into the big data model by taking the grading condition of the test data as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data.
In a second aspect, an embodiment of the present application provides an apparatus for manufacturing accuracy analysis, where the apparatus includes:
the receiving unit is used for receiving the test data of the target business process, and the test data of the target business process comprises the test data of each test interface;
the determining unit is used for determining the grading condition of the test data of each test interface according to the preset grading standard corresponding to the test data of each test interface;
and the input unit is used for inputting the grading condition of the test data of each test interface into the big data model as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data.
In a third aspect, embodiments of the present application provide an electronic device, which includes a processor, a memory, and computer executable instructions stored on the memory and executable on the processor, and when the computer executable instructions are executed, the electronic device is caused to perform some or all of the steps described in any one of the methods of the first aspect of the embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon computer instructions, which, when executed on a communication apparatus, cause the communication apparatus to perform some or all of the steps as described in any one of the methods of the first aspect of the embodiments of the present application.
In a fifth aspect, the present application provides a computer program product, where the computer program product includes a computer program operable to cause a computer to perform some or all of the steps as described in any one of the methods of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
It can be seen that, in the embodiment of the present application, by receiving the test data of the target service process, the test data of the target service process includes the test data of each test interface; determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface; and inputting the grading condition of the test data of each test interface into the big data model by taking the grading condition of the test data as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data. By adopting the method of the embodiment of the application, the usability result of the test data is obtained through the big data model, and the test data is used for testing when the test data is available, so that the precision of the test data on the target business process is guaranteed, and the test efficiency of testers is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1A is a structural deployment diagram of a test system according to an embodiment of the present application;
fig. 1B is a schematic flow chart of a method for manufacturing accuracy analysis according to an embodiment of the present application;
FIG. 1C is a schematic structural diagram of a big data model provided by an embodiment of the present application;
FIG. 1D is a diagram illustrating the structural deployment of a test system based on manufacturing accuracy analysis, as applied to an embodiment of the present application;
fig. 2A is a schematic diagram illustrating an example of a method for manufacturing accuracy analysis according to an embodiment of the present disclosure;
fig. 2B is a schematic diagram illustrating an example of a method for manufacturing accuracy analysis according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an apparatus for manufacturing accuracy analysis according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a server in a hardware operating environment of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps is not limited to only those steps recited, but may alternatively include other steps not recited, or may alternatively include other steps inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The apparatus according to the embodiments of the present application will be described with reference to the accompanying drawings.
Fig. 1A is a structural deployment diagram of a test system according to an embodiment of the present application. As shown in fig. 1A, the system includes a test flow module, a test number module, and a test execution module.
The system comprises a test flow module, a test interface module and a data processing module, wherein the test flow module is used for determining each test interface in a target business flow by a tester according to the received target business flow;
the system comprises a test configuration module, a test configuration module and a data processing module, wherein the test configuration module is used for generating test data of a target business process by a tester according to the target business process, the test data of the target business process comprises the test data aiming at each test interface, and can also be used for determining the grading condition of the test data of each test interface according to a preset grading standard and determining whether to test according to whether the grading condition is good or not;
the system comprises a test execution module, a data processing module and a data processing module, wherein the test execution module is used for testing a target business process by using test data of the target business process by a tester;
in the testing process of the system, the test data of the target business process is generated according to the target business process, and the grading condition of the test data of each test interface is determined according to the preset grading standard, which are artificially determined by the tester, so that the usability result of the test data cannot be accurately predicted according to the actual condition of the target business process, and the testing process has a large failure risk, which leads to low efficiency of testing work.
Accordingly, an embodiment of the present application provides a manufacturing accuracy analysis method, please refer to fig. 1B, where fig. 1B is a schematic flow chart of the manufacturing accuracy analysis method provided in the embodiment of the present application, and as shown in fig. 1B, the method includes the following steps:
101: and receiving test data of the target business process, wherein the test data of the target business process comprises the test data of each test interface.
In the method, the test data is generated, for example, in a certain test interface, customer information needs to be submitted to the test interface for approval, and then test data including customer information such as applicant name, mobile phone number, identification number, bank card number, etc. needs to be generated for test work.
The target business process comprises a plurality of test interfaces according to the process execution sequence.
Illustratively, the target business process is a financial loan process, and the target business process comprises process nodes in the process execution sequence, such as client login → client information submission → financial institution approval → client and financial institution signing, and each process node corresponds to a test interface.
The test data may be in the form of data including a plurality of field parameters, or in the form of data including only a response command for a certain behavior.
For example, if the test data for a certain test interface is customer information data, the test data includes field parameters such as 'applicant name', 'mobile phone number', 'bank card number', 'loan amount', and the like.
For example, if the test data for a certain test interface is an agreement instruction for a certain behavior, the test data only includes an 'agreement' response instruction.
The test interface refers to an interactive interface where data interaction behavior occurs to test data, and the data interaction behavior includes interaction behaviors of sending the test data from a first page to a second page, changing the test data from a first format to a second format, sending the test data from an internal system to an external system, and the like.
102: and determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface.
In a specific implementation, the preset scoring standard may be obtained by manually setting the scoring standard in advance according to the requirement of each test interface for the test data before the test work, and the setting of the scoring standard may be performed according to a conventional data format of the test data as a reference scoring standard.
Illustratively, the test data for a certain test interface is customer information data, and the test data includes 10 field parameters such as ' applicant name ' mobile phone number ', and the score of each field parameter is a score between 0 and 10, as shown in table 1, the content of the ' applicant name ' field parameter is 2 or more Chinese characters without obscure word with a score of 10, 2 or more Chinese characters with obscure word or special symbol with a score of 1 to 9, and a score of 1 or less Chinese characters with a score of 0, the content of the ' mobile phone number ' field parameter is 11 digits and shows a score of 10 when the mobile phone number is arranged consecutively, a score of 11 digits with special symbol between the digits of 1 to 9, and a score of less than 11 digits with a score of 0, and the other field parameters can be set for the scoring standard referring to the conventional field parameter data format, and thus will not be described in detail herein. After the corresponding scores of the 10 field parameters are determined according to the preset scoring standard, the corresponding scores of the 10 field parameters are summed, and the scoring condition of the test data of the test interface is determined to be the corresponding score of the summation result.
TABLE 1
It should be noted that the preset scoring standard shown in table 1 is only an example of a preset scoring standard, and in a specific application, the preset scoring standard corresponding to the test data may also exist in other standard forms, or may be set and adjusted in real time according to actual requirements of the target business process.
103: and inputting the grading condition of the test data of each test interface into the big data model by taking the grading condition of the test data as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data.
The test data are available and represent that the test data can support and complete the process test work of the target business process; if the test data is unavailable, the test data cannot support the process test work of completing the target business process, and if the unavailable test data is used for testing, the test progress will be influenced.
The big data model can comprise an input layer, an analysis layer and an output layer.
For example, referring to fig. 1C, fig. 1C is a schematic structural diagram of a big data model provided in an embodiment of the present application, and as shown in fig. 1C, the big data model includes the following layers:
the input layer is used for receiving the grading condition of the test data of each test interface, normalizing the grading condition of the test data of each test interface, eliminating the dimension of the grading condition and obtaining the grading condition of the test data of each test interface after preliminary treatment;
the analysis layer is used for performing feature extraction on the grading condition of the primarily processed test data of each test interface to obtain the grading condition feature of the test data of each test interface, exemplarily, the grading condition feature comprises that the grading condition of the test data of a certain test interface is that the grading score is lowest/highest in the grading conditions of the test data of all the test interfaces, and the total grade of the test data is obtained according to the grading condition feature of the test data of each test interface; as another example, a logistic regression algorithm for calculating the score of the test data of each test interface may be further included in the analysis layer.
And the output layer is used for obtaining the availability result of the test data according to whether the total score of the test data reaches the preset total score.
It should be noted that the big data model shown in fig. 1C is only an example of a big data model, and in a specific application, the big data model may exist in other hierarchical forms.
The apparatus according to the embodiments of the present application will be described with reference to the accompanying drawings.
Referring to fig. 1D, fig. 1D is a structural deployment diagram of a test system based on manufacturing accuracy analysis, as shown in fig. 1D, the system includes a data receiving module, a score determining module, an availability module, and a test executing module. The first end of the system is connected with a tester terminal, wherein the function of each module can be realized by a single server, or the functions of a plurality of modules can be realized by one server. And a plurality of servers realizing the functions of different modules are mutually communicated and connected. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), and a big data and artificial intelligence platform.
The data receiving module is used for receiving the test data of the target business process according to the test requirement instruction of the tester terminal to the target business process and sending the test data of the target business process to the grading determining module, wherein the test data of the target business process comprises the test data of each test interface.
The score determining module is used for receiving the test data of the target business process, determining the score condition of the test data of each test interface according to the preset score standard corresponding to the test data of each test interface, and sending the score condition to the availability module.
The availability module is used for receiving the grading condition of the test data of each test interface and inputting the grading condition as an input item into the big data model to obtain an availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data.
And the test execution module is used for testing the target business process by using the test data when the availability result of the test data shows that the test data is available.
The tester terminal is used for setting a preset grading standard corresponding to the test data of each test interface, and is also used for testing a target business process by using the test data when the availability result of the test data shows that the test data is available, or enabling the data receiving module to receive new test data of the target business process when the availability result of the test data shows that the test data is unavailable, so as to fulfill the aim of testing the target business process. The terminal comprises a mobile phone, a tablet computer, a personal digital assistant, wearable equipment and the like.
Illustratively, the target business process is a financial loan process, a tester terminal and a financial institution server perform joint debugging test, the target business process comprises a test interface 1 and a test interface 2, the test interface 1 is used for the tester terminal to submit customer information data, the test interface 2 is used for determining whether to sign a financial loan product according to a signing agreement result returned by the financial institution server, the returned result is a result made by the financial institution server according to the content of the customer information, and the scoring condition of the test data of each test interface is 0-100 minutes. The test data of the target business process includes customer information data for the test interface 1 and an agreement contract instruction for the test interface 2, wherein the customer information data includes' applicant name: a mobile phone number: a ·,' bank card number: (ii) an amount of loan: 10 field parameters such as ' applicant name ' field parameter is 10 points, ' mobile phone number ' field parameter is 8 points, ' bankcard number ' field parameter is 10 points, ' loan amount ' field parameter is 10 points ' score … …, the score of the 10 field parameters is determined to be 98 points, the score of the agreement signing instruction of the test interface 2 is 100 points, the score of the customer information data of the test interface 1 and the score of the agreement signing instruction of the test interface 2 are input into a big data model as input items, the result of the obtained test data is that the test data is usable, and the test data can support and complete the process test work of the target business process, therefore, the tester terminal uses the test data to complete joint debugging test work with the financial institution server.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
It can be seen that, in the embodiment of the present application, by receiving the test data of the target service process, the test data of the target service process includes the test data of each test interface; determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface; and inputting the grading condition of the test data of each test interface into the big data model by taking the grading condition of the test data as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data. By adopting the method of the embodiment of the application, the usability result of the test data is obtained through the big data model, and the test data is used for testing when the test data is available, so that the precision of the test data on the target business process is guaranteed, and the test efficiency of testers is improved.
In one possible example, the target business process corresponds to one of multiple business scenarios, and the multiple business scenarios are divided according to the types of customers and/or products; the big data model corresponds to a business scenario of the target business process.
In a specific implementation, the big data model may include a plurality of big data submodels (big data submodel 1, big data submodel 2 … … big data submodel M), please refer to fig. 2A, where fig. 2A is an exemplary schematic diagram of a method for analyzing modeling accuracy according to an embodiment of the present application, as shown in fig. 2A, the big data submodel 1 corresponds to a service scenario 1, and the big data submodel 2 corresponds to a service scenario 2, and the big data submodel M … … corresponds to the service scenario M. The test data of the target business process comprises test data for each test interface, the grading condition of the test data of each test interface is used as an input item to be input into the big data model, the big data model can distinguish a target big data sub-model corresponding to the target business process according to the business scene of the target business process, and the target big data sub-model obtains the availability result of the test data for the business scene according to the business scene and the preset grading standard corresponding to the test data of each test interface. Therefore, the availability result of the test data is obtained on the basis of the corresponding service scene, and the availability result of the test data has higher accuracy.
Illustratively, the target business process is a financial loan process, then the client types include new clients, old clients, VIP-level clients, and the like, and the product types include personal loan products, enterprise loan products, and the like.
As another example, if the service scenario 1 is a new customer with a customer type and the product type is a personal loan product, since the new customer does not have reserved customer information, the big data model corresponding to the service scenario 1 has a high requirement on the customer information data included in the test data, and the availability result of the test data is determined to be available only when the customer information data has the highest integrity; on the contrary, if the type of the 2-bit customer in the service scene is an old customer and the type of the product is a personal loan product, the old customer does not influence the old customer to complete the financial loan service because the old customer has reserved customer information, if unnecessary information such as a zip code of the old customer is missing in the customer information data included in the test data, so that the requirement of the big data model corresponding to the service scene 2 on the customer information data included in the test data is low, and if the customer information data does not have the highest integrity and the missing information data is the unnecessary information, the availability result of the test data is determined to be available.
It can be seen that, in the embodiment of the present application, the big data model corresponds to a service scenario of a target service process, so that for each test data with the same rating and different service scenarios, the big data model obtains different availability results for each test data according to different service scenarios, and the service scenarios are divided according to client types and/or product types.
In one possible example, the preset scoring criteria include a binarization scoring criterion and an interval continuous scoring criterion, and the binarization scoring criterion is that the score of the test data at the corresponding test interface is 0 or 1; and the interval continuous scoring standard is a certain fraction of the test data between 0 and N in the corresponding test interface, wherein N is greater than 0.
Illustratively, in the binarization scoring standard, if the test data is not available in the corresponding test interface, the score is 0; otherwise, if the test data is available in the corresponding test interface, the score is 1.
Illustratively, in the interval continuity score criterion, if N is 100, the score of the test data at the corresponding test interface is a certain score from 0 to 100.
In the embodiment of the application, the preset scoring standard comprises a binarization scoring standard and an interval continuous scoring standard, so that when the scoring condition of the test data of each test interface is determined according to the preset scoring standard corresponding to the test data of each test interface, the flexibility of scoring is realized, the scoring condition of the test data of each test interface is more consistent with the actual condition of the target business process, the precision of the test data on the target business process is ensured, and the test efficiency of a tester is improved.
In a possible example, the determining, according to the preset scoring standard corresponding to the test data of each test interface, the scoring condition of the test data of each test interface specifically includes: determining a reference score corresponding to the test data of each test interface according to a preset score standard corresponding to the test data of each test interface; acquiring whether an interface updating behavior exists in each test interface within a preset time period from the current time, if so, determining a score adjusting value of the corresponding test interface according to the interface updating behavior, wherein the interface updating behavior comprises the improvement of data compatibility or the reduction of data compatibility; if the interface updating behavior is to improve the data compatibility, the score adjusting value of the corresponding test interface is greater than 1; if the interface updating behavior is to reduce the data compatibility, the score adjusting value of the corresponding test interface is less than 1; and determining the grading condition of the test data of each test interface according to the grade regulating value and the reference grade of each test interface, wherein the grade condition is the grade regulating value and the reference grade.
For example, the data compatibility is improved, and the types of the test data formats which can be processed by the test interface are increased; reducing data compatibility may be reducing the types of test data formats that the test interface is capable of handling.
In the embodiment of the application, when the interface updating behavior exists in the corresponding test interface within the preset time period from the current time, the score adjusting value of the corresponding test interface is determined according to the interface updating behavior, and then the scoring condition of the test data of each test interface is determined according to the score adjusting value and the reference score of each test interface, so that when the interface updating behavior is performed on the test interface but the preset scoring standard corresponding to the test data of each test interface is not updated, the reference score of the test data of each test interface can be adjusted according to the specific type of the interface updating behavior, the scoring condition conforming to the interface updating behavior is obtained, and the accuracy of the availability result of the test data is further ensured.
In one possible example, the training process of the big data model is as follows: acquiring a training data set, wherein the training data set comprises target test data of each target test interface; determining the grading condition of the test data of each target test interface according to a preset grading standard corresponding to the test data of each target test interface; inputting the grading condition of the target test data in each target test interface into the initial data model to obtain the availability result of the target test data; matching the availability result with the historical test result, if the matching is successful, determining that the initial data model is successfully predicted, and if the matching is unsuccessful, determining that the initial data model is unsuccessfully predicted; determining the prediction success rate of the initial data model, wherein the prediction success rate represents the proportion of target test data which is successfully predicted in the training data set to the training data set; if the prediction success rate is lower than the first preset probability, adjusting model parameters in the initial data model to obtain a new data model, and repeatedly inputting the grading condition of the target test data in each target test interface into the new data model to obtain the prediction success rate of the new data model; and determining the new data model as a big data model until the prediction success rate of the new data model is greater than or equal to the first preset probability.
In a specific implementation, a scoring weight may be given to each target test interface, so as to calculate a total score of the target test data according to the scoring weight of each target test interface and the scoring condition of the target test data in each target test interface, and further determine the availability result of the target test data by whether the total score reaches a preset total score; the usability of the target test data can also be determined by analyzing the scoring characteristic values of the target test data in each target test interface, for example, by the scoring characteristic values such as the minimum scoring value, the maximum scoring value and the like in the scoring condition of each target test interface, so as to obtain the usability result of the target test data.
It can be seen that, in the training process of the big data model provided in the embodiment of the present application, only if it is determined that the ratio of the target test data successfully predicted in the training data set to the training data set is greater than or equal to the first preset probability, and the new data model including the adjusted model parameters is used as the big data model, the accuracy of the usability result of the big data model for predicting the test data can be ensured. Furthermore, the usability result of the test data is predicted by using the big data model obtained through training in the training process provided by the embodiment of the application, so that the accuracy of the test data on the target business process can be guaranteed while the manpower is liberated, the time cost and the resource cost of a tester are reduced, and the test efficiency of the tester is improved.
In one possible example, the model parameters in the initial data model include a scoring weight for each target test interface, and the above-mentioned inputting the scoring condition of the target test data in each target test interface into the initial data model to obtain the usability result of the target test data includes: inputting the scoring condition of the target test data in each target test interface into an initial data model, calling the initial data model to perform result reasoning, wherein the result reasoning comprises determining the total scoring of the target test data based on the scoring condition of the target test data in each target test interface and model parameters, and outputting an availability result of the target test data according to whether the total scoring is greater than a first preset total scoring or not; wherein, the adjusting of the model parameters in the initial data model includes: and adjusting the scoring weight of each target test interface.
The scoring weight of each target test interface is adjusted, so that the ratio of target test data successfully predicted in the training data set to the training data set is larger than or equal to a first preset probability by adjusting model parameters in the initial data model.
The result reasoning includes determining a total score of the target test data based on the score condition of the target test data in each target test interface and the model parameter, and outputting an availability result of the target test data according to whether the total score is greater than a first preset total score, and in a specific implementation, the total score may be:
total score of target test data ═ c (
The scoring weight of the target test interface i is the scoring condition of the target test data in the target test interface i), and the availability result of the target test data is determined by whether the total score reaches the preset total score, wherein N is the total number of the target test interfaces, and i is more than or equal to 1 and less than or equal to N.
For example, please refer to fig. 2B, where fig. 2B is a schematic diagram illustrating an example of a method for analyzing a manufacturing accuracy provided in an embodiment of the present application, as shown in fig. 2B, there are 3 target test interfaces including a target test interface 1, a target test interface 2, and a target test interface 3, an initial data model assigns a score weight α to the target test interface 1, a score weight β to the target test interface 2, and a score weight δ to the target test interface 3, and according to a preset score standard corresponding to test data of each target test interface, it is determined that score conditions of the test data of the target test interfaces 1-3 are A, B and C, respectively, and according to the score weight of each target test interface and the score condition of the target test data in each target test interface, it is determined that a total score of the target test data is a score weight of the target test interface 1 + a score of the target test interface 2 of the target test interface 1 The scoring condition of the target test data at the target test interface 2 + the scoring weight of the target test interface 3 + the scoring condition of the target test data at the target test interface 3 ═ a + β + B + δ C, if the total scoring of the target test data does not reach a preset total scoring, determining that the availability result of the target test data is that the target test data is unavailable, matching the availability result with the historical test result of the target test data, if the matching fails, determining that the initial data model fails to predict, adjusting the scoring values α, β and δ of the target test interfaces 1 to 3, and obtaining the scoring weights of the adjusted target test interfaces 1 to 3 as α ', β ' and δ ', respectively, and recalculating the total scoring of the adjusted target test data ═ a + β ' + B + δ '/C, if the total scoring of the adjusted target test data is greater than the preset total scoring, determining that the availability result of the target test data is available, and determining that the matching result of the availability result and the historical test result of the target test data is successful, and determining that the initial data model is successful in prediction.
It can be seen that, in the embodiment of the application, a score weight is given to each target test interface, and a total score for determining the availability result of the target test data is determined according to the score weight of each target test interface and the score condition of the target test data at each target test interface, so that the score weights of different target test interfaces can be adjusted according to actual conditions in the training process of the big data model, and the prediction success rate of the big data model for the target test data is further improved.
In a possible example, if the prediction success rate is lower than the first preset probability, the adjusting the model parameters in the initial data model specifically includes: analyzing each target test interface, and determining the association degree between each target test interface and the next test interface; determining a weak association test interface according to the association degree between each target test interface and the next test interface, wherein the weak association test interface is a test interface of which the association degree with the next test interface is smaller than the preset association degree; and adjusting the score weight value corresponding to the weak association test interface to be 0.
In a specific implementation, only the remaining target test interfaces are reserved by independently removing a certain target test interface for multiple times, and the association between the removed target test interface and the next test interface is determined according to whether the remaining target test interfaces can complete the test process.
Exemplarily, if a remaining target test interface after a certain target test interface is removed can complete the test process, the association degree between the removed certain target test interface and the next test interface is low association degree, if the association degree is lower than the preset association degree, the score weight corresponding to the test interface is adjusted to 0, otherwise, if the target service process cannot be completed, the association degree is high.
It can be seen that, in the embodiment of the present application, when the prediction success rate is lower than the first preset probability, the score weight corresponding to the weak association test interface whose association between the weak association test interface and the next test interface is smaller than the preset association is adjusted to 0, so that in the training process of the big data model, when the model parameter in the initial data model is adjusted, the weak association test interface can be prevented from affecting the availability result of the target test data, thereby improving the prediction success rate, and improving the training efficiency in the training process.
In one possible example, the training process further includes: acquiring historical test data of each target test interface; acquiring average response time and data processing capacity corresponding to each target test interface according to historical test data of each target test interface; determining the interface performance of each target test interface according to the average response time and the data processing amount corresponding to each target test interface; sequencing the interface performance of each target test interface according to the size sequence to obtain a sequenced target test interface; wherein, adjusting the scoring weight of each target test interface comprises: and on the basis of determining that the grading weight value relationship of each target test interface corresponds to the interface performance order, regulating the grading weight value of each target test interface.
The average response time refers to the average time consumed from the time when the target test interface issues the data processing request until the last data is processed.
The data processing amount can be measured by units such as data capacity processing size, transaction processing number, page processing number, query processing number and the like from the service perspective; the number of bytes of data can be measured from a network perspective.
The interface performance of each target test interface is determined according to the average response time and the data processing amount corresponding to each target test interface, and in a specific implementation, the interface performance may be: the interface performance of the target test interface is data throughput/average response time.
Illustratively, the interface performance of the target test interface is data throughput/average response time, and there are 3 target test interfaces including the target test interface 1, the target test interface 2 and the target test interface 3, where the average response time 1 of the target test interface 1 is less than the average response time 2 of the target test interface 2 is less than the average response time 3 of the target test interface 3, and the data throughput 1 of the target test interface 1 is greater than the data throughput 2 of the target test interface 2 is greater than the data throughput 3 of the target test interface 3, and then the interface performance 1 of the target test interface 1 is calculated as data throughput 1/average response time 1, the interface performance 2 of the target test interface 2 is data throughput/2/average response time 2 and the interface performance 3 of the target test interface 3 is data throughput 3/average response time according to the interface performance of the target test interface being data throughput/average response time And 3, and it can be seen that the interface performance 1 of the target test interface 1 is greater than the interface performance 2 of the target test interface 2 is greater than the interface performance 3 of the target test interface 3, so that, in the process of adjusting the score weight of each target test interface, the score weight 1 of the target test interface 1 is greater than the score weight 2 of the target test interface 2 is greater than the score weight 3 of the target test interface 3, that is, the score weight magnitude relation of each target test interface corresponds to the interface performance magnitude sequence of each target test interface.
It can be seen that, in the embodiment of the present application, when the score weight of each target test interface is adjusted in the training process of the big data model, the score weight of each target test interface is adjusted on the basis of determining that the size relationship of the score weight of each target test interface corresponds to the size sequence of the interface performance, so that the interface performance of each target test interface can be used as a reference basis in the process of adjusting the score weight of each target test interface, the risk of blind adjustment when the score weight of each target test interface is adjusted is avoided, and the training efficiency of the big data model is improved while the prediction success rate of the big data model for the target test data is improved.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an apparatus for manufacturing accuracy analysis according to an embodiment of the present application, as shown in fig. 3:
an apparatus for manufacturing accuracy analysis, said apparatus comprising:
301: the receiving unit is used for receiving the test data of the target business process, and the test data of the target business process comprises the test data of each test interface;
302: the determining unit is used for determining the grading condition of the test data of each test interface according to the preset grading standard corresponding to the test data of each test interface;
303: and the input unit is used for inputting the grading condition of the test data of each test interface into the big data model as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data.
It can be seen that, in the embodiment of the present application, the test data of the target service process is received by the receiving unit, where the test data of the target service process includes test data of each test interface; determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface through a determining unit; and inputting the grading condition of the test data of each test interface into the big data model by using the input unit as an input item to obtain the usability result of the test data, wherein the usability result comprises the availability or unavailability of the test data. By adopting the device provided by the embodiment of the application, the usability result of the test data is obtained through the big data model, and the test data is used for testing when the test data is available, so that the precision of the test data on the target business process is guaranteed, and the test efficiency of testers is further improved.
Specifically, in the embodiment of the present application, the manufacturing accuracy analysis apparatus may be divided into the functional units according to the above method example, for example, each functional unit may be divided for each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Consistent with the embodiment shown in fig. 1B, an electronic device is provided in the embodiment of the present application, please refer to fig. 4, fig. 4 is a schematic diagram illustrating a server structure of a hardware operating environment of an electronic device provided in the embodiment of the present application, and as shown in fig. 4, the electronic device includes a processor, a memory, and computer-executable instructions stored in the memory and executable on the processor, and when the computer-executable instructions are executed, the electronic device executes the instructions including the steps of any one of the manufacturing accuracy analysis methods.
Wherein the processor is a CPU.
The memory may be a high-speed RAM memory, or may be a stable memory, such as a disk memory.
Those skilled in the art will appreciate that the configuration of the server shown in fig. 4 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 4, the memory may include an operating system, a network communication module, and computer-executable instructions for manufacturing accuracy analysis. The operating system is used for managing and controlling hardware and software resources of the server and supporting the operation of executing instructions by the computer. The network communication module is used for realizing communication between each component in the memory and communication with other hardware and software in the server, and the communication may use any communication standard or protocol, including but not limited to GSM (Global System of Mobile communication), GPRS (General Packet Radio Service), CDMA2000(Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access), TD-SCDMA (Time Division-Synchronous Code Division Multiple Access), etc.
In the server shown in fig. 4, the processor is configured to execute computer-executable instructions for personnel management stored in the memory, and to implement the following steps: receiving test data of a target business process, wherein the test data of the target business process comprises the test data of each test interface; determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface; and inputting the grading condition of the test data of each test interface into the big data model by taking the grading condition of the test data as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data.
For specific implementation of the server according to the present application, reference may be made to the above embodiments of the method for analyzing the manufacturing accuracy, which are not described herein again.
An embodiment of the present application provides a computer-readable storage medium, in which computer instructions are stored, and when the computer instructions are executed on a communication apparatus, the communication apparatus is caused to perform the following steps: receiving test data of a target business process, wherein the test data of the target business process comprises the test data of each test interface; determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface; and inputting the grading condition of the test data of each test interface into the big data model by taking the grading condition of the test data as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data. The computer includes an electronic device.
The electronic terminal equipment comprises a mobile phone, a tablet computer, a personal digital assistant, wearable equipment and the like.
The computer-readable storage medium may be an internal storage unit of the electronic device described in the above embodiments, for example, a hard disk or a memory of the electronic device. The computer readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the electronic device. Computer-readable storage media are used to store computer-executable instructions and data as well as other computer-executable instructions and data needed by electronic devices. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
For specific implementation of the computer-readable storage medium according to the present application, reference may be made to the embodiments of the method for analyzing manufacturing accuracy, which are not described herein again.
Embodiments of the present application provide a computer program product, wherein the computer program product comprises a computer program operable to cause a computer to perform some or all of the steps of any one of the manufacturing accuracy analysis methods as described in the above method embodiments, and the computer program product may be a software installation package.
It should be noted that any of the above embodiments of the manufacturing accuracy analysis method are described as a series of actions for simplicity of description, but those skilled in the art should understand that the present application is not limited by the described action sequence, because some steps can be performed in other sequences or simultaneously according to the present application. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
The above embodiments are described in detail, and the principles and embodiments of the method, apparatus, electronic device and storage medium for manufacturing accuracy analysis according to the present application are described herein by using specific examples, and the description of the above embodiments is only used to help understand the method and core ideas of the present application; meanwhile, for those skilled in the art, the idea of the method, the apparatus, the electronic device and the storage medium for manufacturing accuracy analysis according to the present application may be changed in the specific implementation and application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, hardware products and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. The memory may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
While the present application has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a review of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
One of ordinary skill in the art will appreciate that all or part of the steps in the various methods of any of the above method embodiments of the method for manufacturing accuracy analysis may be performed by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
It is apparent that those skilled in the art can make various changes and modifications to the manufacturing accuracy analysis method, apparatus, electronic device, and storage medium provided herein without departing from the spirit and scope of the present application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A method of manufacturing accuracy analysis, the method comprising:
receiving test data of a target business process, wherein the test data of the target business process comprises the test data of each test interface;
determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface;
and inputting the grading condition of the test data of each test interface into a big data model by taking the grading condition of the test data as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data.
2. The method of claim 1, wherein the target business process corresponds to one of a plurality of business scenarios, and the plurality of business scenarios are divided according to a client type and/or a product type; the big data model corresponds to a business scenario of the target business process.
3. The method according to claim 1 or 2, wherein the preset scoring criteria include a binarization scoring criteria and an interval continuity scoring criteria,
the binarization scoring standard is that the score of the test data at the corresponding test interface is 0 or 1;
the interval continuous scoring standard is a certain score of the test data between 0 and N in the corresponding test interface, wherein N is greater than 0.
4. The method of claim 1, wherein the big data model is trained as follows:
obtaining a training data set, wherein the training data set comprises target test data of each target test interface;
determining the grading condition of the target test data of each target test interface according to a preset grading standard corresponding to the target test data of each target test interface;
inputting the scoring condition of the target test data in each target test interface into an initial data model to obtain the availability result of the target test data;
matching the availability result with the historical test result, if the matching is successful, determining that the initial data model is successfully predicted, and if the matching is unsuccessful, determining that the initial data model is failed to predict;
determining the prediction success rate of the initial data model, wherein the prediction success rate represents the proportion of target test data which is successfully predicted in the training data set;
if the prediction success rate is lower than a first preset probability, adjusting model parameters in the initial data model to obtain a new data model, and repeatedly inputting the scoring condition of the target test data in each target test interface into the new data model to obtain the prediction success rate of the new data model;
and determining the new data model as the big data model until the prediction success rate of the new data model is greater than or equal to a first preset probability.
5. The method of claim 4, wherein the model parameters in the initial data model include a scoring weight for each of the target test interfaces, and the entering of the scoring condition of the target test data in each of the target test interfaces into the initial data model results in the availability result of the target test data comprises:
inputting the scoring condition of the target test data in each target test interface into the initial data model, calling the initial data model to perform result reasoning, wherein the result reasoning comprises determining the total score of the target test data based on the scoring condition of the target test data in each target test interface and the model parameters, and outputting the availability result of the target test data according to whether the total score is greater than a first preset total score;
wherein the adjusting model parameters in the initial data model comprises:
and adjusting the scoring weight of each target test interface.
6. The method according to claim 5, wherein if the prediction success rate is lower than a first preset probability, adjusting the model parameters in the initial data model, specifically:
analyzing each target test interface, and determining the association degree between each target test interface and the next test interface;
determining a weak association test interface according to the association degree between each target test interface and the next test interface, wherein the weak association test interface is a test interface of which the association degree with the next test interface is smaller than the preset association degree;
and adjusting the score weight value corresponding to the weak correlation test interface to be 0.
7. The method of claim 5, wherein the training process further comprises:
acquiring historical test data of each target test interface;
acquiring average response time and data processing capacity corresponding to each target test interface according to historical test data of each target test interface;
determining the interface performance of each target test interface according to the average response time and the data processing amount corresponding to each target test interface;
sequencing the interface performance of each target test interface according to the size sequence to obtain a sequenced target test interface;
wherein, the adjusting the scoring weight of each target test interface includes:
and on the basis of determining that the grading weight value relationship of each target test interface corresponds to the interface performance sequence, regulating the grading weight value of each target test interface.
8. An apparatus for manufacturing accuracy analysis, the apparatus comprising:
the system comprises a receiving unit, a processing unit and a processing unit, wherein the receiving unit is used for receiving test data of a target business process, and the test data of the target business process comprises test data of each test interface;
the determining unit is used for determining the grading condition of the test data of each test interface according to a preset grading standard corresponding to the test data of each test interface;
and the input unit is used for inputting the grading condition of the test data of each test interface into a big data model by taking the grading condition of the test data as an input item to obtain the availability result of the test data, wherein the availability result comprises the availability or unavailability of the test data.
9. An electronic device comprising a processor, a memory, and computer-executable instructions stored on the memory and executable on the processor, which when executed cause the electronic device to perform the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon computer instructions which, when run on a communication device, cause the communication device to perform the method of any one of claims 1-7.
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