Front-end reverse abnormal data robustness detection method and device and storage medium

Abstract

The application relates to the technical field of data processing, and provides a front-end reverse abnormal data robustness detection method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring front-end request information; creating a data test case, wherein the data test case is used for feeding back interface return parameters to a test interface and filling a field assertion for data on a front-end page, the interface return parameters are obtained by performing correlation analysis and processing on the front-end request information; performing matching processing on the interface return parameters and the data filling field to obtain a reverse verification file; creating a data detection project, and adding preset abnormal values in a configuration file; generating a reverse abnormal return value case according to the reverse verification file and the configuration file; and running the abnormal return value case in the data detection project to obtain a data robustness detection result. Through the above technical scheme, front-end reverse abnormal data robustness detection processing of the financial industry can be realized, and safety is improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to, but are not limited to, the technical field of data detection, and in particular to a front-end reverse abnormal data robustness detection method and device, an electronic device, and a computer readable storage medium. BACKGROUND

[0002] Robustness testing is a basic dimension of test design in the process of software testing, but in the existing agile development engineering mode, it is gradually marginalized because of its low priority and great verification difficulty; at present, for the display of front-end data in the financial industry, it is usually verified only in the case of abnormal input, which belongs to the positive coverage case; but for the reverse scene of abnormal return, it is impossible to predict whether the actual display result of the front-end can meet the expectation, and there is a lack of special verification of reverse abnormal return value, which leads to insufficient robustness of data and brings safety hazards to users. SUMMARY

[0003] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0004] In order to solve the problems mentioned in the background art, the embodiments of the present application provide a front-end reverse abnormal data robustness detection method, device, electronic device and computer readable storage medium, which can realize the front-end reverse abnormal data robustness detection processing of the financial industry and improve the security.

[0005] In a first aspect, the embodiments of the present application provide a front-end reverse abnormal data robustness detection method, which comprises:

[0006] Obtaining front-end request information;

[0007] Creating a data test case, wherein the data test case is used to feed back interface return parameters to a test interface and fill in field assertions for data on a front-end page, and the interface return parameters are obtained by associating and analyzing the front-end request information;

[0008] Matching the interface return parameters with the data filling fields to obtain the reverse verification file;

[0009] Creating a data detection project and adding preset abnormal values in the configuration file of the data detection project;

[0010] Generating a reverse abnormal return value case according to the reverse verification file and the configuration file;

[0011] Running the abnormal return value case in the data detection project to obtain a data robustness detection result.

[0012] According to some embodiments of the present application, the data detection project is a simulation Mock baffle project, and the creating a data detection project comprises:

[0013] A first network address is configured for a preset data detection device, and a second network address is configured for a simulation Mock service;

[0014] The first network address is connected with the second network address to form the simulation Mock baffle project.

[0015] According to some embodiments of the present application, the abnormal values are multiple, and the adding preset abnormal values in a configuration file of the data detection project comprises:

[0016] The configuration file is established in the data detection project;

[0017] The multiple abnormal values are sequentially added to the configuration file, and each abnormal value is assigned an abnormal number, wherein the abnormal values include an empty set, an empty string, an empty object and an empty array.

[0018] According to some embodiments of the present application, the reverse verification file comprises the test interface and the data filling field corresponding to the test interface, and the generating a reverse abnormal return value use case according to the reverse verification file and the configuration file comprises:

[0019] A traversal number is determined according to a first number of the test interface, a second number of the data filling field and a third number of the abnormal values;

[0020] The data test use case is processed according to the traversal number to obtain a traversal result;

[0021] The traversal result is verified to obtain the reverse abnormal return value use case.

[0022] According to some embodiments of the present application, the running the abnormal return value use case in the data detection project to obtain a data robustness detection result comprises:

[0023] The abnormal return value use case is executed to obtain abnormal data;

[0024] The abnormal data is directionally parsed to obtain the data robustness detection result.

[0025] According to some embodiments of the present application, after the running the abnormal return value use case in the data detection project to obtain a data robustness detection result, the method further comprises:

[0026] A first analysis is performed on the data robustness detection result to obtain an interface abnormal return result.

[0027] determine an exception cause information according to the interface exception return result;

[0028] perform a second analysis processing on the exception cause information to obtain exception processing suggestion information.

[0029] According to some embodiments of the present application, the directed analysis processing on the abnormal data to obtain the data robustness detection result comprises:

[0030] perform a classification processing on the abnormal data to obtain exception type information;

[0031] perform a comprehensive determination on the exception type information to obtain the data robustness detection result.

[0032] In a second aspect, the embodiments of the present application further provide a front-end reverse abnormal data robustness detection device, which comprises:

[0033] a first processing module, configured to acquire front-end request information;

[0034] a second processing module, configured to create a data test case, wherein the data test case is used to feed back interface return parameters to a test interface and fill in a data field assertion on a front-end page, and the interface return parameters are obtained by performing an associated analysis processing on the front-end request information;

[0035] a third processing module, configured to perform a matching processing on the interface return parameters and the data field assertion to obtain the reverse verification file;

[0036] a fourth processing module, configured to create a data detection project and add preset abnormal values in a configuration file of the data detection project;

[0037] a fifth processing module, configured to generate a reverse abnormal return value case according to the reverse verification file and the configuration file;

[0038] a sixth processing module, configured to run the abnormal return value case in the data detection project to obtain a data robustness detection result.

[0039] In a third aspect, the embodiments of the present application further provide an electronic device, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the front-end reverse abnormal data robustness detection method of the first aspect when executing the computer program.

[0040] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium storing computer executable instructions for executing the front-end reverse abnormal data robustness detection method according to the first aspect.

[0041] The front-end reverse abnormal data robustness detection method according to the embodiments of the present application has at least the following beneficial effects: in the process of performing the front-end reverse abnormal data robustness detection, first, the front-end request information is acquired; then, a data test case is created, wherein the data test case is used to feed back interface return parameters to a test interface and fill fields for data assertion on a front-end page, the interface return parameters are obtained by performing correlation analysis and processing on the front-end request information; then, the interface return parameters are matched with the data filling fields to obtain a reverse verification file; then, a data detection project is created, and preset abnormal values are added in a configuration file of the data detection project; then, a reverse abnormal return value case is generated according to the reverse verification file and the configuration file; finally, the abnormal return value case is run in the data detection project to obtain a data robustness detection result. Through the above technical solution, the front-end reverse abnormal data robustness detection processing of the financial industry can be realized, and the security is improved. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the technical solutions of the present application, and constitute a part of the specification, and are used to explain the technical solutions of the present application together with the embodiments of the present application, and do not constitute a limitation to the technical solutions of the present application.

[0043] Figure 1 is a flowchart of the front-end reverse abnormal data robustness detection method according to an embodiment of the present application;

[0044] Figure 2 is a flowchart of creating a data detection project in the front-end reverse abnormal data robustness detection method according to an embodiment of the present application;

[0045] Figure 3 is a flowchart of adding abnormal values in the front-end reverse abnormal data robustness detection method according to an embodiment of the present application;

[0046] Figure 4 is a flowchart of generating a reverse abnormal return value case in the front-end reverse abnormal data robustness detection method according to an embodiment of the present application;

[0047] Figure 5 is a flowchart of generating a data robustness detection result in the front-end reverse abnormal data robustness detection method according to an embodiment of the present application;

[0048] Figure 6is a flowchart of a front-end reverse abnormal data robustness detection method provided by another embodiment of the present application.

[0049] Figure 7 is a flowchart of directional analysis of abnormal data in a front-end reverse abnormal data robustness detection method provided by an embodiment of the present application.

[0050] Figure 8 is a schematic diagram of a front-end reverse abnormal data robustness detection device provided by an embodiment of the present application.

[0051] Figure 9 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0053] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification and claims and the above-described drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence.

[0054] It should be noted that unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0055] Robustness testing is a basic dimension of test design in the process of software testing, but in the existing agile development engineering mode, it is gradually marginalized because of its low priority and difficulty in verification. At present, for the display of front-end data in the financial industry, it is usually verified only in the case of abnormal input, which belongs to the case of positive coverage. However, for the reverse scenario of abnormal return, it is impossible to predict whether the actual display result of the front end can meet the expectation, and there is a lack of special verification of reverse abnormal return value, resulting in insufficient robustness of data and safety hazards for users.

[0056] The application provides a front-end reverse abnormal data robustness detection method and device, electronic equipment and a computer readable storage medium. In the process of front-end reverse abnormal data robustness detection, first, front-end request information is acquired; then, a data test case is created, wherein the data test case is used for feeding back interface return parameters to a test interface and filling a field assertion for data on a front-end page, and the interface return parameters are obtained by performing associated analysis and processing on the front-end request information; then, the interface return parameters are matched with data filling fields, so that a reverse verification file can be obtained; then, a data detection project is created, and preset abnormal values are added to a configuration file of the data detection project; then, a reverse abnormal return value case is generated according to the reverse verification file and the configuration file; finally, the abnormal return value case is run in the data detection project, so that a data robustness detection result is obtained. Through the above technical solution, front-end reverse abnormal data robustness detection processing of the financial industry can be realized, and the security is improved.

[0057] The front-end reverse abnormal data robustness detection method provided by the application is related to the technical field of data processing. The front-end reverse abnormal data robustness detection method provided by the application can be applied to a terminal, can be applied to a server end, and can also be software running in the terminal or the server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server end can be configured as a separate physical server, can be configured as a server cluster or a distributed system formed by multiple physical servers, can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and big data and artificial intelligence platforms; and the software can be an application for implementing the front-end reverse abnormal data robustness detection method, but is not limited to the above forms.

[0058] The application can be used in many general or specific computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0059] It should be noted that in various specific embodiments of the present application, when relevant processing needs to be performed on data related to the identity or characteristics of the user, such as user information, user behavior data, user history data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards of the relevant country or region. In addition, when the embodiments of the present application need to obtain sensitive personal information of the user, the separate permission or separate consent of the user will be obtained through a pop-up window or a jump to a confirmation page, and after obtaining the separate permission or separate consent of the user, the necessary user-related data for enabling the embodiments of the present application to normally operate will be obtained.

[0060] The embodiments of the present application will be further described below with reference to the accompanying drawings.

[0061] As shown in Figure 1 Figure 1 is a flowchart of a front-end reverse abnormal data robustness detection method provided by an embodiment of the present application. The front-end reverse abnormal data robustness detection method includes but is not limited to steps S100 to S600.

[0062] Step S100, obtaining front-end request information;

[0063] Step S200, creating a data test case, wherein the data test case is used to feed back interface return parameters to a test interface and fill in fields for data assertion on a front-end page, and the interface return parameters are obtained by performing associated analysis and processing on the front-end request information;

[0064] Step S300, matching the interface return parameters with the data filling fields to obtain a reverse verification file;

[0065] Step S400, creating a data detection project and adding preset abnormal values in a configuration file of the data detection project;

[0066] Step S500, generating a reverse abnormal return value case according to the reverse verification file and the configuration file;

[0067] Step S600, running the abnormal return value case in the data detection project to obtain a data robustness detection result.

[0068] ​It should be noted that in the process of carrying out the front-end reverse abnormal data robustness detection, first, the front-end request information is acquired; then a data test case is created, wherein the data test case is used to feed back the interface return parameters to the test interface and fill in the data field assertion on the front-end page, the interface return parameters are obtained by correlating and analyzing the front-end request information; then the interface return parameters are matched with the data filling field, and the reverse verification file can be obtained; then a data detection project is created, and preset abnormal values are added to the configuration file of the data detection project; then the reverse abnormal return value case is generated according to the reverse verification file and the configuration file; finally, the abnormal return value case is run in the data detection project, so that the data robustness detection result is obtained. Through the above technical solution, the front-end reverse abnormal data robustness detection processing of the financial industry can be realized, and the security is improved.

[0069] It should be noted that the front end refers to the front-end part of the website, which runs on the computer end, mobile end and other browsers to display web pages for users to browse. With the development of Internet technology, the application of front-end framework, cross-platform responsive web design can adapt to various screen resolutions, appropriate motion design, and bring users a very high user experience. Front-end technology is generally divided into front-end design and front-end development. Front-end design can generally be understood as the visual design of the website, and front-end development is the front-end code implementation of the website. From the application range, front-end development is not only known by ordinary people, but also has a more extensive application scene than back-end. At present, the computer end is still a major field of front-end, which is mainly divided into various websites for the public, such as news media, social media, e-commerce, forums, etc., and various content management systems and other back-end management systems for administrators. Among them, the front-end request information in the embodiment of the application is information generated by performing a related data request triggering operation on the front-end page. For example, a user performing a related data request triggering operation on the front-end page can be logging in to a website on the front-end page, searching for related content on a certain website, or clicking on a related link on a certain page.

[0070] It should be noted that the robustness of data refers to the fact that the running result of the entire system meets the expectations of the designers and developers; can tolerate within a certain limit, avoid the misoperation of the operator to the greatest extent; the running performance of the system can meet the needs of common user scenarios and the predicted data throughput; the system prevents unauthorized behavior and other illegal operations of users. Robustness is usually reflected in system fault tolerance, that is, the processing mechanism when an error occurs rather than trying to avoid errors. In order to test the effect of the processing mechanism, sometimes errors need to be actively triggered.

[0071] It should be noted that the data test case can be created on an end-to-end automatic test architecture; the data test case is used to feed back the interface return parameter to the test interface and fill in the field assertion for the data on the front-end page; the data test case can be understood as a function test scene case; the case can feed back the test interface return parameter and fill in the field assertion for the data on the front-end page. The assertion is a first-order logic in the program, for example, a logical judgment formula with a true or false result, the purpose is to represent and verify the result expected by the software developer, when the program executes to the position of the assertion, the corresponding assertion should be true. If the assertion is not true, the program will stop executing and give an error message; when writing code, the developer will always make some assumptions, and the assertion is used to capture these assumptions in the code, the developer at a certain point in the program that the expression value is true, the assertion verification can be enabled and disabled at any time, so the assertion can be enabled when testing and disabled when deploying, similarly, after the program is put into operation, the assertion can be re-enabled when problems are encountered.

[0072] It should be noted that the interface return parameter in the embodiments of the present application can be obtained by associatively analyzing the front-end request information; the front-end request information is analyzed, and the corresponding interface return parameter can be obtained according to the preset return strategy.

[0073] It should be noted that matching the interface return parameter with the data filling field can obtain the reverse verification file; the reverse verification file includes all test interfaces related to front-end page data filling and corresponding data filling fields.

[0074] It should be noted that a data detection project is created, and a configuration file is created in the data detection project, and then a preset abnormal value is added in the configuration file; the abnormal value can be a null value, an empty string, an empty array or other customized state.

[0075] It should be noted that front-end reverse is a technology that uses the feature that all front-end code and data are exposed to the client to analyze the source code to obtain data. Front-end reverse analysis is usually used to analyze dynamically rendered websites. If the analysis is thorough, the browser simulation method can be avoided for crawling.

[0076] In some embodiments, as shown in Figure 2 The data detection project is a simulation Mock baffle project, and the step S400 can include but is not limited to steps S410 to S420.

[0077] Step S410, a first network address is configured for a preset data detection device, and a second network address is configured for a simulation Mock service.

[0078] Step S420, the first network address is connected with the second network address to form the simulation Mock baffle project.

[0079] It should be noted that in the embodiments of the present application, the data detection project is the simulation Mock baffle project, and in the process of creating the data detection project, first, the first network address is configured to the preset data detection device and the second network address is configured to the simulation Mock service; then the first network address and the second network address are connected to form the simulation Mock baffle project, which makes a good preparation for the subsequent data robustness detection.

[0080] It should be noted that the data detection device can be a computer, a mobile phone, a tablet or other terminals, which are not limited here; wherein, the Mock service is a virtual service object created in the process of Mock test; Mock test is a test method that uses a virtual object to create in order to test in the testing process for some objects that are not easy to construct or not easy to obtain; for example, the first interface of the test depends on the second interface, but the development of the second interface has not been completed, if waiting for iteration, the time will be compressed, in such a case, the value returned by the second interface can be simulated based on the interface of the Mock service to test the first interface; or the dependent external interface is not open, such as the payment block, the payment interface has not been opened, then Mock can also be used to solve; or the test depends on too many things, the environment is complex to build, and the time cost is high, Mock can also be used.

[0081] It can be understood that the first network address and the second network address are connected, so that the preset data detection device can establish data connection with the simulation Mock service, thereby forming the simulation Mock baffle project. By establishing the simulation Mock baffle project, the verification of the normal logic of the conventional version is completed by using the false data.

[0082] In some embodiments, as shown in Figure 3 The above step S400 can further include but not limited to steps S430 to S440.

[0083] Step S430, a configuration file is established in the data detection project;

[0084] Step S440, a plurality of abnormal values are sequentially added to the configuration file, and each abnormal value is assigned an abnormal number, wherein the abnormal values include an empty set, an empty string, an empty object and an empty array.

[0085] It should be noted that the configuration file is established in the data detection process, and a plurality of abnormal values are sequentially added to the configuration file, and each abnormal value is assigned an abnormal number, wherein the abnormal value can include an empty set, an empty string, an empty object and an empty array.

[0086] It can be understood that the configuration file is essentially a file containing information required for successful operation of a program, which is structured in a specific way, and is not hard-coded in the program, but is user-configurable, usually stored in a plain text file. The set abnormal value is added to the created configuration file, and each abnormal value is sequentially assigned an abnormal number, for example, for the first added abnormal value, the abnormal number can be 001, and for the second added abnormal value, the abnormal number can be 002.

[0087] It should be noted that in the embodiments of the present application, the abnormal value can include an empty set, an empty string, an empty object, an empty array or other custom elements.

[0088] In some embodiments, as shown in Figure 4 The reverse verification file includes a test interface and a data filling field corresponding to the test interface, and the step S500 can include but is not limited to steps S510 and S530.

[0089] Step S510, determining a traversal number according to a first number of test interfaces, a second number of data filling fields and a third number of abnormal values;

[0090] Step S520, performing traversal processing on the data test case according to the traversal number to obtain a traversal result;

[0091] Step S530, performing verification processing on the traversal result to obtain a reverse abnormal return value case.

[0092] It should be noted that in the process of generating the reverse abnormal return value case, first, the traversal number is determined according to the first number of test interfaces, the second number of data filling fields and the third number of abnormal values; then the data test case is processed according to the traversal data to obtain a traversal result; finally, the reverse abnormal return value case can be obtained by performing verification processing on the traversal result.

[0093] It should be noted that the traversal number is calculated according to the first number of test interfaces, the second number of data filling fields and the third number of abnormal values by using a set algorithm, and then the data test case can be processed according to the traversal number to obtain the corresponding traversal result, in order to prepare for subsequent abnormal data robustness analysis.

[0094] It can be understood that traversal refers to sequentially accessing each node in a tree (or graph) along a certain search route. The operation of accessing a node depends on the specific application problem, and the specific access operation can be checking the value of the node, updating the value of the node, etc. The order of accessing nodes is different for different traversal methods. Traversal is one of the most important operations on binary trees and is the basis for other operations on binary trees. Of course, the concept of traversal is also suitable for the case of a multi-element set, such as an array.

[0095] It is worth noting that the verification processing of the traversal result is the analysis and verification processing of the traversal result to form the reverse abnormal return value use case.

[0096] In some embodiments, as shown in Figure 5 The above step S600 can include but is not limited to step S610 and step S620.

[0097] Step S610, performing processing on the abnormal return value use case to obtain abnormal data;

[0098] Step S620, performing directional analysis processing on the abnormal data to obtain a data robustness detection result.

[0099] It should be noted that in the process of generating the data robustness detection result, first, performing processing on the abnormal return value use case to obtain abnormal data; then performing directional analysis processing on the abnormal data to obtain the data robustness detection result.

[0100] It is worth noting that in the process of performing processing on the abnormal return value use case, the abnormal data will be obtained; then performing directional analysis processing on the abnormal data to obtain the data robustness detection result, and performing analysis processing on the abnormal data to obtain the data robustness detection result.

[0101] It can be understood that performing processing on the abnormal return value use case means operating the corresponding abnormal return value use case, and in this process, the corresponding abnormal data will be generated; then performing directional analysis processing on the abnormal data obtained to obtain the data robustness detection result.

[0102] In some embodiments, as shown in Figure 6 After performing the above step S600, it can also include but is not limited to step S710, step S720 and step S730.

[0103] Step S710, performing first analysis processing on the data robustness detection result to obtain an interface abnormal return result;

[0104] Step S720, determining abnormal cause information according to the interface abnormal return result;

[0105] Step S730, the second analysis processing of the abnormal cause information obtains abnormal processing suggestion information.

[0106] It should be noted that after obtaining the data robustness detection result, the data robustness detection result can be first analyzed and processed to obtain the interface abnormal return result; then the abnormal cause information is determined according to the interface abnormal return result; finally, the second analysis processing of the abnormal cause information can obtain the abnormal processing suggestion information, which provides the suggestion for the R&D personnel to solve the data detection abnormality, so that the R&D personnel can quickly repair the related vulnerability, improve the data repair efficiency, and improve the security of the data system.

[0107] It can be understood that the first analysis processing of the data robustness detection result can obtain the interface abnormal return result; then the abnormal cause information can be obtained by determining according to the interface abnormal return result; finally, the second analysis processing of the abnormal cause information according to the preset strategy can obtain the abnormal processing suggestion information.

[0108] In some embodiments, as shown in Figure 7 The above step S620 can include but is not limited to step S621 and step S622.

[0109] Step S621, the abnormal data is classified and processed to obtain abnormal type information;

[0110] Step S622, the abnormal type information is comprehensively determined to obtain the data robustness detection result.

[0111] It should be noted that in the process of directional analysis processing of the abnormal data, the abnormal data can be first classified and processed to obtain the abnormal type information, and then the abnormal type information is comprehensively determined to obtain the data robustness detection result.

[0112] It can be understood that the abnormal data is classified to distinguish different abnormal types, so as to obtain the abnormal type information; then the abnormal type information is comprehensively determined to obtain the data robustness detection result.

[0113] In addition, as shown in Figure 8 One embodiment of the present application also provides a front-end reverse abnormal data robustness detection device 10, which comprises:

[0114] The first processing module 100 is used for acquiring front-end request information.

[0115] The second processing module 200 is configured to create a data test case, wherein the data test case is used to feed back interface return parameters to a test interface and fill in fields for data assertion on a front-end page, and the interface return parameters are obtained by performing correlation analysis and processing on the front-end request information.

[0116] The third processing module 300 is configured to perform matching processing on the interface return parameters and the data filling fields to obtain a reverse verification file.

[0117] The fourth processing module 400 is configured to create a data detection project and add preset abnormal values in a configuration file of the data detection project.

[0118] The fifth processing module 500 is configured to generate a reverse abnormal return value case according to the reverse verification file and the configuration file.

[0119] The sixth processing module 600 is configured to run the abnormal return value case in the data detection project to obtain a data robustness detection result.

[0120] It should be noted that, in the process of the front-end reverse abnormal data robustness detection, the front-end request information is first obtained; then a data test case is created, wherein the data test case is used to feed back interface return parameters to a test interface and fill in fields for data assertion on a front-end page, and the interface return parameters are obtained by performing correlation analysis and processing on the front-end request information; then matching processing is performed on the interface return parameters and the data filling fields to obtain a reverse verification file; then a data detection project is created, and preset abnormal values are added in a configuration file of the data detection project; then a reverse abnormal return value case is generated according to the reverse verification file and the configuration file; finally, the abnormal return value case is run in the data detection project, so as to obtain a data robustness detection result. Through the above technical solution, the front-end reverse abnormal data robustness detection processing of the financial industry can be realized, and the security is improved.

[0121] It should be noted that the robustness of data refers to that the running result of the entire system meets the expectation of the designer and developer; the operation of the operator can be tolerated within a certain limit, and the operation of the operator can be avoided within the maximum extent; the running performance of the system can meet the demand of common user scenarios and the expected data throughput; the system prevents the overreach behavior and other illegal operations of the user. The robustness is usually reflected in the system fault tolerance, that is, the processing mechanism when the error occurs rather than trying to avoid the error. In order to test the effect of the processing mechanism, the error is sometimes even triggered actively.

[0122] It is worth noting that the interface return parameter in the embodiment of the present application can be obtained by performing correlation analysis on the front-end request information; the correlation analysis on the front-end request information is to analyze the front-end request information, and the corresponding interface return parameter can be obtained according to the preset return strategy.

[0123] It is worth noting that the interface return parameter and the data filling field are matched and processed, and the reverse verification file can be obtained; wherein the reverse verification file includes all test interfaces and corresponding data filling fields related to front-end page data filling.

[0124] It is worth noting that a data detection project is created, and a configuration file is created in the data detection project, and then a preset abnormal value is added in the configuration file; wherein the abnormal value can be a null value, an empty string, an empty array or other customized state.

[0125] It is worth noting that the front-end reverse is to use the feature that all codes and data of the front-end are exposed to the client, and to obtain data by analyzing the source code. The front-end reverse analysis is usually used to analyze dynamically rendered websites. If the analysis is thorough, the use of browser simulation for crawling can be avoided.

[0126] The specific embodiments of the front-end reverse abnormal data robustness detection device are basically the same as the specific embodiments of the above-mentioned front-end reverse abnormal data robustness detection method, and will not be repeated here.

[0127] In addition, as shown in Figure 9 , one embodiment of the present application also provides an electronic device 700, which comprises a memory 720, a processor 710 and a computer program stored in the memory 720 and executable on the processor 710.

[0128] The processor 710 and the memory 720 can be connected by a bus or other means.

[0129] The non-transitory software program and instructions required for the front-end reverse abnormal data robustness detection method of the above-mentioned embodiments are stored in the memory 720, and when executed by the processor 710, the front-end reverse abnormal data robustness detection method of the above-mentioned embodiments is executed, for example, the method steps S100 to S600 in Figure 1 , the method steps S410 to S420 in Figure 2 , the method steps S430 to S440 in Figure 3 , the method steps S510 to S530 in Figure 4 , the method steps S610 to S620 in Figure 5 , the method steps S710 to S730 in Figure 6 and the method steps S810 to S830 in Figure 7the method steps S621-S622 in

[0130] The apparatus embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0131] In addition, one embodiment of the present application further provides a computer readable storage medium storing computer executable instructions, which are executed by a processor 710 or a controller, for example, a processor 710 in the above device embodiment, so that the above processor 710 executes the front-end reverse abnormal data robustness detection method in the above embodiment, for example, executes the method steps S100-S600 in Figure 1 the method steps S100-S600 in Figure 2 the method steps S410-S420 in Figure 3 the method steps S430-S440 in Figure 4 the method steps S510-S530 in Figure 5 the method steps S610-S620 in Figure 6 the method steps S710-S730 in Figure 7 the method steps S621-S622 in

[0132] The above embodiments can be used in combination, and the modules with the same name in different embodiments can be the same or different.

[0133] The above describes specific embodiments of the present application, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than the order in which they are recited and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily have to be performed in the specific order shown and / or sequentially, as some of the processes can be performed in parallel or with partial concurrence. In addition, certain embodiments of this application can be implemented over a time period, such as a few seconds, minutes, hours, days, or weeks, where the operations of the various embodiments can be performed at different times during this time period.

[0134] Each of the embodiments in the present application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the device, equipment and computer readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0135] The device, the equipment, the computer readable storage medium and the method provided by the embodiments of the present application are corresponding, therefore, the device, the equipment, the nonvolatile computer storage medium also has similar beneficial technical effects as the corresponding method, since the beneficial technical effects of the method have been described in detail above, therefore, the beneficial technical effects of the corresponding device, equipment, computer storage medium will not be described here.

[0136] In the 1990s, it was quite obvious to distinguish whether an improvement in a technology was in hardware (e.g., improvement in circuit structures of diodes, transistors, switches, etc.) or in software (improvement in method flow). However, as technology has evolved, many improvements in method flow today can be considered as direct improvements in hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structures by programming the improved method flow into hardware circuits. Therefore, it cannot be said that an improvement in a method flow cannot be implemented by a hardware entity module. For example, a programmable logic device (PLD) such as a field programmable gate array (FPGA) is an integrated circuit whose logic function is determined by user programming of the device. A digital system is "integrated" on a PLD by the designer programming it, rather than by asking a chip manufacturer to design and fabricate a custom integrated circuit chip. Moreover, instead of manually fabricating integrated circuit chips, this programming is now mostly implemented using "logic compiler" software, which is similar to software compilers used in program development, and the original code to be compiled is written in a specific programming language, which is called a hardware description language (HDL), and there are many such languages, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should be aware that, as long as the method flow is logically programmed in the above-mentioned hardware description languages and programmed into an integrated circuit, a hardware circuit implementing the logical method flow can be easily obtained.

[0137] The controller can be implemented in any suitable way, for example, the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, such as software or firmware, executable by the (micro)processor, logic gates, switches, application specific integrated circuits (ASICs), programmable logic controllers and embedded microcontrollers, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicon Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that, in addition to implementing the controller in pure computer readable program code, it is possible to implement the controller in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers, etc. to perform the same functions by logically programming the method steps. Such a controller can therefore be considered to be a hardware component, and the means included therein for performing various functions can also be considered to be structures within the hardware component. Alternatively, the means for performing various functions can even be considered to be both a software module implementing the method and a structure within the hardware component.

[0138] The systems, apparatuses, modules or units illustrated by the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0139] For the sake of description, the above apparatuses are described in functional division and are described respectively as various units. Of course, the functions of the units can be implemented in the same or multiple software and / or hardware in the implementation of the embodiments of the present application.

[0140] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the embodiments of the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0141] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 2 A device that provides the functions specified in one or more boxes.

[0142] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 3 One or more processes and / or boxes Figure 4 The function specified in one or more boxes.

[0143] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 5 One or more processes and / or boxes Figure 6 Figure 7 Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 The steps of the function specified in one or more boxes.

[0144] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0145] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory. Memory is an example of computer-readable media.

[0146] Computer-readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0147] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusions, so that processes, methods, articles or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed, or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0148] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. The association relationship between the associated objects is described by "and / or", which means that there can be three kinds of relationships, for example, A and / or B, which can represent the cases of A alone, A and B together, and B alone. Wherein A, B can be singular or plural. The character " / " generally represents that the associated objects before and after are in an "or" relationship. "At least one of the following" and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0149] Embodiments of the present application can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. Embodiments of the present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.

[0150] Various embodiments in the present application are described with progressive manner, and the same or similar parts among various embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. Especially, the system embodiments are described simply because they are basically similar to the method embodiments, and the related parts can be referred to the description of the method embodiments.

[0151] The above only describes the embodiments of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims

1. A method for robustness detection of front-end reverse anomaly data, characterized in that, The method includes: Get frontend request information; Create data test cases, wherein the data test cases are used to feed back interface return parameters to the test interface and assertion to populate data fields on the front-end page, and the interface return parameters are obtained by performing correlation analysis on the front-end request information; The parameters returned by the interface are matched with the data filling fields to obtain a reverse verification file; Create a data inspection project and add preset outliers to the configuration file of the data inspection project; Generate reverse exception return value test cases based on the reverse verification file and the configuration file; Run the abnormal return value test case in the data inspection project to obtain the data robustness inspection results; The reverse verification file includes the test interface and the data filling field corresponding to the test interface. The step of generating reverse exception return value test cases based on the reverse verification file and the configuration file includes: The number of iterations is determined based on the first number of test interfaces, the second number of data filling fields, and the third number of outliers. The data test cases are traversed according to the number of traversals to obtain the traversal results. The traversal results are validated to obtain the reverse exception return value test case.

2. The front-end reverse abnormal data robustness detection method according to claim 1, characterized in that, The data detection project is a mock baffle project. The creation of the data detection project includes: Configure a first network address for the preset data detection device and a second network address for the simulated Mock service; The first network address and the second network address are connected to form the simulated Mock baffle project.

3. The front-end reverse abnormal data robustness detection method according to claim 1, characterized in that, There are multiple outliers, and the preset outliers are added to the configuration file of the data detection project, including: The configuration file is created in the data detection project; Multiple abnormal values ​​are added sequentially to the configuration file, and an abnormal value is assigned an abnormal number. The abnormal values ​​include empty sets, empty strings, empty objects, and empty arrays.

4. The front-end reverse abnormal data robustness detection method according to claim 1, characterized in that, The process of running the abnormal return value test case in the data inspection project to obtain data robustness inspection results includes: The exception return value test case is processed to obtain exception data; The abnormal data is analyzed in a targeted manner to obtain the data robustness detection results.

5. The front-end reverse abnormal data robustness detection method according to claim 1, characterized in that, After running the abnormal return value test case in the data inspection project to obtain the data robustness inspection result, the method further includes: The data robustness test results are first analyzed and processed to obtain the interface anomaly return result; Determine the cause of the exception based on the abnormal return result of the interface; The anomaly cause information is analyzed and processed a second time to obtain anomaly handling suggestion information.

6. The front-end reverse abnormal data robustness detection method according to claim 4, characterized in that, The process of performing targeted parsing on the abnormal data to obtain the data robustness detection result includes: The abnormal data is classified to obtain abnormality type information; The data robustness detection result is obtained by comprehensively judging the anomaly type information.

7. A robustness detection device for front-end reverse abnormal data, characterized in that, The device includes: The first processing module is used to obtain front-end request information; The second processing module is used to create data test cases, wherein the data test cases are used to feed back interface return parameters to the test interface and assert data fields on the front-end page, wherein the interface return parameters are obtained by performing correlation analysis on the front-end request information; The third processing module is used to match the parameters returned by the interface with the data filling field to obtain a reverse verification file; The fourth processing module is used to create a data inspection project and add preset outlier values ​​to the configuration file of the data inspection project. The fifth processing module is used to generate reverse exception return value test cases based on the reverse verification file and the configuration file; The sixth processing module is used to run the abnormal return value test case in the data detection project to obtain the data robustness detection result; The reverse verification file includes the test interface and the data filling field corresponding to the test interface. The step of generating reverse exception return value test cases based on the reverse verification file and the configuration file includes: The number of iterations is determined based on the first number of test interfaces, the second number of data filling fields, and the third number of outliers. The data test cases are traversed according to the number of traversals to obtain the traversal results. The traversal results are validated to obtain the reverse exception return value test case.

8. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the front-end reverse abnormal data robustness detection method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the front-end reverse abnormal data robustness detection method according to any one of claims 1 to 6.

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