A regression testing method, device, storage medium, electronic device and product

Abstract

The specification provides a regression test method, device, storage medium, electronic equipment and product. The regression test method comprises: obtaining credit investigation business data generated by a credit investigation system in the process of executing actual credit investigation business, the credit investigation business data comprising: input request parameters input by a data demand party into the credit investigation system, original data returned by a data provider to the credit investigation system, actual output request parameters sent by the credit investigation system to the data provider, and actual credit investigation data returned by the credit investigation system to the data demand party; after business change is performed on the credit investigation system, inputting the input request parameters into the credit investigation system to obtain simulated output request parameters, and inputting the original data into the credit investigation system to obtain simulated credit investigation data; and determining whether the credit investigation system successfully completes the business change according to the deviation between the actual output request parameters and the simulated output request parameters, and the deviation between the actual credit investigation data and the simulated credit investigation data.

Description

TECHNICAL FIELD

[0001] One or more embodiments of the present specification relate to the technical field of regression testing, and in particular to a regression testing method, device, storage medium, electronic device and product. BACKGROUND

[0002] In the related art, a credit investigation agency is a key hub connecting upstream financial institutions and downstream data source institutions, and is responsible for data aggregation, processing and result feedback. The upstream financial institutions need to initiate data query requests to multiple downstream data source institutions through the credit investigation agency. After the downstream data source institutions provide relevant data according to the request, the credit investigation agency integrates, verifies, analyzes and processes the data, and then returns the final result to the upstream financial institutions. The smooth operation of this data interaction process is an important technical support for maintaining the safety and stability of financial services.

[0003] To adapt to the iterative and updating needs of financial services, business changes such as function adjustment and business optimization need to be implemented on the credit investigation system of the credit investigation agency. After the business changes are implemented, regression testing needs to be performed on the credit investigation system to verify that the system can still operate stably and correctly after the business changes, and to avoid business interruption risks caused by changes.

[0004] However, the existing regression testing method after the business changes of the credit investigation system mainly relies on manual verification, which not only has a huge workload, but also highly depends on the quality of the test data, and is difficult to restore the actual credit investigation business scenario, resulting in insufficient verification coverage and limited accuracy, and being unable to fully guarantee the stability and correctness of the credit investigation system after the changes. SUMMARY

[0005] Therefore, one or more embodiments of the present specification provide technical solutions as follows:

[0006] According to a first aspect of one or more embodiments of the present specification, a regression testing method is provided, comprising:

[0007] Obtaining credit investigation business data generated by a credit investigation system in the process of executing actual credit investigation business, the credit investigation business data comprising: input request parameters input by a data demand side into the credit investigation system, original data returned by a data provider to the credit investigation system, actual output request parameters sent by the credit investigation system to the data provider, and actual credit investigation data returned by the credit investigation system to the data demand side;

[0008] After a business change occurs in the credit investigation system, inputting the input request parameters into the credit investigation system to make the credit investigation system perform parameter processing on the input request parameters to obtain simulated output request parameters, and inputting the original data into the credit investigation system to make the credit investigation system perform business processing on the original data to obtain simulated credit investigation data.

[0009] According to the deviation between the actual output request parameter and the simulation output request parameter, and the deviation between the actual credit investigation data and the simulation credit investigation data, it is determined whether the credit investigation system successfully completes the business change.

[0010] According to a second aspect of one or more embodiments of the present specification, a regression testing apparatus is provided, comprising:

[0011] An acquisition module is configured to acquire credit investigation business data generated by a credit investigation system in the process of performing an actual credit investigation business, wherein the credit investigation business data comprises: input request parameters input by a data demand side into the credit investigation system, actual output request parameters sent by the credit investigation system to a data provider, actual credit investigation data returned by the credit investigation system to the data demand side, and original data returned by the data provider to the credit investigation system.

[0012] An input module is configured to input the input request parameters into the credit investigation system, so that the credit investigation system performs parameter processing on the input request parameters to obtain simulation output request parameters, and input the original data into the credit investigation system, so that the credit investigation system performs business processing on the original data to obtain simulation credit investigation data.

[0013] A determination module is configured to perform regression testing on the credit investigation system according to the deviation between the actual output request parameter and the simulation output request parameter, and the deviation between the actual credit investigation data and the simulation credit investigation data.

[0014] According to a third aspect of one or more embodiments of the present specification, an electronic device is provided, comprising: a processor; a memory for storing processor executable instructions; wherein the processor implements the steps of the above method by running the executable instructions.

[0015] According to a fourth aspect of one or more embodiments of the present specification, a computer readable storage medium is provided, which stores computer instructions, and the instructions are executed by a processor to implement the steps of the above method.

[0016] According to a fifth aspect of one or more embodiments of the present specification, a computer program product is provided, comprising computer program / instructions, and the computer program / instructions are executed by a processor to implement the steps of the above method.

[0017] As can be known from the above embodiments, the present specification can collect real data generated by the credit investigation system in the process of performing actual credit investigation business. After the credit investigation system is changed, the input request parameters can be input into the credit investigation system to obtain simulated output request parameters, and the original data can be input into the credit investigation system to obtain simulated credit investigation data. In this way, the data interaction logic in the real credit investigation business can be accurately reproduced, so as to restore the real credit investigation business scene on the line to the greatest extent, and then based on the comparison and analysis of the real data and the simulation results, automatic regression testing is performed, so as to effectively guarantee the scene coverage and result accuracy of the regression testing, and improve the reliability of the stability verification of the credit investigation system after the business change. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 FIG. 1 is a schematic diagram of an architecture of a credit investigation service system provided by an example embodiment;

[0019] Figure 2 FIG. 3 is a schematic diagram of a regression testing method provided by an example embodiment;

[0020] Figure 3 FIG. 5 is a schematic diagram of an internal structure of a credit investigation system provided by an example embodiment;

[0021] Figure 4 FIG. 7 is a whole flowchart of regression testing provided by an example embodiment;

[0022] Figure 5 FIG. 9 is a schematic diagram of a structure of a device provided by an example embodiment;

[0023] Figure 6 FIG. 11 is a block diagram of a regression testing device provided by an example embodiment. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions in the present specification, the technical solutions in the present specification will be clearly and completely described below in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present specification, rather than all the embodiments. Based on the embodiments in the present specification, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present specification.

[0025] The user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the specification are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0026] In the field of financial services, financial institutions usually need to obtain multi-dimensional credit-related data from credit investigation agencies to accurately assess the credit status of users. Specifically, the information flow path is as follows: the financial institution as the data demander initiates a data query or credit evaluation request to the credit investigation agency; the credit investigation agency as an intermediate node receives the request, connects multiple data source agencies based on its own business system, and obtains various credit data such as user credit records, repayment behavior, and performance history; after processing and integration, the credit investigation agency feeds back the formed credit report, credit score, etc. to the financial institution, providing decision support for the financial institution's credit approval, risk control and other businesses.

[0027] To adapt to the iterative and updating needs of financial businesses, business changes need to be made to the credit investigation system, i.e. adjusting and optimizing the function modules, data processing rules, business interaction logic or output result format of the system, such as adding data source connection interfaces, updating credit score algorithms, optimizing credit report generation logic, adapting to new compliance data fields, etc. In the process of implementing these changes, the financial system may face problems such as changes in data interaction links, compatibility risks of original functions, or abnormal connection of business logic, etc. Therefore, comprehensive regression testing needs to be carried out on the changed credit investigation system to verify the system's function integrity, data processing accuracy and business running stability, to ensure that it meets the business needs and compliance requirements of financial institutions.

[0028] Based on this, the present specification provides a regression testing method, which collects real data generated in the process of the credit investigation system performing actual business, so that after a business change occurs in the credit investigation system, the data interaction logic in the real credit investigation business can be accurately reproduced through the collected actual credit investigation business data, thereby restoring the real credit investigation business scene on the line to the greatest extent, and then performing automatic regression testing based on the comparison and analysis of real data and simulation results.

[0029] Figure 1 FIG. 1 is a schematic diagram of an architecture of a credit investigation service system provided by an example embodiment.

[0030] Among them, the data demander can be a business processing system corresponding to a financial institution (i.e. data demander) such as a bank, a securities company, an insurance company, etc., which is used to provide financial businesses such as deposit and withdrawal, transfer and remittance, credit issuance, securities trading, insurance and claim settlement, etc.

[0031] The credit investigation system can be a specialized platform operated by a credit investigation agency, used for integrating, verifying, analyzing and feedback processing of related query requests and multi-channel credit data (such as credit records and default information), to form a standardized credit evaluation result.

[0032] The data provider can be a data platform of a data source agency (i.e., a data provider) such as an e-commerce platform, a social network, a communication operator, a government department, etc., used for storing cross-field data such as user consumption behavior records, social interaction data, communication trajectories and positioning, enterprise business information, tax records, etc.

[0033] On the basis of the above credit investigation server system architecture, the present specification provides a regression testing method, as shown in Figure 2

[0034] Figure 2 FIG. 1 is a flow diagram of a regression testing method provided by an exemplary embodiment, including the following steps:

[0035] S200: Obtain credit investigation business data generated by the credit investigation system in the process of executing actual credit investigation business, wherein the credit investigation business data includes input request parameters input by a data demand side into the credit investigation system, original data returned by the data provider to the credit investigation system, actual output request parameters sent by the credit investigation system to the data provider, and actual credit investigation data returned by the credit investigation system to the data demand side.

[0036] In the present specification, the execution subject for executing the regression testing method can be a credit investigation system, specifically a test subsystem provided inside the credit investigation system. The test subsystem can be an embedded hardware unit with high concurrent computing capability, or a hardware terminal integrated with data storage and operation functions, which is equipped with a special data processing chip. Of course, it can also be an automatic test program deployed in the credit investigation system server, a software component integrated with data comparison and result analysis functions, or a test management system developed based on a cloud platform. In order to facilitate understanding, the present specification provides an internal structure diagram of a credit investigation system, as shown in Figure 3

[0037] Figure 3 FIG. 2 is an internal structure diagram of a credit investigation system provided by an exemplary embodiment.

[0038] ​​In the process of the credit investigation system performing actual credit investigation business (i.e., an actual business link), a business demander (i.e., a financial system corresponding to a financial institution) sends a business request to the credit investigation system, an entry gateway of the credit investigation system receives the business request, and transmits corresponding input request parameters of the business request to a business subsystem. At this time, the business subsystem can perform parameter processing (such as format standardization conversion, sensitive information desensitization, compulsory field verification, and request parameter encryption) on the input request parameters, so as to convert the input request parameters into output request parameters that can be recognized and analyzed by a data provider. Then, the output request parameters are sent to the data provider through an exit gateway.

[0039] After receiving and verifying the legality and integrity of the output request parameters, the data provider can further determine corresponding original data (such as user credit repayment details in the last N years, credit card overdue records, and public utility fee payment situations) based on the output request parameters. Then, the original data is returned to the credit investigation system, and the original data is transmitted to the business subsystem by the exit gateway. At this time, the business subsystem can perform business processing (such as data cleaning and deduplication, abnormal value correction, multi-source data correlation and integration, and credit index calculation) on the original data, so as to obtain credit investigation data, and return the credit investigation data to the data demander through the entry gateway.

[0040] In the actual business link process, two business subsystems are involved in the processing process, that is, the actual input request parameters are processed to obtain actual output request parameters, and the actual original data is processed to obtain actual credit investigation data. In this process, the test subsystem can monitor the entire business link and collect credit investigation business data generated in the process of actual credit investigation business. The credit investigation business data can include: input request parameters (InReq) input by the data demander into the credit investigation system, original data (OutResult) returned by the data provider to the credit investigation system, actual output request parameters (OutReq) sent by the credit investigation system to the data provider, and actual credit investigation data (InResult) returned by the credit investigation system to the data demander.

[0041] In addition, in order to ensure that the credit investigation business data in the process of the credit investigation system performing actual credit investigation business and the regression test data generated in the regression test process are isolated from each other, the test subsystem can store the above credit investigation business data in a business database, retrieve the credit investigation business data from the business database after the credit investigation system is changed, use the credit investigation business data to perform regression testing on the credit investigation system, and store regression test data generated in the regression test process in a test database.

[0042] Furthermore, during the process of storing credit reporting business data into the business database, the testing subsystem can first obtain the initial business data generated by the credit reporting system during the execution of actual credit reporting business. Then, it filters out invalid data that does not conform to business logic (such as query request data with missing user identity information, credit record data with incorrect field format, performance data that exceeds the business time range, etc.), abnormal data that failed to be processed by the credit reporting system (such as data that did not return results due to interruption of data source interface, request data that was rejected due to data verification failure, incomplete data generated by errors in the business subsystem, etc.), and redundant data that is not related to the credit reporting data processing link (such as redundant records in system logs, blank data that is repeatedly transmitted, test debugging data that is not the target business type, etc.), thereby obtaining the aforementioned credit reporting business data.

[0043] S202: After a business change occurs in the credit reporting system, the input request parameters are input into the credit reporting system so that the credit reporting system processes the input request parameters to obtain simulated output request parameters. Also, the original data is input into the credit reporting system so that the credit reporting system processes the original data to obtain simulated credit reporting data.

[0044] After implementing business changes to the credit reporting system, a regression testing process is triggered. At this time, the credit reporting subsystem can retrieve the aforementioned credit reporting business data from the business database.

[0045] The testing subsystem can assemble the input request parameters (InReq) into a request according to the format required by the transaction and input it into the credit reporting system so that the credit reporting system can process the input request parameters to obtain simulated output request parameters (OutReq(NEW)). It can also input the original data (OutResult) into the credit reporting system so that the credit reporting system can process the original data to obtain simulated credit reporting data (InResult(NEW)).

[0046] by Figure 3 For example, after the test subsystem retrieves credit reporting business data from the business database, it transmits the input request parameters (InReq) to the business subsystem through the ingress gateway, so that the business subsystem can process the input request parameters to obtain simulated output request parameters (OutReq(NEW)). It then transmits the original data (OutResult) to the business subsystem through the egress gateway, so that the business subsystem can process the original data to obtain simulated credit reporting data (InResult(NEW)). Finally, the simulated output request parameters and simulated credit reporting data are stored in the test database to achieve isolated storage of credit reporting business data and regression test data.

[0047] Further, in order to ensure the distinction and isolated storage of regression test data and credit investigation business data, the test subsystem can mark the regression test data generated in the regression test process through a test state identifier. The test subsystem can monitor and obtain target data generated by the credit investigation system in real time. The target data can be credit investigation business data generated in the actual business process or regression test data generated in the regression test process.

[0048] After the test subsystem obtains the target data, it can first determine whether the target data carries a test state identifier. If yes, it means that the target data is regression test data generated in the regression test process, and therefore it can be stored in the test database.

[0049] If the target data does not carry a test state identifier, it means that the target data is credit investigation business data generated in the actual credit investigation business process, and therefore it can be stored in the business database.

[0050] In the process of executing the actual credit investigation business of the credit investigation system, the data provider usually returns the original data based on the actual output request data. However, in the regression test process, the test environment cannot trigger the real data query process of the data provider, and the test operation may interfere with the safety of offline production data, and therefore the original data corresponding to the simulated output request parameter cannot be directly obtained from the data provider. Therefore, the original data returned by the data provider based on the actual output request data in the process of executing the actual credit investigation business of the credit investigation system is used to simulate the original data returned by the data provider based on the simulated output request parameter, so as to ensure the integrity of the data interaction link in the regression test and avoid test interruption due to the inability to obtain the original data.

[0051] In this process, the test subsystem can determine whether the current input request parameter carries a test state identifier. If yes, it means that the current operation belongs to the regression test scenario, and a real data provider request does not need to be initiated. At this time, the test subsystem can prohibit sending the output request parameter to the data provider, and input the original data as the original data returned by the data provider based on the simulated output request parameter into the credit investigation system, so that the credit investigation system processes the original data to obtain simulated credit investigation data.

[0052] S204: According to the deviation between the actual output request parameter and the simulated output request parameter, and the deviation between the actual credit investigation data and the simulated credit investigation data, it is determined whether the credit investigation system successfully completes the business change.

[0053] After the simulation output request parameters and the simulation credit data are determined, the test subsystem can determine whether the credit system successfully completes the business change according to a deviation between the actual output request parameters and the simulation output request parameters and a deviation between the actual credit data and the simulation credit data.

[0054] Specifically, the credit subsystem can determine a corresponding determination strategy according to a specific change type.

[0055] For example, when the implemented business change is a format optimization, i.e., a change of a non-core parameter (for example, a change of only a date display format in the input request parameters or a non-key remark information field), the output request parameters and the credit data generated by the changed credit system need to be guaranteed to be consistent with those before the change, and therefore, the actual output request parameters and the simulation output request parameters need to be guaranteed to be the same (i.e. ), and the actual credit data and the simulation credit data need to be guaranteed to be the same (i.e. ), so as to determine that the credit system successfully completes the business change.

[0056] If the actual output request parameters and the simulation output request parameters are different or the actual credit data and the simulation credit data are different, it indicates that the business change of the credit system is abnormal, resulting in that the system output does not meet the expected standard, at this time, data rollback can be performed on the credit system, or an abnormal positioning process is started, problems in the change link are checked by comparing business logs, and the regression test is performed again after the incorrect processing rules are corrected.

[0057] For another example, when the implemented business change is an optimization of a core data processing rule (for example, a change of only a weight coefficient in a credit score algorithm or a matching threshold of multi-source data association), the output request parameters and the credit data generated by the changed credit system are allowed to have a certain deviation from those before the change, the deviation can be determined according to a specific implemented change operation, at this time, a deviation between the actual output request parameters and the simulation output request parameters needs to be guaranteed to be not greater than a first change deviation X (i.e. ), and a deviation between the actual credit data and the simulation credit data needs to be guaranteed to be not greater than a second change deviation Y (i.e. ), so as to determine that the credit system completes the business change.

[0058] If the deviation between the actual output request parameters and the simulation output request parameters is greater than the first change deviation X or the deviation between the actual credit data and the simulation credit data is greater than the second change deviation Y, it indicates that the business change of the credit system is abnormal, resulting in that the system output does not meet the expected standard, at this time, data rollback can be performed on the credit system, or an abnormal positioning process is started, problems in the change link are checked by comparing business logs, and the regression test is performed again after the incorrect processing rules are corrected.

[0059] In practical applications, the first change bias and the second change bias can be determined by a pre-trained bias prediction model. The test subsystem can obtain business change parameters (such as weight adjustment values of a credit scoring algorithm, threshold modification amounts of data verification rules, field mapping change parameters of a data source interface, branch condition adjustment items of business logic, etc.) for a credit investigation system, and then input the credit investigation business data and the business change parameters into the pre-trained bias prediction model to determine, by the bias prediction model, the first change bias corresponding to the output request parameter and the second change bias corresponding to the credit investigation data after the credit investigation system implements the business change corresponding to the business change parameters.

[0060] Similarly, in the process of training the bias prediction model, historical business change data and actual first bias values and second bias values generated after the change can be collected first. The historical business change parameters and the original credit investigation business data are used as model input features, and the actual first bias values and the second bias values are used as label data input into the model. The loss value of the bias prediction model is determined according to the deviation between the predicted bias values (including the predicted first change bias and the predicted second change bias) output by the model and the actual bias values in the label data, and the model parameters of the bias prediction model are adjusted according to the loss value. The bias prediction model converges to a preset range or reaches a preset number of training times.

[0061] Of course, the first change bias and the second change bias can also be determined by other methods such as bias statistical analysis based on historical change cases, bias threshold tables formulated by industry experts according to business scenarios, and actual bias values collected through small-scale gray test, and the present specification does not make specific limitations thereto.

[0062] For ease of understanding, the present specification provides a whole flowchart of regression testing, as shown in Figure 4 .

[0063] For the credit investigation system, it can be determined whether the input business request carries a regression test identifier. If not, it is a normal business request, and the actual credit investigation business process is executed (i.e., the original data is requested from the data provider and the credit investigation data is returned to the data demander). The data generated in this process is cleaned to obtain credit investigation business data and stored in a business database. In practical applications, the test subsystem can perform feature analysis on the initial business data to identify invalid data, abnormal data, and redundant data contained in the initial business data, thereby realizing data cleaning of the global data generated in the actual credit investigation business and ensuring that the subsequent data playback can cover the complete business scenario of the actual credit investigation business.

[0064] If so, it indicates a regression test request. In this case, the regression test process is executed, and the cleaned credit reporting business data is retrieved from the business database. On the one hand, the input request parameters in the credit reporting business data are processed to obtain simulated output request parameters. On the other hand, based on the actual original data in the credit reporting business data, the original data returned by the business provider based on the simulated output request parameters is simulated and processed to obtain simulated credit reporting data. Then, the actual credit reporting data and the actual output request parameters are compared with the simulated credit reporting data and the simulated output request parameters respectively to obtain and output the final regression test results.

[0065] Figure 5 This is a schematic structural diagram of a device provided in an exemplary embodiment. For example... Figure 5 As shown, device 500 mainly consists of a communication interface 502, a user interface 504, a processor 506, and a data storage 508. These components are interconnected and communicate with each other via a system bus, network, or other connection mechanism 510. The communication interface 502 enables device 500 to communicate with other devices, access networks, and transmission networks via analog or digital modulation. For example, the communication interface 502 may include a chipset and antenna for wireless communication with a radio access network or access point. Furthermore, the communication interface 502 can be a wired interface such as Ethernet, Token Ring, or a USB port, or a wireless interface such as Wi-Fi, Bluetooth, Global Positioning System (GPS), or a wide-area wireless interface (e.g., WiMAX or LTE). Of course, the communication interface 502 can also support other forms of physical layer interfaces and standard or proprietary communication protocols. The communication interface 502 may also include multiple physical communication interfaces, such as Wi-Fi interfaces, Bluetooth interfaces, and wide-area wireless interfaces.

[0066] The user interface 504 includes input components such as a keypad, keyboard, touch- sensitive or presence-sensitive panel, computer mouse, trackball, joystick, microphone, still camera, and video camera, among others, and output components such as a display screen (which can be combined with a touch-sensitive panel), CRT, LCD, LED, display using DLP technology, printer, other known or future developed equivalent devices, among others. The user interface 504 can also generate auditory output through a speaker, speaker jack, audio output port, audio output device, headphones, and other known or future developed equivalent devices. In some embodiments, the user interface 504 can include software, circuitry, or other forms of logic that enables the device 500 to transmit data to and receive data from external user input / output devices. In addition or in the alternative, the device 500 can support remote access from other devices through the communication interface 502 or another physical interface (not shown). The user interface 504 can be configured to receive user input, the location and movement of which can be indicated by a pointer or cursor as described herein. The user interface 504 can also be configured as a display device for rendering or displaying a text segment.

[0067] The processor 506 can include one or more general processors and / or special purpose processors.

[0068] The data storage 508 can include one or more volatile and / or non-volatile storage components, and can be integrated in whole or in part with the processor 506. The data storage 508 can include removable and / or non-removable components.

[0069] The processor 506 is capable of executing program instructions 518 (e.g., compiled or interpreted program logic and / or machine code) stored in the data storage 508 to implement various functionality described herein. The data storage 508 can include non-transitory computer-readable media having stored thereon program instructions that, when executed by the device 500, enable the device 500 to carry out any of the methods, processes, or functions disclosed in the specification and / or drawings. Execution of the program instructions 518 by the processor 506 can involve the use of the data 512 by the processor 506.

[0070] For example, the program instructions 518 can include an operating system 522 (e.g., operating system kernel, device drivers, and / or other modules) installed on the device 500, as well as one or more application programs 520 (e.g., a browser, social application, or game application). Similarly, the data 512 can include operating system data 516 and application data 514. The operating system data 516 is primarily accessible to the operating system 522, while the application data 514 is primarily accessible to the one or more application programs 520. The application data 514 can be located in a file system that is visible or hidden to a user of the device 500.

[0071] The application programs 520 can communicate with the operating system 522 through one or more application programming interfaces (APIs). These APIs can help the application programs 520 read and / or write to the application data 514, transmit or receive information via the communication interface 502, receive or display information on the user interface 504, etc.

[0072] In some terminology, the application programs 520 can be referred to simply as "apps." In addition, the application programs 520 can be downloaded to the device 500 through one or more online application stores or application markets. However, the application programs can also be installed on the device 500 through other means, such as through a web browser or a physical interface on the device 500 (e.g., a USB port).

[0073] Referring to Figure 6 , the regression testing device can be applied to a device as shown in Figure 5 , to implement the technical solutions of the present specification. Wherein the regression testing device can include:

[0074] The acquisition module 600 is configured to acquire credit investigation business data generated by the credit investigation system in the process of performing actual credit investigation business, wherein the credit investigation business data includes: input request parameters input by a data demand side to the credit investigation system, original data returned by the data provider to the credit investigation system, actual output request parameters sent by the credit investigation system to the data provider, and actual credit investigation data returned by the credit investigation system to the data demand side.

[0075] The input module 602 is configured to input the input request parameters to the credit investigation system after a business change occurs in the credit investigation system, so that the credit investigation system performs parameter processing on the input request parameters to obtain simulated output request parameters, and input the original data to the credit investigation system, so that the credit investigation system performs business processing on the original data to obtain simulated credit investigation data.

[0076] The determination module 604 is configured to determine whether the credit investigation system successfully completes the business change according to the deviation between the actual output request parameters and the simulated output request parameters, and the deviation between the actual credit investigation data and the simulated credit investigation data.

[0077] Optionally, the credit investigation system includes a business database and a test database.

[0078] The acquisition module 600 is specifically configured to collect the credit investigation business data in a process in which the credit investigation system performs actual credit investigation business, and store the credit investigation business data into the business database; after the credit investigation system has a business change, retrieve the credit investigation business data from the business database, use the credit investigation business data to perform regression testing on the credit investigation system, and store regression testing data generated in the regression testing process in the test database.

[0079] Optionally, the acquisition module 600 is further configured to mark the regression testing data by a test state identifier; determine whether target data generated by the credit investigation system carries the test state identifier; if yes, determine that the target data is regression testing data, and store the target data in the test database; otherwise, determine that the target data is credit investigation business data, and store the credit investigation business data in the business database.

[0080] Optionally, the input module 602 is specifically configured to determine whether the input request parameter carries the test state identifier; if yes, prohibit sending the output request parameter to the data provider, and input the original data as original data returned by the data provider based on the simulated output request parameter into the credit investigation system.

[0081] Optionally, the credit investigation system comprises an entry gateway, a business subsystem, an exit gateway, a business database and a test database.

[0082] The acquisition module 600 is specifically configured to retrieve the credit investigation business data from the business database.

[0083] The input module 602 is specifically configured to transmit the input request parameter to the business subsystem through the entry gateway, so that the business subsystem performs parameter processing on the input request parameter to obtain the simulated output request parameter; transmit the original data to the business subsystem through the exit gateway, so that the business subsystem performs business processing on the original data to obtain the simulated credit investigation data; and store the simulated output request parameter and the simulated credit investigation data into the test database.

[0084] Optionally, the acquisition module 600 is specifically configured to acquire initial business data generated by the credit investigation system in a process in which the credit investigation system performs actual credit investigation business; filter invalid data that does not conform to business logic, abnormal data that fails to be processed by the credit investigation system, and redundant data irrelevant to a credit investigation data processing link in the initial business data, to obtain the credit investigation business data.

[0085] Optionally, the determining module 604 is specifically configured to: acquire a service change parameter for the credit investigation system; input the credit investigation service data and the service change parameter into a pre-trained deviation prediction model, so as to determine, by the deviation prediction model, a first change deviation corresponding to an output request parameter and a second change deviation corresponding to credit investigation data after the credit investigation system implements a service change corresponding to the service change parameter; and determine that the credit investigation system fails the regression test if a deviation between the actual output request parameter and the simulated output request parameter is greater than the first change deviation, or a deviation between the actual credit investigation data and the simulated credit investigation data is greater than the second change deviation.

[0086] For ease of description, the above apparatus is described in various modules or units in terms of functions. Of course, the functions of each module or unit can be implemented in one or more software and / or hardware when one or more of the present specification is implemented, and the modules implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The apparatus embodiments described above are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0087] Based on the same idea as the above method, the present specification also provides an electronic device, comprising: a processor; a memory for storing processor executable instructions; wherein the processor implements the steps of the method according to any one of the above embodiments by running the executable instructions.

[0088] Based on the same idea as the above method, the present specification also provides a computer readable storage medium, which stores computer instructions, and the instructions are executed by a processor to implement the steps of the method according to any one of the above embodiments.

[0089] Based on the same idea as the above method, the present specification also provides a computer program product, which comprises computer program / instructions, and the computer program / instructions are executed by a processor to implement the steps of the method according to any one of the above embodiments.

[0090] Those skilled in the art can understand that:

[0091] In this specification, the term "comprising" or "including" or any other variant thereof is intended to cover non-exclusive inclusions, such that processes, methods, products, or devices that comprise a list of elements are not limited to those elements, but can include other elements not expressly listed or inherent to such processes, methods, products, or devices. Without further limitation, additional same or equivalent elements can be included in processes, methods, products, or devices that include the listed elements.

[0092] In this specification, "one", "a", and "the" do not refer to the singular, but can include the plural as well, unless otherwise indicated.

[0093] In this specification, the first, second, etc. ordinal terms do not necessarily indicate a sequence or order, and are often used for the purpose of convenience in distinguishing one object from another. For example, the first server and the second server often refer to two servers. In order to distinguish the two servers, they are expressed as the first server and the second server. Of course, at times, the two servers can be the same server.

[0094] In this specification, unless otherwise explicitly stated, "receiving and transmitting of data" is not necessarily direct receiving and transmitting, and can be indirect receiving and transmitting. For example, A receiving data transmitted by B can be understood as A directly receiving data transmitted by B, or can be understood as A indirectly receiving data transmitted by B through C or other subjects. Similarly, B transmitting data to A can be understood as B directly transmitting data to A, or can be understood as B indirectly transmitting data to A through C or other subjects. Here, C can be one subject, or two or more subjects.

[0095] In this specification, unless otherwise explicitly stated, the association relationship generated between structures can be a direct association relationship or an indirect association relationship. For example, when describing "A is connected with B", unless it is explicitly stated that A is directly connected with B, it should be understood that A can be directly connected with B, or A can be indirectly connected with B; for another example, when describing "A is on B", unless it is explicitly stated that A is directly above B (AB is adjacent and A is above B), it should be understood that A can be directly above B, or A can be indirectly above B (there are other elements between AB and A is above B). By analogy.

[0096] This specification uses specific terms to describe embodiments thereof. Terms such as "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described herein, as well as the features of those different embodiments or examples, without contradiction.

[0097] Although one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is only one of many possible execution orders and does not represent the only execution order. Therefore, when the claims involve method steps, any changes or adjustments to the order of such steps, or the parallelism between steps, are also within the scope of protection of the claims.

Claims

1. A regression testing method, comprising: obtaining credit investigation business data generated by a credit investigation system in the process of performing actual credit investigation business, wherein the credit investigation business data comprises: input request parameters input by a data demander into the credit investigation system, original data returned by a data provider to the credit investigation system, actual output request parameters sent by the credit investigation system to the data provider, and actual credit investigation data returned by the credit investigation system to the data demander; after a business change occurs in the credit investigation system, inputting the input request parameters into the credit investigation system to make the credit investigation system perform parameter processing on the input request parameters to obtain simulated output request parameters, and inputting the original data into the credit investigation system to make the credit investigation system perform business processing on the original data to obtain simulated credit investigation data; determining whether the credit investigation system successfully completes the business change according to a deviation between the actual output request parameters and the simulated output request parameters, and a deviation between the actual credit investigation data and the simulated credit investigation data, wherein the business change parameters for the credit investigation system and the credit investigation business data are input into a pre-trained deviation prediction model to determine, by the deviation prediction model, a first change deviation of the output request parameters and a second change deviation of the credit investigation data after the business change corresponding to the business change parameters is implemented on the credit investigation system; if the deviation between the actual output request parameters and the simulated output request parameters is greater than the first change deviation, or the deviation between the actual credit investigation data and the simulated credit investigation data is greater than the second change deviation, it is determined that the credit investigation system fails the regression test.

2. The method of claim 1, the credit system comprising: a business database and a test database; obtaining credit investigation business data generated by a credit investigation system in the process of performing actual credit investigation business, specifically comprising: in the process of performing actual credit investigation business by the credit investigation system, collecting the credit investigation business data and storing the credit investigation business data into the business database; after a business change occurs in the credit investigation system, retrieving the credit investigation business data from the business database to perform regression testing on the credit investigation system by using the credit investigation business data, and storing regression testing data generated in the regression testing process in the test database. 3.The method of claim 2, before storing the regression testing data generated in the regression testing process in the test database, the method further comprises: labeling the regression testing data by a test state identifier; storing the regression testing data generated in the regression testing process in the test database, specifically comprising: determining whether target data generated by the credit investigation system carries the test state identifier; if yes, determining that the target data is regression testing data and storing it in the test database, otherwise, determining that the target data is credit investigation business data and storing it in the business database. 4.The method of claim 1, inputting the original data into the credit investigation system, specifically comprising: determining whether the input request parameters carry a test state identifier; If yes, the output request parameter is prohibited from being sent to the data provider, and the original data is input into the credit investigation system as the original data returned by the data provider based on the simulated output request parameter.

5. The method of claim 1, the credit system comprising: An entry gateway, a service subsystem, an exit gateway, a service database, and a test database; The credit investigation data generated by the credit investigation system in the process of performing actual credit investigation services is acquired, specifically including: The credit investigation data is called from the service database; The input request parameter is input into the credit investigation system to make the credit investigation system perform parameter processing on the input request parameter to obtain a simulated output request parameter, and the original data is input into the credit investigation system to make the credit investigation system perform service processing on the original data to obtain simulated credit investigation data, specifically including: The input request parameter is transmitted to the service subsystem through the entry gateway to make the service subsystem perform parameter processing on the input request parameter to obtain the simulated output request parameter, the original data is transmitted to the service subsystem through the exit gateway to make the service subsystem perform service processing on the original data to obtain the simulated credit investigation data, and the simulated output request parameter and the simulated credit investigation data are stored into the test database.

6. The method of claim 1, wherein the credit investigation data generated by the credit investigation system in the process of performing actual credit investigation services is acquired, specifically including: Initial service data generated by the credit investigation system in the process of performing actual credit investigation services is acquired; Invalid data that does not conform to service logic, abnormal data that fails to be processed by the credit investigation system, and redundant data irrelevant to the credit investigation data processing link in the initial service data are filtered to obtain the credit investigation data.

7. A regression testing device, comprising: An acquisition module configured to acquire credit investigation data generated by a credit investigation system in the process of performing actual credit investigation services, the credit investigation data including input request parameters input into the credit investigation system by a data demander, actual output request parameters sent by the credit investigation system to a data provider, actual credit investigation data returned by the credit investigation system to the data demander, and original data returned by the data provider to the credit investigation system; An input module configured to input the input request parameters into the credit investigation system to make the credit investigation system perform parameter processing on the input request parameters to obtain simulated output request parameters, and input the original data into the credit investigation system to make the credit investigation system perform service processing on the original data to obtain simulated credit investigation data. The determining module is configured to determine whether the credit investigation system successfully completes the service change according to a deviation between the actual output request parameter and the simulated output request parameter and a deviation between the actual credit investigation data and the simulated credit investigation data. The service change parameter of the credit investigation system and the credit investigation service data are input into a pre-trained deviation prediction model to determine, by the deviation prediction model, a first change deviation of an output request parameter and a second change deviation of credit investigation data after the credit investigation system is implemented with the service change corresponding to the service change parameter. If the deviation between the actual output request parameter and the simulated output request parameter is greater than the first change deviation or the deviation between the actual credit investigation data and the simulated credit investigation data is greater than the second change deviation, it is determined that the credit investigation system fails the regression test.

8. An electronic device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor implements the steps of the method of any one of claims 1-6 by running the executable instructions.

9. A computer-readable storage medium having computer instructions stored thereon, the instructions being executed by a processor to implement the steps of the method of any one of claims 1-6.

10. A computer program product comprising computer program / instructions, the computer program / instructions being executed by a processor to implement the steps of the method of any one of claims 1-6.

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