Regression test method and device, storage medium and electronic equipment

Abstract

The invention provides a regression testing method and device, a storage medium and electronic equipment, and relates to the technical field of computers. The method comprises the steps that first data and second data are obtained, the first data are regression test data, and the second data are regression test historical data; converting the first data and the second data according to preset metadata configuration information, and determining first metadata and second metadata; according to preset rule configuration information, distinguishing and judging the first metadata and the second metadata to determine abnormal data; and determining a regression test report according to the abnormal data. The regression test report is obtained by comparing and verifying the regression test data and the regression test historical data, so that the test environment is ensured to be an online environment, and the problem of online accidents caused by different test environments is solved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a regression testing method and device, a storage medium, and an electronic device. Background Art

[0002] In the construction of the order system, online regression after going live has always been a pain point in daily development. The order platform undertakes multiple business lines (sales orders, return orders, purchase orders, express delivery, express transportation, large items, cold chain) in a single system, which means that a single online change will affect multiple business lines. At the same time, the order center business has long links (involving access layer, fulfillment, warehousing, billing, distribution, procurement, replenishment and other systems) and a long time span (order acceptance, warehouse production, distribution, etc.). Manual regression is used to perform regression verification online using test scenarios, and the online environment is used to perform targeted verification of the online requirements. However, this is inefficient and the regression content is limited.

[0003] Automated regression can significantly improve efficiency compared to manual regression. Order centers now have a collection of automated regression cases, and before each launch, they regress previous cases in a test environment. However, a drawback of automated regression is that the inputs and outputs are essentially fixed. This works well in a test environment, but production environments differ from test environments. Sometimes, a single configuration error in upstream and downstream systems can lead to completely different results, causing online incidents.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0005] The present disclosure provides a regression testing method and apparatus, a storage medium, and an electronic device, which, at least to a certain extent, overcome the problem of online accidents caused by different testing environments in related technologies.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a regression testing method is provided, comprising: obtaining first data and second data, wherein the first data is regression test data and the second data is regression test history data; transforming the first data and the second data according to preset metadata configuration information to determine first metadata and second metadata; distinguishing and judging the first metadata and the second metadata according to preset rule configuration information to determine abnormal data; and determining a regression testing report based on the abnormal data.

[0008] In some embodiments, the method further includes: configuring metadata configuration information; wherein the metadata configuration information includes any one or more of the following information: metadata name, configuration effective time, data source and metadata details.

[0009] In some embodiments, the method further includes: obtaining order data, plug-in data and log data before regression testing, wherein the order data includes order details, the plug-in data includes external system data, and the log data includes order system log data; determining the second data based on the order data, plug-in data and log data before regression testing.

[0010] In some embodiments, the method further includes: determining plug-in data based on external data integrated by the plug-in; and determining log data based on the external request message and / or the internal request message.

[0011] In some embodiments, the method further includes: configuring rule configuration information; wherein, the rule configuration information includes any one or more of the following information: online name, effective time, result push, sampling rate, regression range, scene name and judgment rules, wherein, the result push includes push time, push person and push method.

[0012] In some embodiments, the first metadata and the second metadata are distinguished and judged according to the preset rule configuration information, and the determination of abnormal data includes: determining the first order data in the first metadata according to the effective time, sampling rate and regression range configured by the preset rule; judging the scene name in the first order data and the data with the same scene name in the second metadata according to the judgment rule to determine the abnormal data.

[0013] In some embodiments, after determining the regression test report based on the abnormal data, the method further includes: sending the regression test report to a terminal based on the result push information.

[0014] According to another aspect of the present disclosure, a regression testing device is provided, including: a data acquisition module, configured to acquire first data and second data, wherein the first data is regression test data and the second data is regression test history data; a metadata configuration module, configured to transform the first data and the second data according to preset metadata configuration information to determine first metadata and second metadata; a rule configuration module, configured to distinguish and judge the first metadata from the second metadata according to preset rule configuration information to determine abnormal data; and a regression test report determination module, configured to determine a regression test report based on the abnormal data.

[0015] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any one of the above-mentioned regression testing methods by executing the executable instructions.

[0016] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the regression testing method described above is implemented.

[0017] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the computer program implements any one of the above regression testing methods.

[0018] The regression testing method provided in the embodiments of the present disclosure obtains first data and second data, where the first data is regression test data and the second data is historical regression test data; transforms the first data and the second data according to preset metadata configuration information to determine the first metadata and the second metadata; distinguishes and judges the first metadata and the second metadata according to preset rule configuration information to determine abnormal data; and determines a regression test report based on the abnormal data. The present disclosure uses a comparative verification method between the regression test data and the historical regression test data to obtain a regression test report, ensuring that the testing environment is an online environment, thus resolving the problem of online incidents caused by different test environments.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0021] Figure 1 A schematic diagram showing a regression testing system structure according to an embodiment of the present disclosure is shown;

[0022] Figure 2 A schematic diagram of metadata configuration in an embodiment of the present disclosure is shown;

[0023] Figure 3 A schematic diagram of business rule configuration in an embodiment of the present disclosure is shown;

[0024] Figure 4A flow chart of a regression testing method according to an embodiment of the present disclosure is shown;

[0025] Figure 5 A flowchart showing a specific example of a regression testing method according to an embodiment of the present disclosure is shown;

[0026] Figure 6 A flowchart showing another specific example of a regression testing method according to an embodiment of the present disclosure is shown;

[0027] Figure 7 A flowchart showing another specific example of a regression testing method according to an embodiment of the present disclosure is provided;

[0028] Figure 8 A schematic diagram of regression report confirmation according to an embodiment of the present disclosure is shown;

[0029] Figure 9 A schematic diagram of a regression testing device according to an embodiment of the present disclosure is shown;

[0030] Figure 10 A structural block diagram of a computer device according to an embodiment of the present disclosure is shown;

[0031] Figure 11 A schematic diagram illustrating a computer-readable storage medium in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0033] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0034] The specific implementation of the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0035] Figure 1 FIG. 1 shows an exemplary application system architecture diagram to which the regression testing method in the embodiment of the present disclosure can be applied. Figure 1As shown, the system architecture may include an order center service 101 , a network 102 , and a business rule validation tool 103 .

[0036] The network 102 is a medium used to provide a communication link between the order center service 101 and the business rule verification tool 103 , and can be a wired network or a wireless network.

[0037] Optionally, the above-mentioned wireless network or wired network uses standard communication technologies and / or protocols. The network is typically the Internet, but it can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network). In some embodiments, technologies and / or formats including Hypertext Markup Language (HTML), Extensible Markup Language (XML), etc. are used to represent data exchanged over the network. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPSec), etc. can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

[0038] The order center service 101 sends the online order data to the business rule verification tool 103 via the network.

[0039] The business rule verification tool includes a configuration module 1031 , a calculation module 1032 , a monitoring module 1033 and a statistics module 1034 .

[0040] Configuration module 1031 includes business rule configuration 10311 and metadata configuration 10312. Metadata configuration 10312 extracts relevant messages from the log based on the orders within the regression range, processes and transforms the data according to the metadata configuration, and saves it. Metadata management allows for low-code monitoring configuration without the need for additional code development.

[0041] Metadata configuration diagram is as follows Figure 2For example, in a specific example, the metadata configuration 10312 is designed according to the following Table 1:

[0042] Table 1: Metadata storage table design

[0043]

[0044]

[0045] Among the data sources, order data refers to order details; customized plug-ins refer to external system data, such as inventory flow and waybill status. Since the order center cannot redundantly have data from other systems, the integration of external data is achieved through customized plug-ins. The functions of the plug-ins are inventory flow query, waybill status comparison, etc.; log data is in daily development. The order system will record relevant request information and corresponding results in the log. These messages can be requests from external systems to the order system (order acceptance, cancellation, warehouse production information feedback, etc.), or requests from the order system to external systems (such as query of product details messages and inventory flow deductions).

[0046] The data structure includes: Order represents the object, Class represents the type of the field, and Desc represents the business meaning of the field. In an example, Order is the order data.

[0047] Business rule configuration diagram is as follows Figure 3 For example, in a specific example, the business rule configuration 10311 is designed according to the following Table 2:

[0048] Table 2: Metadata storage table design

[0049]

[0050] Launch Name: Refers to the specific launch requirement name. This indicates that this configuration is a regression solution based on this requirement.

[0051] Effective time: During the effective time of the rule, the regression platform will screen the data that meets the rule for statistical analysis.

[0052] Result push related information includes push time, online on-duty personnel, and push settings. The push time is a scheduled task configured using a time expression (Cron expression, a string expression used to specify a task to be executed at a certain time point or periodically). When the scheduled task is triggered, the relevant data will be summarized to generate a report, and the online on-duty personnel will be notified to accept the report according to the push settings.

[0053] Sampling rate: For scenarios with large data volumes, support sampling partial data for verification to reduce pressure on online production systems.

[0054] Regression Scope: Information about the business lines affected by this rollout. Since the order system includes multiple business lines, you need to specify which business line the data to be regressed belongs to and the rollout start time. For example, the Vendor Managed Inventory (VMI) business line.

[0055] Scenarios: Extreme scenarios that require attention before going online, such as cancellations where the order doesn't exist, orders created from the warehouse management system (WMS), and unusual retail status comparisons. Under "Scenarios," you can configure the scenario name and corresponding rules. Rules are expressions based on metadata configuration. For example, cancellations where the order doesn't exist require information about the cancellation message. This cancellation message requires metadata for the log source to be configured, and then log cleaning is performed to store the corresponding cancellation message. Finally, statistics are generated.

[0056] The calculation module 1032 includes data calculation 10321, which is used to filter eligible orders according to the rule configuration, at the rule effective time, combined with the sampling rate. When the push time configured by the user is received, the data source data in the rule configuration is extracted according to the rule configuration, and the orders within the regression range are calculated to calculate the data distribution under various abnormal scenarios.

[0057] The monitoring module 1033 includes a monitoring center 10331 and an abnormal alarm 10332 .

[0058] The statistics module 1034 includes data reports 10341 and report push 10342 .

[0059] In a specific example, the regression testing system includes the following steps during operation: configuring multiple scenario rules; obtaining input data based on screening rules; extracting data from order data and real-time logs; matching scenario data according to rules and generating reports; identifying problematic orders in reports and issuing alerts; tracking orders, comparing status data, and locating problems.

[0060] The regression testing system compiles and pushes all data related to the user's scenarios according to the user's configured report delivery schedule. The system displays the overall sampled data and the data distribution for each specific anomaly scenario, and provides the order number of the anomaly for easy troubleshooting. Once R&D confirms the report is correct, they can click "Confirm." The regression testing system archives the relevant reports, promoting the standardization of the online verification process.

[0061] Those skilled in the art will know that Figure 1The number of order center services, networks, and business rule verification tools is merely illustrative, and any number of terminal devices, networks, and servers may be provided based on actual needs. This disclosure does not limit this.

[0062] Figure 4 A flow chart of a regression testing method according to an embodiment of the present disclosure is shown as follows: Figure 4 As shown, the regression testing method provided in the embodiment of the present disclosure includes the following steps:

[0063] S402 , obtaining first data and second data, wherein the first data is regression test data, and the second data is regression test history data.

[0064] It should be noted that the above-mentioned first data may be data generated in an online environment when performing regression testing, for example, data obtained by testing in an online environment after the code is modified. The above-mentioned second data may be data that has already been generated in an online environment before performing regression testing, for example, data obtained by testing in an online environment before the code is modified. The above-mentioned regression test may be a testing process that detects whether new problems or errors have appeared in the product by re-executing already passed test cases when the software code, usage environment or product requirements have changed. For example, after modifying the old code, retesting is performed to confirm that the modification has not introduced new errors or caused errors in other codes.

[0065] S404: Convert the first data and the second data according to preset metadata configuration information to determine the first metadata and the second metadata.

[0066] It should be noted that the metadata described above can be information about the organization, domains, and relationships of data. In other words, metadata is data that describes data. For example, data reflects real-world objects, events, activities, and relationships, while metadata reflects the structure, characteristics, relationships, and management of data. The metadata configuration information described above can be the configuration (conversion) of data into corresponding metadata according to preset rules.

[0067] In one example, before step S404, the following step is also included: configuring metadata configuration information; the metadata configuration information includes any one or more of the following information: metadata name, configuration effective time, data source, and metadata details. Through metadata management, monitoring configuration can be completed in a low-code format without the need for additional code development.

[0068] For example, in a specific example, the metadata configuration information is designed according to Table 1 above.

[0069] S406: According to the preset rule configuration information, the first metadata and the second metadata are distinguished and judged to determine abnormal data.

[0070] It should be noted that the above abnormal data can be data with different states in the same scenario. For example, in the order cancellation scenario, the order status should also be "existing". If the order status is "non-existing", it is judged as abnormal data.

[0071] In a specific example, the metadata configuration information is designed according to Table 2 above.

[0072] In one example, before S406, the following step is also included: configuring rule configuration information; the rule configuration information includes any one or more of the following: online name, effective time, result push, sampling rate, regression range, scenario name, and judgment rule. The result push includes the push time, push personnel, and push method. Through rule configuration, important scenarios can be regressed, and online data can be used for targeted monitoring.

[0073] S408: Determine a regression test report based on the abnormal data.

[0074] It should be noted that the regression test report can include any one or more of the following information: launch name, filter data range, and result details. The filter data range includes any one or more of the following information: request message source, configuration effective time, and sampling rate. The result details include any one or more of the following information: scenario name, number of hits, and order number (which can be viewed by going to order tracking).

[0075] The present disclosure obtains a regression test report by comparing regression test data with historical regression test data, ensuring that the testing environment is an online environment, and solving the problem of online accidents caused by different test environments.

[0076] In one embodiment of the present disclosure, Figure 5 As shown, the regression testing method provided in the embodiment of the present disclosure can determine the second data through the following steps, and can compare the waybill status to ensure the reliability of the second data:

[0077] S502, obtaining order data, plug-in data, and log data before regression testing, wherein the order data includes order details, the plug-in data includes external system data, and the log data includes order system log data;

[0078] S504: Determine second data based on the order data, plug-in data, and log data before the regression test.

[0079] In one embodiment of the present disclosure, order data, plug-in data, and log data during regression testing are obtained; and first data is determined based on the order data, plug-in data, and log data during regression testing.

[0080] In one embodiment of the present disclosure, Figure 6 As shown, the regression testing method provided in the embodiment of the present disclosure can determine the plug-in data and log data through the following steps, can obtain data through different channels, and compare and verify the same data in different channels to ensure the reliability of the data:

[0081] S602, integrating external data according to the plug-in and determining plug-in data;

[0082] S604: Determine log data according to the external request message and / or the internal request message.

[0083] For example, data obtained from external system data through plug-ins is plug-in data, such as inventory flow and waybill status. By integrating external data through customized plug-ins, data from different sources can be obtained to achieve waybill status comparison.

[0084] In one example, the log data is determined based on a message from the external system requesting the order system and / or a message from the order system requesting the external system.

[0085] For example, in daily development, system logs record relevant request information and corresponding results as log data. These messages can be requests from external systems to the order system, or from the order system to external systems. Log cleaning converts log data into log messages that the system can recognize based on metadata configuration information.

[0086] In one embodiment of the present disclosure, Figure 7 As shown, the regression testing method provided in the embodiment of the present disclosure can determine abnormal data through the following steps, which can quickly and accurately determine abnormal data:

[0087] S702, determining first order data in the first metadata according to the effective time, sampling rate, and regression range configured in the preset rule;

[0088] S704: The scene name in the first order data and the data with the same scene name in the second metadata are judged according to the judgment rule to determine abnormal data.

[0089] The regression report confirmation diagram is as follows Figure 8As shown. ESL is the prefix of the sales order, and 001-006 is the number. For example, the effective time is from 2023-08-10 20:00:00 to 2023-08-18 20:00:00, the sampling rate is 50 (a number between 0 and 100), and the regression range is the source address C of the request message. Based on the above three conditions, the first order data is filtered out from the first metadata. The scenario of interest is the scenario where the order does not exist when the order is canceled. Orders with the scenario name "Order Cancelled" are found in the first order data. The order status of the order with the scenario name "Order Cancelled" in the second metadata indicates that the order status exists. Therefore, if the order status of order A with the scenario name "Order Cancelled" in the first order data is "Not Existing", order A is judged to be abnormal data.

[0090] In one embodiment of the present disclosure, after determining a regression test report based on abnormal data, the regression testing method provided in this embodiment further includes: sending the regression test report to a terminal based on result push information. By dynamically configuring the result push information, the regression test report can be accurately pushed to the terminal based on different needs.

[0091] For example, the abnormal data is persisted and a report is sent. Users can check whether there is erroneous data in the abnormal scenario based on the relevant report. If there is no problem, they can confirm the regression report.

[0092] This disclosure takes order data as the core, integrates various upstream and downstream systems in the form of plug-ins, performs offline calculations through clear log data collection, quickly counts the data situation after going online, and promptly discovers possible problems in critical insurance scenarios.

[0093] It should be noted that the acquisition, storage, use, and processing of data in the technical solution disclosed herein are in compliance with the relevant provisions of national laws and regulations. Various types of data such as personal identity data, operation data, behavioral data, etc. related to individuals, customers, and groups obtained in the embodiments of the present disclosure have been authorized.

[0094] Based on the same inventive concept, the present disclosure also provides a regression testing device, as described in the following embodiment. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0095] Figure 9 A schematic diagram of a regression testing device according to an embodiment of the present disclosure is shown. Figure 9 As shown, the device includes: a data acquisition module 91, a metadata configuration module 92, a rule configuration module 93, a regression test report determination module 94 and a report sending module 95.

[0096] The data acquisition module 91 is configured to acquire first data and second data, wherein the first data is regression test data and the second data is regression test history data;

[0097] A metadata configuration module 92 is configured to convert the first data and the second data according to preset metadata configuration information to determine the first metadata and the second metadata;

[0098] A rule configuration module 93 is configured to distinguish and judge the first metadata from the second metadata according to preset rule configuration information to determine abnormal data;

[0099] The regression test report determination module 94 is used to determine a regression test report based on the abnormal data.

[0100] In one example of the present disclosure, the metadata configuration module 92 is further used to configure metadata configuration information; wherein the metadata configuration information includes any one or more of the following information: metadata name, configuration effective time, data source and metadata details.

[0101] In one example of the present disclosure, the above-mentioned data acquisition module 91 is also used to obtain order data, plug-in data and log data before regression testing, wherein the order data includes order details, the plug-in data includes external system data, and the log data includes order system log data; the second data is determined based on the order data, plug-in data and log data before regression testing.

[0102] In an example of the present disclosure, the data acquisition module 91 is further configured to integrate external data according to the plug-in to determine plug-in data; and to determine log data according to external request messages and / or internal request messages.

[0103] In one example of the present disclosure, the above-mentioned rule configuration module 93 is also used to configure rule configuration information; wherein, the rule configuration information includes any one or more of the following information: online name, effective time, result push, sampling rate, regression range, scene name and judgment rules, wherein, the result push includes push time, push personnel and push method.

[0104] In one example of the present disclosure, the above-mentioned rule configuration module 93 is also used to determine the first order data in the first metadata according to the effective time, sampling rate and regression range configured by the preset rules; the scene name in the first order data and the data with the same scene name in the second metadata are judged according to the judgment rules to determine the abnormal data.

[0105] In an example of the present disclosure, the regression testing apparatus further includes a report sending module 95 configured to send a regression test report to a terminal according to result push information.

[0106] It should be noted that the aforementioned data acquisition module 91, metadata configuration module 92, rule configuration module 93, and regression test report determination module 94 correspond to S402 to S408 in the method embodiment. The examples and application scenarios implemented by these modules and corresponding steps are the same, but are not limited to the contents disclosed in the aforementioned method embodiment. It should be noted that these modules, as part of the apparatus, can be executed in a computer system, such as a set of computer-executable instructions.

[0107] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods, or program products. Therefore, various aspects of the present disclosure may be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0108] Refer to the following Figure 10 1000 according to this embodiment of the present disclosure will be described. Figure 10 The electronic device 1000 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0109] like Figure 10 As shown, electronic device 1000 is implemented as a general-purpose computing device. Components of electronic device 1000 may include, but are not limited to, the aforementioned at least one processing unit 1010, the aforementioned at least one storage unit 1020, and a bus 1030 connecting various system components (including storage unit 1020 and processing unit 1010).

[0110] The storage unit stores program codes, which can be executed by the processing unit 1010, so that the processing unit 1010 executes the steps described in the above “Exemplary Method” section of this specification according to various exemplary embodiments of the present disclosure.

[0111] For example, the processing unit 1010 can execute the following steps of the above method embodiment: obtaining first data and second data, wherein the first data is regression test data and the second data is regression test historical data; converting the first data and the second data according to preset metadata configuration information to determine the first metadata and the second metadata; distinguishing and judging the first metadata and the second metadata according to preset rule configuration information to determine abnormal data; and determining a regression test report based on the abnormal data.

[0112] For example, the processing unit 1010 may execute the following steps of the above method embodiment: configuring metadata configuration information; wherein the metadata configuration information includes any one or more of the following information: metadata name, configuration effective time, data source and metadata details.

[0113] For example, the processing unit 1010 can execute the following steps of the above method embodiment: obtaining order data, plug-in data and log data before regression testing, wherein the order data includes order details, the plug-in data includes external system data, and the log data includes order system log data; and determining the second data based on the order data, plug-in data and log data before regression testing.

[0114] For example, the processing unit 1010 may execute the following steps of the above method embodiment: determine plug-in data according to the plug-in integration external data; and determine log data according to the external request message and / or the internal request message.

[0115] For example, the processing unit 1010 can execute the following steps of the above method embodiment: configure rule configuration information; wherein the rule configuration information includes any one or more of the following information: online name, effective time, result push, sampling rate, regression range, scene name and judgment rules, wherein the result push includes push time, push person and push method.

[0116] For example, the processing unit 1010 can execute the following steps of the above method embodiment: determine the first order data in the first metadata according to the effective time, sampling rate and regression range configured by the preset rules; judge the scene name in the first order data and the data with the same scene name in the second metadata according to the judgment rules to determine the abnormal data.

[0117] For example, the processing unit 1010 may execute the following steps of the above method embodiment: sending the regression test report to the terminal according to the result push information.

[0118] The storage unit 1020 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 10201 and / or a cache memory unit 10202 , and may further include a read-only memory unit (ROM) 10203 .

[0119] The storage unit 1020 may also include a program / utility 10204 having a set (at least one) of program modules 10205, such program modules 10205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0120] Bus 1030 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0121] The electronic device 1000 may also communicate with one or more external devices 1040 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 1000, and / or any device that enables the electronic device 1000 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may occur via an input / output (I / O) interface 1050. Furthermore, the electronic device 1000 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 1060. As shown, the network adapter 1060 communicates with other modules of the electronic device 1000 via the bus 1030. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1000, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0122] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0123] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer program product, which includes: a computer program, which implements the above regression testing method when executed by a processor.

[0124] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is further provided. The computer-readable storage medium may be a readable signal medium or a readable storage medium. Figure 11 A schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure is shown. Figure 11As shown, the computer-readable storage medium data download stores a program product capable of implementing the above-mentioned method of the present disclosure. In some possible implementations, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Methods" section above.

[0125] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0126] In the present disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0127] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0128] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a standalone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0129] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0130] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0131] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0132] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A regression testing method, characterized in that: include: Acquire first data and second data, wherein the first data is regression test data and the second data is regression test historical data; Convert the first data and the second data according to preset metadata configuration information to determine first metadata and second metadata; According to the preset rule configuration information, the first metadata and the second metadata are distinguished and judged to determine abnormal data; A regression test report is determined based on the abnormal data.

2. The regression testing method according to claim 1, wherein: The method further comprises: Configure metadata configuration information; The metadata configuration information includes any one or more of the following information: metadata name, configuration effective time, data source and metadata details.

3. The regression testing method according to claim 1, wherein: The method further comprises: Obtaining order data, plug-in data, and log data before regression testing, wherein the order data includes order details, the plug-in data includes external system data, and the log data includes order system log data; The second data is determined based on the order data, plug-in data and log data before the regression test.

4. The regression testing method according to claim 3, wherein: The method further comprises: Determine the plug-in data based on the plug-in's integration of external data; Log data is determined based on the external request message and / or the internal request message.

5. The regression testing method according to any one of claims 1 to 4, characterized in that: The method further comprises: Configuration rule configuration information; The rule configuration information includes any one or more of the following information: Online name, effective time, result push, sampling rate, regression range, scenario name and judgment rules, among which the result push includes push time, push person and push method.

6. The regression testing method according to claim 5, characterized in that: The configuring information according to the preset rules, distinguishing and judging the first metadata and the second metadata, and determining abnormal data includes: Determine the first order data in the first metadata according to the effective time, sampling rate, and regression range configured by the preset rule; The scene name in the first order data and the data with the same scene name in the second metadata are judged according to the judgment rule to determine abnormal data.

7. The regression testing method according to claim 5, wherein: After determining the regression test report based on the abnormal data, the method further includes: According to the result push information, the regression test report is sent to the terminal.

8. A regression testing device, characterized in that: include: A data acquisition module, configured to acquire first data and second data, wherein the first data is regression test data and the second data is regression test history data; a metadata configuration module, configured to transform the first data and the second data according to preset metadata configuration information to determine first metadata and second metadata; A rule configuration module, configured to distinguish and judge the first metadata from the second metadata according to preset rule configuration information, and determine abnormal data; The regression test report determination module is used to determine the regression test report according to the abnormal data.

9. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to perform the regression testing method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the regression testing method according to any one of claims 1 to 7 is implemented.

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