Interface iteration risk warning method, device and equipment and storage medium

Abstract

This invention relates to the field of software development technology and discloses a method, apparatus, device, and storage medium for interface iteration risk alarm. The method includes: acquiring data returned by the interface after an iterative update as comparison data, and acquiring pre-stored data returned by the interface before the iterative update as reference data; converting the comparison data and reference data into a structured data model in a preset format using a nested structure to generate a first structured data representation and a second structured data representation; performing a multi-dimensional comparison based on the first and second structured data representations to obtain a multi-dimensional comparison result; and determining whether to trigger an alarm notification based on the business impact of the multi-dimensional comparison result. Applying the technical solution of this invention can solve the problems of inefficiently handling complex data structure comparisons during interface iteration and the imperfect alarm mechanism in the prior art.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of software development technology, specifically to an interface iteration risk alarm method, apparatus, device, and storage medium. Background Technology

[0002] In software development and maintenance, interfaces serve as the core hub for interaction between front-end and back-end systems, and their stability directly impacts the overall system's functionality and business continuity. With the rapid iteration of software product requirements, back-end interfaces often need frequent adjustments to adapt to new features (such as adding fields, modifying data structures, and optimizing nesting levels).

[0003] However, changes to interfaces often come with compatibility risks: if core fields are accidentally deleted, field data types are modified, or data hierarchy structures are adjusted during iteration, it may lead to abnormal front-end page rendering, failure of downstream service calls, or even system-level cascading failures. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide an interface iteration risk alarm method, apparatus, device and storage medium to solve the problems of inefficient processing of complex data structure comparisons in interface iteration and imperfect alarm mechanisms in the prior art.

[0005] According to one aspect of the present invention, an interface iteration risk alarm method is provided, the method comprising:

[0006] Get the data returned by the interface after the iteration update as the comparison data, and get the data returned by the interface before the iteration update that was stored in advance as the reference data;

[0007] The data to be compared and the reference data are converted into a structured data model in a preset format using a nested structure, generating a first structured data representation and a second structured data representation;

[0008] A multi-dimensional comparison is performed based on the first structured data representation and the second structured data representation to obtain the multi-dimensional comparison result;

[0009] Based on the business impact of the multi-dimensional comparison results, determine whether to trigger an alarm notification.

[0010] According to another aspect of the present invention, an interface iteration risk alarm device is provided, comprising:

[0011] The acquisition module is used to acquire the data returned by the interface after the iteration update as the comparison data, and to acquire the data returned by the interface before the iteration update that was stored in advance as the reference data.

[0012] The conversion module is used to convert the data to be compared and the reference data into a structured data model in a preset format organized with a nested structure, and generate a first structured data representation and a second structured data representation.

[0013] The comparison module is used to perform multi-dimensional comparison based on the first structured data representation and the second structured data representation to obtain multi-dimensional comparison results;

[0014] The determination module is used to determine whether to trigger an alarm notification based on the degree of business impact of the multi-dimensional comparison results.

[0015] According to another aspect of the present invention, an interface iteration risk alarm device is provided, comprising:

[0016] The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus.

[0017] The memory is used to store at least one executable instruction that causes the processor to perform the interface iteration risk warning method as described above.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing an interface iteration risk alarm device / apparatus to perform the operation of the interface iteration risk alarm method as described above.

[0019] This invention converts the data to be compared and the reference data into a unified nested structured data model and performs multi-dimensional comparisons. This comprehensive detection capability ensures that every detail of interface changes is captured, avoiding the problem of missed changes caused by the single detection dimension in traditional methods. Converting the interface-returned data into a pre-formatted nested structured data model provides a standardized input format for comparison. This standardization not only improves the accuracy and efficiency of the comparison but also makes the comparison process more stable and scalable. Regardless of how the data format returned by the interface changes, as long as it is converted into a unified structured model, effective comparison can be performed. The mechanism of determining whether to trigger an alarm notification based on the business impact of the multi-dimensional comparison results makes alarms more intelligent and accurate, avoiding unnecessary alarm interference.

[0020] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0021] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0022] Figure 1 This invention illustrates a flowchart of a first embodiment of the interface iteration risk warning method provided by the present invention.

[0023] Figure 2 A flowchart illustrating a second embodiment of the interface iteration risk warning method provided by the present invention is shown.

[0024] Figure 3 This diagram illustrates the application of the interface iteration risk alarm method provided by the present invention.

[0025] Figure 4 A schematic diagram of an embodiment of the interface iteration risk alarm device provided by the present invention is shown;

[0026] Figure 5 A schematic diagram of an embodiment of the interface iteration risk alarm device provided by the present invention is shown. Detailed Implementation

[0027] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0028] Figure 1 A flowchart of a first embodiment of the interface iteration risk warning method of the present invention is shown. This method is executed by a computing device, such as a server, cloud platform, or personal computer. Figure 1 As shown, the method includes the following steps:

[0029] Step 110: Obtain the data returned by the interface after the iteration update as the comparison data, and obtain the data returned by the interface before the iteration update that was stored in advance as the reference data.

[0030] In this step, two sets of data are obtained for comparison: one is the interface return data after the current iteration update (data to be compared), and the other is the interface return data before the iteration update (reference data).

[0031] Step 120: Convert the data to be compared and the reference data into a structured data model in a preset format with a nested structure, and generate the first structured data representation and the second structured data representation.

[0032] In this step, the two sets of data are converted into a unified, nested structured data model for subsequent multi-dimensional comparison. The converted data are referred to as the first structured data representation (data to be compared) and the second structured data representation (reference data).

[0033] Step 130: Perform a multi-dimensional comparison based on the first structured data representation and the second structured data representation to obtain the multi-dimensional comparison results.

[0034] Perform a multi-dimensional comparison of the two sets of structured data. The comparison dimensions include, but are not limited to, field existence, field values, data types, comment completeness, non-functional attributes (such as field length and enumeration value range), and the hierarchical relationship of the fields. After the comparison is completed, generate multi-dimensional comparison results and record all differences found.

[0035] Step 140: Based on the business impact of the multi-dimensional comparison results, determine whether to trigger an alarm notification.

[0036] In this step, based on the business impact of the multi-dimensional comparison results, a decision is made on whether to trigger an alarm notification to promptly identify and address interface changes that may affect business operations. For example, a pre-defined mapping relationship between difference types and business impact levels can be queried to determine the business impact level of each difference in the multi-dimensional comparison results. Based on the business impact level, a decision is made on whether to trigger an alarm notification. For example, high-impact differences trigger a critical alarm, while low-impact differences only log. This intelligent alarm mechanism avoids alarm overload, ensuring that development and operations personnel can promptly focus on truly important interface changes and reduce potential business risks.

[0037] This invention converts the data to be compared and the reference data into a unified nested structured data model and performs multi-dimensional comparisons. This comprehensive detection capability ensures that every detail of interface changes is captured, avoiding the problem of missed changes caused by the single detection dimension in traditional methods. Converting the interface-returned data into a pre-formatted nested structured data model provides a standardized input format for comparison. This standardization not only improves the accuracy and efficiency of the comparison but also makes the comparison process more stable and scalable. Regardless of how the data format returned by the interface changes, as long as it is converted into a unified structured model, effective comparison can be performed. The mechanism of determining whether to trigger an alarm notification based on the business impact of the multi-dimensional comparison results makes alarms more intelligent and accurate, avoiding unnecessary alarm interference.

[0038] Figure 2 A flowchart of another embodiment of the interface iteration risk warning method of the present invention is shown. This method is executed by a computing device, such as a server, cloud platform, or personal computer. Figure 2As shown, the method includes the following steps:

[0039] Step 210: Obtain the data returned by the interface after the iteration update as the comparison data, and obtain the data returned by the interface before the iteration update that was stored in advance as the reference data.

[0040] In this step, an automated script triggers the comparison process. Once triggered, the script automatically completes a series of operations, including data acquisition, transformation, and comparison, and generates comparison results or triggers alarm notifications according to preset rules. This significantly improves work efficiency, reduces errors and delays that may result from manual operations, and makes interface change management more efficient and reliable.

[0041] Step 220: Convert the data to be compared and the reference data into a structured data model in a preset format with a nested structure, and generate the first structured data representation and the second structured data representation.

[0042] In one alternative approach, the data to be compared and the reference data are converted into a structured data model in a preset format organized using a nested structure, generating a first structured data representation and a second structured data representation. This may specifically include the following steps:

[0043] The data to be compared and the reference data are converted into a tree structure model to generate a first structured data representation and a second structured data representation.

[0044] A tree structure is a hierarchical data structure that clearly represents the hierarchical and nested relationships of data. Each node can have multiple child nodes, but only one parent node. This structure is well-suited for representing nested data returned by APIs (such as data in JSON or XML format).

[0045] In this embodiment, data parsing tools (such as JSON parsers or XML parsers) are used to parse the data to be compared and the reference data into operable data structures. The parsed data is then constructed into a tree structure according to its hierarchical relationship. The tree structure clearly represents the hierarchical relationship of the data, making it easy to identify changes in the hierarchical relationship of fields during the comparison process. The constructed tree structure models are labeled as the first structured data representation (data to be compared) and the second structured data representation (reference data), respectively.

[0046] Step 230: Perform a multi-dimensional comparison between the first structured data representation and the second structured data representation, identify the changed dimensions and the differential information corresponding to each dimension, and generate multi-dimensional comparison results.

[0047] Change dimensions include at least one of the following: changes in hierarchical relationships, changes in data types, differences in annotation completeness, changes in non-functional attributes, and missing or added fields.

[0048] Differentiated information refers to details of the differences.

[0049] Identifying changes in the hierarchical relationship of fields refers to checking whether the hierarchical relationship of fields has changed, such as whether the parent-child relationship or nesting level of fields has changed. This is done by traversing the tree structure to check whether the hierarchical path of the fields is consistent.

[0050] Identifying data type changes refers to checking whether the data type of a field has changed. For example, a field may exist in two versions, but the data type is different (e.g., from integer to string). By checking the data type of the field, fields with inconsistent types can be marked.

[0051] Identifying discrepancies in annotation completeness involves checking whether field annotations are complete and consistent. For example, a field may have annotations in the reference data but be missing in the data being compared; or, a field may have annotations in both versions, but the annotation content differs. By examining the field annotation information, fields with missing or changed annotations can be identified.

[0052] Identifying changes to non-functional attributes involves checking whether the non-functional attributes of a field have changed. This is done by examining the field's metadata and marking fields whose non-functional attributes have changed.

[0053] Identifying missing or added fields refers to checking for fields that exist in one data structure but not in the reference data, or fields that exist in the reference data but not in the data to be compared. This is done by iterating through the fields of both data structures, comparing field names, and marking added and deleted fields.

[0054] One approach is to use a tree-based difference detection algorithm to compare two sets of structured data node by node.

[0055] In one alternative approach, non-functional attribute changes include at least one of the following: field length change, enumeration value range change, data format constraint change, default value change, or numerical precision change.

[0056] Non-functional attribute changes refer to changes in certain non-core functional attributes of fields in the data returned by the interface. While these changes do not directly affect the basic functionality of the fields (such as their existence or value), they can significantly impact data usage, validation, and processing. By detecting changes in attributes such as field length, enumeration value range, data format constraints, default values, and numerical precision, the potential impact of interface changes can be comprehensively identified, providing accurate data for subsequent business impact assessments and alert notifications.

[0057] The comparison should focus on the semantic level of non-functional attributes of fields (such as the range of enumerated values ​​and field length limits), rather than relying solely on text similarity analysis. For example, when a field annotation changes from "1: Active" to "1: Normal", it is necessary to determine whether the change constitutes a substantial adjustment to the business semantics, rather than a simple difference in expression.

[0058] In one optional approach, a multi-dimensional comparison is performed based on the first structured data representation and the second structured data representation to obtain the multi-dimensional comparison result. Specifically, this may include the following steps:

[0059] The multi-dimensional comparison task targeting the first and second structured data representations is split into multiple subtasks and assigned to different computing nodes for execution.

[0060] In this implementation, by breaking down the multi-dimensional comparison task into multiple subtasks and assigning them to different computing nodes for execution, the comparison efficiency can be significantly improved and the processing time reduced. This distributed computing strategy is particularly suitable for large-scale data comparison scenarios, as it can fully utilize the parallel processing capabilities of multiple nodes to improve the overall performance and scalability of the system.

[0061] Step 240: Based on the business impact of the multi-dimensional comparison results, determine whether to trigger an alarm notification.

[0062] In one alternative approach, the determination of whether to trigger an alarm notification is based on the degree of business impact of multi-dimensional comparison results may include the following steps:

[0063] If there is a difference in at least one dimension in the multi-dimensional comparison results, then identify the type of difference to which the difference belongs;

[0064] Based on the difference type to which the difference belongs, determine the degree of business impact of the difference. The difference type must include at least one of the following: field addition type, field deletion type, field value modification type, data type change type, nested structure change type, comment integrity difference type, and non-functional attribute change type.

[0065] Based on the degree of business impact of the differences, determine whether to trigger an alarm notification.

[0066] In this implementation, when at least one dimension of the multi-dimensional comparison results shows a difference, the detected differences are classified to determine their specific difference type. Based on the specific difference type, the business impact of the difference is assessed. A preset mapping table between difference types and business impact levels can be established. Specifically, the business impact level corresponding to field deletion is Level 1; the business impact level corresponding to changes in nested structures is Level 1; the business impact level corresponding to changes in non-functional attributes is Level 2, and so on. Based on the difference type of each difference in the multi-dimensional comparison results, the mapping table is queried to determine the business impact level of each difference, thereby deciding whether to trigger an alarm notification.

[0067] In one alternative approach, the business impact of the difference is determined based on its type, which may include the following steps:

[0068] Query the pre-configured difference type impact weight table, and determine the basic impact score of the difference based on the difference type to which the difference belongs. The weight table includes the basic impact score corresponding to each difference type.

[0069] Based on the business criticality level of the field where the difference exists, the basic impact score is weighted and adjusted to obtain a quantitative value of the degree of business impact of the difference.

[0070] In this implementation, a pre-configured weight table assigns a basic impact score to each type of difference to initially assess its potential impact. Based on the difference type, the corresponding basic impact score is retrieved from the weight table. The basic impact score is then weighted and adjusted according to the business criticality level of the field containing the difference. The quantified business impact of the difference is calculated using this weighted basic impact score. The business criticality level refers to the importance of a data field to business operations. This mechanism enables a quantitative assessment of the business impact of differences, ensuring that alarm notifications are triggered more scientifically and rationally.

[0071] The interface iteration risk warning method provided by this invention can be integrated into continuous integration (CI) and continuous delivery / deployment (CD) processes, in addition to comparing the updated interfaces. It can automatically perform interface iteration risk detection operations during code submission and build phases, realizing a proactive prevention and control mechanism of "detecting changes as they occur". It also supports the archiving of historical difference records, making it easy to trace the interface change trajectory.

[0072] Furthermore, each difference in the multi-dimensional comparison results can be categorized as a functional difference or a non-functional difference based on its attributes, and clearly identified and distinguished in the visualization interface and structured report.

[0073] Reference Figure 3 The diagram illustrates the application of the interface iteration risk alerting method provided by this invention. It is initiated via a "timed trigger" node, pulling the front-end machine logs from the platform for the most recent 15 minutes. A four-step cleaning process is then performed: first, deduplication to eliminate duplicate data; second, removal of advertising parameters (such as ad parameters) to purify the data; third, stripping domain information to focus on core data; and finally, storing the parameter information in a database to ensure data purity and analyzability. After data cleaning, parallel traffic replay verification is performed in two environments: on one hand, real traffic pressure is simulated in the online environment, and the real traffic data from the first 15 minutes is replayed to the production environment to verify system stability; on the other hand, regression testing is conducted in the test environment for new versions or changes to ensure the correctness of the functionality. When launching new features, the development team does not directly deploy them to the online environment for users to test and error. Instead, they first conduct replay tests in the test environment using pre-recorded real traffic data to check whether the new interface is compatible with the old data, whether there are calculation errors, and whether the page is stable. Furthermore, for interfaces handling extremely large amounts of data, the system performs sharding to avoid overload of a single request and ensure the smoothness of the replay process.

[0074] During the data sampling phase, data returned by the interface after iterative updates is acquired as comparison data, and data returned by the interface before iterative updates is acquired as reference data. These two sets of data are then uniformly converted into a tree structure model, forming the first and second structured data representations, respectively. The Longest Common Subsequence (LCS) algorithm and array element similarity comparison algorithm are used for comparison analysis, including field value verification, hierarchical relationship verification, data type verification, and existence verification, to obtain multi-dimensional comparison results. A detailed analysis report is generated based on the multi-dimensional comparison results, and the results are fed back to the automated interface platform for further integration and optimization. Furthermore, the business impact of each difference in the multi-dimensional comparison results can be assessed. Statistical analysis of the effectiveness of this detection tool is conducted, and the analysis process is optimized based on the statistical analysis results to achieve noise reduction in the comparison results.

[0075] The interface iteration risk warning method provided by this invention aims to significantly improve the efficiency of regression testing during the interface iteration process. This method automatically compares the output results of the same interface under different versions by implementing an interface diff operation. This allows testers to focus only on the differing parts, effectively avoiding the linear increase in manual regression testing workload as the number of interfaces increases.

[0076] Furthermore, this method can quickly and accurately identify differences before and after interface modifications. Since most code changes are reflected in changes to the interface's returned results, the interface diff operation can quickly locate these differences, and then verify the impact of the code modifications in reverse. For example, it can detect whether the interface is missing key display fields due to code modifications, or whether field values ​​are incorrect due to internal logic adjustments, ensuring the accuracy and completeness of the interface.

[0077] This method can also leverage a large amount of online request logs to replay the changes between the old and new versions. This process can cover a large amount of real-world abnormal request data from the production environment that is difficult to access through manual or automated interface regression testing, thereby more comprehensively detecting the stability of the interface and effectively improving the coverage of test scenarios.

[0078] Furthermore, when developers submit tests, this method can filter relevant automated test cases based on the interface diff results and integrate them with the continuous integration (CI) system. By reducing the number of test cases and execution time, it not only improves the stability of CI but also reduces the cost of CI maintenance and troubleshooting, achieving a dual improvement in testing efficiency and quality.

[0079] This invention converts the data to be compared and the reference data into a unified nested structured data model and performs multi-dimensional comparisons. This comprehensive detection capability ensures that every detail of interface changes is captured, avoiding the problem of missed changes caused by the single detection dimension in traditional methods. Converting the interface-returned data into a pre-formatted nested structured data model provides a standardized input format for comparison. This standardization not only improves the accuracy and efficiency of the comparison but also makes the comparison process more stable and scalable. Regardless of how the data format returned by the interface changes, as long as it is converted into a unified structured model, effective comparison can be performed. The mechanism of determining whether to trigger an alarm notification based on the business impact of the multi-dimensional comparison results makes alarms more intelligent and accurate, avoiding unnecessary alarm interference.

[0080] Figure 4 A schematic diagram of an embodiment of the interface iteration risk alarm device of the present invention is shown. Figure 4 As shown, the device 400 includes: an acquisition module 410, a conversion module 420, a comparison module 430, and a determination module 440.

[0081] The acquisition module is used to acquire the data returned by the interface after the iteration update as the comparison data, and to acquire the data returned by the interface before the iteration update that was stored in advance as the reference data.

[0082] The conversion module is used to convert the data to be compared and the reference data into a structured data model in a preset format organized with a nested structure, generating a first structured data representation and a second structured data representation;

[0083] The comparison module is used to perform multi-dimensional comparison based on the first structured data representation and the second structured data representation to obtain multi-dimensional comparison results;

[0084] The determination module is used to determine whether to trigger an alarm notification based on the business impact of the multi-dimensional comparison results.

[0085] In one alternative approach, the comparison module is specifically used for:

[0086] The first structured data representation and the second structured data representation are compared in multiple dimensions to identify the changed dimensions and the corresponding differences in each dimension, and to generate multi-dimensional comparison results. The changed dimensions include at least one of the following: changes in hierarchical relationships, changes in data types, differences in annotation completeness, changes in non-functional attributes, and missing or added fields.

[0087] In one alternative approach, the conversion module is specifically used for:

[0088] The data to be compared and the reference data are converted into a tree structure model to generate a first structured data representation and a second structured data representation.

[0089] In one alternative approach, the module is specifically used for:

[0090] If there is a difference in at least one dimension in the multi-dimensional comparison results, then identify the type of difference to which the difference belongs;

[0091] Based on the difference type to which the difference belongs, determine the degree of business impact of the difference. The difference type must include at least one of the following: field addition type, field deletion type, field value modification type, data type change type, nested structure change type, comment integrity difference type, and non-functional attribute change type.

[0092] Based on the degree of business impact of the differences, determine whether to trigger an alarm notification.

[0093] In one alternative approach, the comparison module is specifically used for:

[0094] The multi-dimensional comparison task targeting the first and second structured data representations is split into multiple subtasks and assigned to different computing nodes for execution.

[0095] In one alternative approach, the module is specifically used for:

[0096] Query the pre-configured difference type impact weight table, and determine the basic impact score of the difference based on the difference type to which the difference belongs. The weight table includes the basic impact score corresponding to each difference type.

[0097] Based on the business criticality level of the field where the difference exists, the basic impact score is weighted and adjusted to obtain a quantitative value of the degree of business impact of the difference.

[0098] In one alternative approach, non-functional attribute changes include at least one of the following: field length change, enumeration value range change, data format constraint change, default value change, or numerical precision change.

[0099] This invention converts the data to be compared and the reference data into a unified nested structured data model and performs multi-dimensional comparisons. This comprehensive detection capability ensures that every detail of interface changes is captured, avoiding the problem of missed changes caused by the single detection dimension in traditional methods. Converting the interface-returned data into a pre-formatted nested structured data model provides a standardized input format for comparison. This standardization not only improves the accuracy and efficiency of the comparison but also makes the comparison process more stable and scalable. Regardless of how the data format returned by the interface changes, as long as it is converted into a unified structured model, effective comparison can be performed. The mechanism of determining whether to trigger an alarm notification based on the business impact of the multi-dimensional comparison results makes alarms more intelligent and accurate, avoiding unnecessary alarm interference.

[0100] Figure 5 The diagram shows a structural schematic of an embodiment of the interface iteration risk alarm device of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the interface iteration risk alarm device.

[0101] like Figure 5 As shown, the interface iteration risk alarm device may include: processor 502, communication interface 504, memory 506, and communication bus 508.

[0102] The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508. Communication interface 504 is used to communicate with other network elements, such as clients or other servers. Processor 502 executes program 510, specifically performing the relevant steps described in the embodiment of the interface iteration risk alarm method.

[0103] Specifically, program 510 may include program code, which includes computer-executable instructions.

[0104] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The interface iteration risk alarm device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0105] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0106] Specifically, program 510 can be called by processor 502 to cause the interface iteration risk alarm device to perform the following operations:

[0107] Get the data returned by the interface after the iteration update as the comparison data, and get the data returned by the interface before the iteration update that was stored in advance as the reference data;

[0108] The data to be compared and the reference data are converted into a structured data model in a preset format with a nested structure, generating a first structured data representation and a second structured data representation;

[0109] Multi-dimensional comparison is performed based on the first structured data representation and the second structured data representation to obtain multi-dimensional comparison results;

[0110] Based on the business impact of the multi-dimensional comparison results, determine whether to trigger an alarm notification.

[0111] In an alternative manner, program 510 is invoked by processor 502 to cause the interface iteration risk alarm device to perform the following operations:

[0112] The first structured data representation and the second structured data representation are compared in multiple dimensions to identify the changed dimensions and the corresponding differences in each dimension, and to generate multi-dimensional comparison results. The changed dimensions include at least one of the following: changes in hierarchical relationships, changes in data types, differences in annotation completeness, changes in non-functional attributes, and missing or added fields.

[0113] In an alternative manner, program 510 is invoked by processor 502 to cause the interface iteration risk alarm device to perform the following operations:

[0114] The data to be compared and the reference data are converted into a tree structure model to generate a first structured data representation and a second structured data representation.

[0115] In an alternative manner, program 510 is invoked by processor 502 to cause the interface iteration risk alarm device to perform the following operations:

[0116] If there is a difference in at least one dimension in the multi-dimensional comparison results, then identify the type of difference to which the difference belongs;

[0117] Based on the difference type to which the difference belongs, determine the degree of business impact of the difference. The difference type must include at least one of the following: field addition type, field deletion type, field value modification type, data type change type, nested structure change type, comment integrity difference type, and non-functional attribute change type.

[0118] Based on the degree of business impact of the differences, determine whether to trigger an alarm notification.

[0119] In an alternative manner, program 510 is invoked by processor 502 to cause the interface iteration risk alarm device to perform the following operations:

[0120] The multi-dimensional comparison task targeting the first and second structured data representations is split into multiple subtasks and assigned to different computing nodes for execution.

[0121] In an alternative manner, program 510 is invoked by processor 502 to cause the interface iteration risk alarm device to perform the following operations:

[0122] Query the pre-configured difference type impact weight table, and determine the basic impact score of the difference based on the difference type to which the difference belongs. The weight table includes the basic impact score corresponding to each difference type.

[0123] Based on the business criticality level of the field where the difference exists, the basic impact score is weighted and adjusted to obtain a quantitative value of the degree of business impact of the difference.

[0124] In one alternative approach, non-functional attribute changes include at least one of the following: field length change, enumeration value range change, data format constraint change, default value change, or numerical precision change.

[0125] This invention converts the data to be compared and the reference data into a unified nested structured data model and performs multi-dimensional comparisons. This comprehensive detection capability ensures that every detail of interface changes is captured, avoiding the problem of missed changes caused by the single detection dimension in traditional methods. Converting the interface-returned data into a pre-formatted nested structured data model provides a standardized input format for comparison. This standardization not only improves the accuracy and efficiency of the comparison but also makes the comparison process more stable and scalable. Regardless of how the data format returned by the interface changes, as long as it is converted into a unified structured model, effective comparison can be performed. The mechanism of determining whether to trigger an alarm notification based on the business impact of the multi-dimensional comparison results makes alarms more intelligent and accurate, avoiding unnecessary alarm interference.

[0126] This invention provides a computer-readable storage medium storing at least one executable instruction. When the executable instruction is run on an interface iteration risk alarm device / app, it causes the interface iteration risk alarm device / app to execute the interface iteration risk alarm method in any of the above method embodiments.

[0127] Specifically, the executable instructions can be used to cause the interface iteration risk alarm device / app to perform the following operations:

[0128] Get the data returned by the interface after the iteration update as the comparison data, and get the data returned by the interface before the iteration update that was stored in advance as the reference data;

[0129] The data to be compared and the reference data are converted into a structured data model in a preset format with a nested structure, generating a first structured data representation and a second structured data representation;

[0130] Multi-dimensional comparison is performed based on the first structured data representation and the second structured data representation to obtain multi-dimensional comparison results;

[0131] Based on the business impact of the multi-dimensional comparison results, determine whether to trigger an alarm notification.

[0132] In one alternative approach, the executable instructions cause the interface iteration risk alarm device / apparatus to perform the following operations:

[0133] The first structured data representation and the second structured data representation are compared in multiple dimensions to identify the changed dimensions and the corresponding differences in each dimension, and to generate multi-dimensional comparison results. The changed dimensions include at least one of the following: changes in hierarchical relationships, changes in data types, differences in annotation completeness, changes in non-functional attributes, and missing or added fields.

[0134] In one alternative approach, the executable instructions cause the interface iteration risk alarm device / apparatus to perform the following operations:

[0135] The data to be compared and the reference data are converted into a tree structure model to generate a first structured data representation and a second structured data representation.

[0136] In one alternative approach, the executable instructions cause the interface iteration risk alarm device / apparatus to perform the following operations:

[0137] If there is a difference in at least one dimension in the multi-dimensional comparison results, then identify the type of difference to which the difference belongs;

[0138] Based on the difference type to which the difference belongs, determine the degree of business impact of the difference. The difference type must include at least one of the following: field addition type, field deletion type, field value modification type, data type change type, nested structure change type, comment integrity difference type, and non-functional attribute change type.

[0139] Based on the degree of business impact of the differences, determine whether to trigger an alarm notification.

[0140] In one alternative approach, the executable instructions cause the interface iteration risk alarm device / apparatus to perform the following operations:

[0141] The multi-dimensional comparison task targeting the first and second structured data representations is split into multiple subtasks and assigned to different computing nodes for execution.

[0142] In one alternative approach, the executable instructions cause the interface iteration risk alarm device / apparatus to perform the following operations:

[0143] Query the pre-configured difference type impact weight table, and determine the basic impact score of the difference based on the difference type to which the difference belongs. The weight table includes the basic impact score corresponding to each difference type.

[0144] Based on the business criticality level of the field where the difference exists, the basic impact score is weighted and adjusted to obtain a quantitative value of the degree of business impact of the difference.

[0145] In one alternative approach, non-functional attribute changes include at least one of the following: field length change, enumeration value range change, data format constraint change, default value change, or numerical precision change.

[0146] This invention converts the data to be compared and the reference data into a unified nested structured data model and performs multi-dimensional comparisons. This comprehensive detection capability ensures that every detail of interface changes is captured, avoiding the problem of missed changes caused by the single detection dimension in traditional methods. Converting the interface-returned data into a pre-formatted nested structured data model provides a standardized input format for comparison. This standardization not only improves the accuracy and efficiency of the comparison but also makes the comparison process more stable and scalable. Regardless of how the data format returned by the interface changes, as long as it is converted into a unified structured model, effective comparison can be performed. The mechanism of determining whether to trigger an alarm notification based on the business impact of the multi-dimensional comparison results makes alarms more intelligent and accurate, avoiding unnecessary alarm interference.

[0147] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments of this invention are not directed to any particular programming language.

[0148] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. Similarly, for the sake of brevity and to aid in understanding one or more aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0149] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0150] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for alerting risks associated with interface iteration, characterized in that, The method includes: Get the data returned by the interface after the iteration update as the comparison data, and get the data returned by the interface before the iteration update that was stored in advance as the reference data; The data to be compared and the reference data are converted into a structured data model in a preset format using a nested structure, generating a first structured data representation and a second structured data representation; A multi-dimensional comparison is performed based on the first structured data representation and the second structured data representation to obtain the multi-dimensional comparison result; Based on the business impact of the multi-dimensional comparison results, determine whether to trigger an alarm notification.

2. The method according to claim 1, characterized in that, The step of performing a multi-dimensional comparison based on the first structured data representation and the second structured data representation to obtain a multi-dimensional comparison result includes: The first structured data representation and the second structured data representation are compared in multiple dimensions to identify the changed dimensions and the differential information corresponding to each dimension, and the multi-dimensional comparison result is generated. The changed dimensions include at least one of the following: changes in hierarchical relationship, changes in data type, differences in annotation completeness, changes in non-functional attributes, and missing or added fields.

3. The method according to claim 1, characterized in that, The step of converting the data to be compared and the reference data into a structured data model in a preset format using a nested structure, and generating a first structured data representation and a second structured data representation, includes: The data to be compared and the reference data are converted into a tree structure model to generate a first structured data representation and a second structured data representation.

4. The method according to claim 1, characterized in that, The determination of whether to trigger an alarm notification based on the business impact degree of the multi-dimensional comparison results includes: If at least one dimension in the multi-dimensional comparison results shows a difference, then the type of difference to which the difference belongs is identified. Based on the difference type to which the difference belongs, the degree of business impact of the difference is determined. The difference type includes at least one of the following: field addition type, field deletion type, field value modification type, data type change type, nested structure change type, comment integrity difference type, and non-functional attribute change type. Based on the degree of business impact of the aforementioned differences, determine whether to trigger an alarm notification.

5. The method according to any one of claims 1-4, characterized in that, The step of performing a multi-dimensional comparison based on the first structured data representation and the second structured data representation to obtain a multi-dimensional comparison result includes: The multi-dimensional comparison task between the first structured data representation and the second structured data representation is split into multiple sub-tasks and assigned to different computing nodes for execution.

6. The method according to claim 4, characterized in that, Determining the business impact of the difference based on its type includes: Query the pre-configured difference type impact weight table, and determine the basic impact score of the difference based on the difference type to which the difference belongs. The weight table includes the basic impact score corresponding to each difference type. Based on the business criticality level of the field where the difference is located, the basic impact score is weighted and adjusted to obtain a quantitative value of the degree of business impact of the difference.

7. The method according to claim 2, characterized in that, The non-functional attribute changes include at least one of the following: field length change, enumeration value range change, data format constraint change, default value change, or numerical precision change.

8. An interface iteration risk alarm device, characterized in that, The device includes: The acquisition module is used to acquire the data returned by the interface after the iteration update as the comparison data, and to acquire the data returned by the interface before the iteration update that was stored in advance as the reference data. The conversion module is used to convert the data to be compared and the reference data into a structured data model in a preset format organized with a nested structure, and generate a first structured data representation and a second structured data representation. The comparison module is used to perform multi-dimensional comparison based on the first structured data representation and the second structured data representation to obtain multi-dimensional comparison results; The determination module is used to determine whether to trigger an alarm notification based on the degree of business impact of the multi-dimensional comparison results.

9. An interface iteration risk alarm device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the interface iteration risk alarm method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on the interface iteration risk alarm device / app, causes the interface iteration risk alarm device / app to perform the operation of the interface iteration risk alarm method as described in any one of claims 1-7.

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