Intelligent agent development platform upgrading compatibility evaluation method, equipment, medium and product

Abstract

The invention discloses an agent development platform upgrading compatibility evaluation method and device, a medium and a product, and the method comprises the steps: analyzing a technical document of a target agent development platform to obtain API change information, and storing the change information in an information change library in a structured manner; original process codes of the target agent are obtained, all API calling records are extracted, matching mapping is carried out on the API calling records and the change information in the information change library, and therefore affected API calling and corresponding change types of the API calling are recognized; according to a matching mapping result, calculating an influence score value for each influenced API call, summarizing the influence score values of all the influenced API calls, and performing normalization processing to obtain an overall compatibility influence score value; according to the matching mapping result and the overall compatibility influence score, a compatibility evaluation report is generated, and the technical scheme of the embodiment of the invention can realize automatic, efficient and accurate evaluation of the upgrading compatibility of the intelligent agent development platform.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a method, device, medium, and product for evaluating the compatibility of an intelligent agent development platform upgrade. Background Technology

[0002] As enterprises deepen their digital transformation, intelligent agents, as the core carriers for automating business processes, are experiencing increasing complexity and application scale. The stable operation of intelligent agent processes highly depends on the stability of the APIs (Application Programming Interfaces) provided by the underlying development platform. However, when the intelligent agent development platform undergoes version iterations or the underlying system is upgraded, the accompanying API changes can easily trigger compatibility issues with existing intelligent agent processes, leading to process execution failures or abnormal behavior. This poses a serious challenge to the continuity and stability of enterprise business.

[0003] In existing technologies, solutions to the aforementioned compatibility issues mainly rely on developers manually comparing API change documents, static code scanning based on predefined rules, and writing a large number of unit test cases. However, manual comparison is inefficient and prone to omissions; static code analysis struggles to cover all dynamic behavior change scenarios; and comprehensive test case coverage requires extremely high time and manpower costs. None of these methods can meet the needs of rapid iteration and maintenance of intelligent agent applications. Summary of the Invention

[0004] This invention provides a method, device, medium, and product for evaluating the upgrade compatibility of an intelligent agent development platform, which can realize automated analysis and risk quantification of the upgrade compatibility of the intelligent agent development platform.

[0005] According to one aspect of the present invention, a method for evaluating the upgrade compatibility of an intelligent agent development platform is provided, the method comprising:

[0006] The technical documentation of the target intelligent agent development platform is parsed to obtain API change information, and the API change information is structured and stored in the information change database.

[0007] Obtain the original process code of the target intelligent agent, extract all API call records in the process code, and match and map the extracted API call records with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result;

[0008] Based on the matching mapping results, an impact score is calculated for each affected API call, and the impact scores of all affected API calls are aggregated and normalized to obtain the overall compatibility impact score.

[0009] A compatibility assessment report is generated based on the matching mapping results and the overall compatibility impact score.

[0010] According to another aspect of the present invention, an intelligent agent development platform upgrade compatibility assessment device is provided, the device comprising:

[0011] The information change database construction module is used to parse the technical documents of the target intelligent agent development platform, obtain API change information, and store the API change information in a structured manner in the information change database;

[0012] The matching and mapping module is used to obtain the original process code of the target intelligent agent, extract all API call records in the process code, and match and map the extracted API call records with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result.

[0013] The impact score calculation module is used to calculate the impact score value for each affected API call based on the matching mapping results, and to summarize the impact score values ​​of all affected API calls. After normalization, the overall compatibility impact score value is obtained.

[0014] The report generation module is used to generate a compatibility assessment report based on the matching mapping results and the overall compatibility impact score.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform an intelligent agent development platform upgrade compatibility assessment method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the intelligent agent development platform upgrade compatibility evaluation method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer program product is also provided, including computer instructions that, when executed by a processor, implement the steps of the method as described in any embodiment of the present invention.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description.

[0022] The technical solution of this invention involves parsing the technical documentation of the target intelligent agent development platform to obtain API change information, and then storing this API change information in a structured manner in an information change database. Next, the original process code of the target intelligent agent is obtained, and all API call records are extracted. These extracted API call records are matched and mapped with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching mapping result. An impact score is calculated for each affected API call based on the matching mapping result, and the impact scores of all affected API calls are summarized. After normalization, an overall compatibility impact score is obtained. Finally, a compatibility assessment report is generated based on the matching mapping result and the overall compatibility impact score. This solution solves the problems of low efficiency, poor accuracy, and inability to quantify risks when manually assessing platform upgrade compatibility. It achieves automated, precise, and quantitative assessment, significantly improving assessment efficiency and reliability, and providing clear guidance for the smooth upgrade of intelligent agent processes. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of an intelligent agent development platform upgrade compatibility evaluation method provided in Embodiment 1 of the present invention;

[0025] Figure 2 This is a flowchart of another intelligent agent development platform upgrade compatibility evaluation method provided in Embodiment 2 of the present invention;

[0026] Figure 3 This is a flowchart of another intelligent agent development platform upgrade compatibility evaluation method provided in Embodiment 3 of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of an intelligent agent development platform upgrade compatibility evaluation device provided in Embodiment 4 of the present invention;

[0028] Figure 5This is a schematic diagram of the structure of an electronic device that implements an intelligent agent development platform upgrade compatibility evaluation method according to an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Example 1

[0032] Figure 1 This is a flowchart of a method for evaluating the compatibility of an intelligent agent development platform upgrade, provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation where the compatibility impact on existing processes is automatically evaluated when the version of the intelligent agent development platform is upgraded. This method can be executed by an intelligent agent development platform upgrade compatibility evaluation device, which can be implemented in hardware and / or software and is generally configured in an electronic device.

[0033] Correspondingly, such as Figure 1 As shown, the method includes:

[0034] S110. Parse the technical documents of the target intelligent agent development platform to obtain API change information, and store the API change information in a structured manner in the information change database.

[0035] In this context, an intelligent agent can be understood as a software entity capable of automating specific business processes; it can be viewed as a "digital employee" designed to replace or assist human intervention, automatically completing a series of predefined tasks. An intelligent agent development platform can be understood as an integrated development environment (IDE) for creating, testing, deploying, and managing these intelligent agents. This platform provides developers with a complete toolset, including a graphical programming interface, code editor, debugger, and most importantly—a series of pre-packaged APIs that allow intelligent agents to interact with external systems (such as databases, network services, etc.).

[0036] In this embodiment, the technical documents released during platform upgrades are analyzed using a pre-trained large language model or a specialized document parsing tool to identify various API change details. These change details include various types such as API additions, deletions, signature modifications, and behavior adjustments. After parsing, the extracted change information is stored in a preset structured format to form a change information database that can be queried later, providing an accurate data foundation for the entire evaluation process.

[0037] S120. Obtain the original process code of the target intelligent agent, extract all API call records in the process code, and match and map the extracted API call records with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result.

[0038] The original process code of the intelligent agent can be understood as the source code of the intelligent agent that was written and built by the developers using the old version of the intelligent agent development platform before the platform upgrade, and which is currently running stably online.

[0039] In this embodiment, the existing process code that needs to be evaluated is then scanned and analyzed. Code parsing technology is used to identify all API records called in the code. Each identified call record is compared one by one with the change information recorded in the change information database. Through precise matching of fully qualified names and method signatures, it is determined which calls will be affected by the platform upgrade, and the specific change type corresponding to each affected API call is accurately marked.

[0040] S130. Based on the matching mapping results, calculate the impact score for each affected API call, and summarize the impact scores of all affected API calls. After normalization, obtain the overall compatibility impact score.

[0041] In this embodiment, based on the identified affected API calls and their change types, a specific impact score is calculated for each affected API call. This calculation process comprehensively considers the severity weight of the change type, the frequency of the call in the code, and the importance coefficient of the call in the business process, obtaining a weighted impact score for each affected API call. Subsequently, the impact scores of all affected API calls are accumulated and normalized by their proportional relationship with the total number of affected API calls in the code, ultimately transforming them into an overall compatibility impact score out of 100.

[0042] S140. Generate a compatibility assessment report based on the matching mapping results and the overall compatibility impact score.

[0043] In this embodiment, the final matching mapping results are integrated with the quantitative scoring results to generate a detailed compatibility assessment report. This report clearly lists all affected call locations, specific change types, and potential risk levels. It provides specific code modification suggestions and operational guidance for each issue, along with an overall scoring conclusion, offering developers a complete upgrade compatibility analysis.

[0044] The technical solution of this invention involves parsing the technical documentation of the target intelligent agent development platform to obtain API change information, and then storing this API change information in a structured manner in an information change database. Next, the original process code of the target intelligent agent is obtained, and all API call records are extracted. These extracted API call records are matched and mapped with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching mapping result. An impact score is calculated for each affected API call based on the matching mapping result, and the impact scores of all affected API calls are summarized. After normalization, an overall compatibility impact score is obtained. Finally, a compatibility assessment report is generated based on the matching mapping result and the overall compatibility impact score. This solution solves the problems of low efficiency, poor accuracy, and inability to quantify risks when manually assessing platform upgrade compatibility. It achieves automated, precise, and quantitative assessment, significantly improving assessment efficiency and reliability, and providing clear guidance for the smooth upgrade of intelligent agent processes.

[0045] Example 2

[0046] Figure 2 This is a flowchart of a method for evaluating the compatibility of an intelligent agent development platform upgrade, provided in Embodiment 2 of the present invention. This embodiment is based on and optimized from the above embodiments. Specifically, the operation of "parseting the technical documents of the target intelligent agent development platform to obtain API change information and storing the API change information in a structured manner in an information change database" has been refined.

[0047] Correspondingly, such as Figure 2 As shown, the method includes:

[0048] S210. Extract API change information from the technical document using a pre-trained large language model or document parsing tool.

[0049] The pre-trained large language model can be understood as an artificial intelligence model pre-trained based on massive amounts of text data, possessing powerful natural language understanding and generation capabilities. In this embodiment, it is applied to the initial stage of the intelligent agent development platform upgrade compatibility assessment process, specifically for automatically parsing the content of technical documents. API change information can be understood as the details of API changes described in the technical documents during the intelligent agent development platform upgrade. In this embodiment, this type of information includes specific details such as the change type (e.g., addition, deletion, signature modification), the old and new method signatures, and the impact risk level.

[0050] In this embodiment, a pre-trained large language model or a specialized document parsing tool is used to automatically parse and extract content from the technical documents published by the target intelligent agent development platform. This process aims to identify various relevant change details from the document text, such as the addition or deletion of APIs, signature adjustments, or behavior modifications, ensuring that all change information is extracted comprehensively and accurately, laying a data foundation for subsequent processing.

[0051] S220. Transform the extracted API change information into structured data containing change type, new signature, old signature, and estimated impact risk level.

[0052] In this embodiment, the extracted change information is transformed into a standardized structured data format. Specifically, the original information is organized into records containing key fields such as change type, signatures of the old and new methods, and estimated impact risk level. This structured processing makes the change information standardized and machine-processable, facilitating subsequent storage, querying, and comparison operations.

[0053] S230. Store the structured data in the information change database, wherein the data in the information change database supports manual input of supplementary information.

[0054] In this embodiment, the structured data is persistently stored in an information change database. This database not only supports automatically stored data, but also allows for manual correction or supplementation of information, such as adding missing change descriptions or adjusting risk levels, thereby ensuring the integrity and reliability of change information and providing stable data support for the entire assessment process.

[0055] Furthermore, this embodiment also designs a change library record format, which comprehensively records the API change details through carefully defined structured fields. Specific fields include: changeId as a unique identifier (e.g., "CHG-001"); apiName recording the fully qualified name of the affected API (e.g., "com.example.api.doSomething"); and the changeType field, which defines nine change types: "Addition," "Removal," "Signature Modification," "Behavior Change," "Deprecation," "Interface Change," "Security Enhancement," "Performance Improvement," and "Exception Handling Change," each with a clear scenario description. In addition, the format includes oldSignature and newSignature to record changes in method signatures, description field to store detailed descriptions of the changes, docReference to annotate the corresponding chapters of the technical documentation, suggestedAction to provide specific code modification suggestions, impactLevel to assess the risk level (e.g., "High"), and additionalInfo field to store extended information for specific change types (e.g., descriptions of parameter differences when the signature is modified or details of protocol updates when the API is changed). This comprehensive field design ensures that change information can be accurately traced and efficiently utilized.

[0056] S240. Obtain the original process code of the target intelligent agent, extract all API call records in the process code, and match and map the extracted API call records with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result.

[0057] Optionally, based on the above embodiments, the original process code of the target intelligent agent is obtained, all API call records in the process code are extracted, and the extracted API call records are matched and mapped with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result, which may include:

[0058] By using a pre-trained large language model, the fully qualified names and method signatures of all API calls in the original process code of the target intelligent agent are identified.

[0059] According to the preset mapping rules, each API call is matched with the API change information recorded in the information change database, and a mapping report of the affected API calls is generated as the matching mapping result.

[0060] The fully qualified name can be understood as a complete path identifier used to uniquely and unambiguously locate an API or method in the code. It is usually composed of nested levels of package name, class name, and method name to ensure accurate location. The method signature can be understood as the core information defining a method's calling method and API form, constituting the calling contract between methods. Typically, a complete method signature includes the method name, parameter list (number, type, and order of parameters), and return type.

[0061] The preset mapping rules can be understood as a set of predefined logical rules that automatically generate code modification suggestions for different change types. For example, the rule base will explicitly define: if the change type is "deletion", then the suggested remedial action is "replace the call with the new method specified in the change record"; if the change type is "signature modification", then the suggested remedial action is "adjust the parameter list when calling according to the new signature". These rules are pre-set based on the summary of common API change patterns.

[0062] Generally, during the compatibility assessment of an intelligent agent development platform upgrade, the first step is to use a pre-trained large language model to deeply analyze the original process code of the target intelligent agent. This step aims to automatically identify the complete identification information of all APIs called in the code, including the globally unique name of the API and specific method feature descriptions. Through the model's understanding of the code structure, the key attributes of each call can be accurately extracted, providing basic data for subsequent analysis. This analysis process relies on the model's grasp of programming language semantics, ensuring that even with complex code structures, all relevant call points can be fully covered.

[0063] Generally, after obtaining API call information, each call record is compared one by one with the change details stored in the information change database according to pre-defined matching rules. The matching process mainly relies on the API's unique name and method characteristics to check for corresponding change records, such as whether the API has been modified, deleted, or added. Through comparison, calls affected by platform upgrades are identified, and the specific change type is determined. Finally, all matching results are integrated into a structured report, detailing the location of the affected API calls, the change category, and preliminary analysis conclusions.

[0064] Furthermore, this embodiment also designs a mapping rule, which defines corresponding code adaptation strategies for different change types. Specifically, the mapping logic includes the following nine core scenarios: For addition-type changes, the rule clearly states that if the old code does not call the new API, no modification is needed, but if the new function is required after the upgrade, the call should be added; For removal-type changes, the rule requires that when a call to a removed API is detected, it must be replaced with the alternative method provided in the new version or the call must be removed; For signature modification-type changes, the rule stipulates that the number and type of parameters must be checked, and parameters should be added or adjusted according to the new signature requirements; For behavior change-type changes, the rule suggests reviewing the calling logic and adding supplementary judgments or auxiliary method calls if necessary; For deprecation-type changes, the rule will generate warnings and prompt migration to the recommended new API; For interface change-type changes, the rule requires remapping the data format or calling method, involving data parsing and conversion operations; For security enhancement-type changes, the rule enforces checks on the integrity of security parameters, and recommends adding authentication tokens when missing; For performance improvement-type changes... For changes classified as "Improvements," the rules recommend using the optimized new API while also indicating the need for adaptation in error handling logic. For changes classified as "Exception Handling Changes," the rules verify the scope of exception handling coverage and provide modification suggestions for any uncovered new exception types. This rule system establishes a precise mapping between change types and fixes, providing a complete decision-making basis for automated code adaptation.

[0065] Optionally, based on the above embodiments, according to preset mapping rules, each API call is matched with the API change information recorded in the information change database, and a mapping report of affected API calls is generated, which may include:

[0066] The information change database is retrieved using the fully qualified name of each API call as the primary key. If the corresponding API change information is successfully located, the current API call is determined to be an affected API call.

[0067] The method signature of each affected API call is compared with the old version signature and the new version signature recorded in the corresponding API change information to determine the change type corresponding to each affected API call.

[0068] Based on the change type corresponding to each of the affected API calls, generate code adaptation suggestions for each affected API call;

[0069] Summarize the change types and code adaptation suggestions for all affected API calls, and generate a structured call mapping report.

[0070] Generally, when conducting compatibility assessments of agent process code, the first step is to use the full pathname of each API call identified in the code as a key search criterion to query a pre-built change information database. The purpose of this step is to quickly filter out API calls that may change after a platform upgrade. If a corresponding change record can be successfully found based on the full pathname, it is preliminarily determined that this call in the current code belongs to a call item that will be affected by the upgrade, thus defining the scope for subsequent in-depth analysis.

[0071] Generally, after initially identifying the affected calls, further analysis of the specific nature of the impact is needed. At this stage, the specific characteristics of the API call in the code are meticulously compared with the old and new version characteristics described in the change log. By analyzing the differences at the characteristic level, the specific category of the change can be accurately determined—for example, whether the API calling method needs adjustment or the internal logic has changed—thus clarifying the specific type of compatibility issue faced by the call.

[0072] Generally, after identifying the specific change category corresponding to each affected API call, targeted code modification suggestions are automatically generated based on pre-defined conversion rules. For example, for changes requiring adjustments to API call methods, specific instructions on how parameters should be adjusted are provided; for deprecated API calls, specific suggestions for alternative API calls are given. This step transforms abstract change types into operational guidelines that developers can directly understand and implement.

[0073] Generally, all analysis results are systematically summarized at the end. Each affected call, its corresponding change category, and customized modification suggestions are integrated to generate an API call relationship analysis report with a unified format. This report clearly shows the distribution of all compatibility issues in the code and their corresponding solutions, providing a direct basis for subsequent remediation work.

[0074] S250. Based on the matching mapping results, calculate the impact score for each affected API call, and summarize the impact scores of all affected API calls. After normalization, obtain the overall compatibility impact score.

[0075] S260. Generate a compatibility assessment report based on the matching mapping results and the overall compatibility impact score.

[0076] The technical solution of this invention extracts API change information from technical documents using a pre-trained large language model or document parsing tool. The extracted change information is then transformed into structured data containing change type, new signature, old signature, and estimated impact risk level, and stored in an information change database. This allows the acquisition of the original process code of the target intelligent agent and the extraction of all API call records. The fully qualified name of each API call is used as the primary key to retrieve the information change database and locate the relevant change record. The method signature of each API call is compared with the old and new version signatures in the corresponding change record to determine the specific change type. Based on the determined change type, specific code adaptation suggestions are generated, and a call mapping report is compiled. Based on this, an impact score is calculated for each affected API call according to the matching mapping results. The impact scores of all affected calls are summarized and normalized to obtain an overall compatibility impact score. Finally, a compatibility assessment report is generated based on the matching mapping results and the overall compatibility impact score. This solves the problems of low efficiency and insufficient accuracy in manually assessing platform upgrade compatibility, achieving the beneficial effect of automated and accurate risk assessment and quantification.

[0077] Example 3

[0078] Figure 3 This is a flowchart of a method for evaluating the compatibility of an intelligent agent development platform upgrade, provided in Embodiment 2 of the present invention. This embodiment is based on and optimized from the above embodiments. Specifically, the operation of "summarizing the impact scores of all affected API calls and obtaining the overall compatibility impact score after normalization" has been refined.

[0079] Correspondingly, such as Figure 3 As shown, the method includes:

[0080] S310. Parse the technical documents of the target intelligent agent development platform to obtain API change information, and store the API change information in a structured manner in the information change database.

[0081] S320. Obtain the original process code of the target intelligent agent, extract all API call records in the process code, and match and map the extracted API call records with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result.

[0082] S330. Calculate the impact score for each affected API call based on the matching mapping results.

[0083] Optionally, based on the above embodiments, calculating an impact score for each affected API call may include:

[0084] Based on the change type of each affected API call, the corresponding weight value is obtained from a pre-defined weight table, where the weight value can be adjusted according to the actual situation;

[0085] The frequency of each affected API call in the original process code of the target agent is counted. The more times the affected API is called, the greater the total impact of the affected API.

[0086] Determine the business criticality coefficient for each affected API call based on the importance of each affected API in the business process;

[0087] The impact score for each affected API call is calculated using a predefined scoring formula, which is:

[0088]

[0089] in, Score the impact of the i-th affected API call. Let i be the weight value of the i-th affected API call. Let i be the call frequency of the i-th affected API call. Let be the business criticality coefficient for the i-th affected API call.

[0090] Generally, during the compatibility assessment of an intelligent agent development platform upgrade, when it is necessary to quantify the impact of each affected API call, the corresponding weight value is first obtained from a pre-configured weight table based on the type of change. This weight table is set based on the potential risks of different types of changes. For example, deletion operations are usually given a higher weight value than addition operations to reflect their greater disruptiveness to existing code. Furthermore, these weight values ​​can be flexibly adjusted according to the actual needs of specific projects to ensure that the assessment results are consistent with real-world scenarios.

[0091] Generally, the next step is to count the number of times each affected API call appears in the original process code, i.e., the call frequency. A higher call frequency indicates that the API is used more extensively in the code, and the potential impact of its changes is greater; therefore, it requires more attention during the evaluation. This statistical process is completed automatically by analyzing the code structure, ensuring data accuracy and comprehensiveness.

[0092] Generally, a business criticality coefficient is then assigned to each affected API call based on its role and importance within the business process. For example, APIs used in core business processes are assigned a higher coefficient, while auxiliary or non-critical APIs have relatively lower coefficients. This coefficient is typically determined by combining business architecture documents or the development team's experience to reflect the actual potential impact of the change on business continuity.

[0093] Generally, a predefined mathematical formula is used to calculate the individual impact score for each affected API call. This calculation multiplies the obtained weight values, call frequency, and business criticality coefficient to obtain a quantitative score. This method ensures that the score comprehensively reflects the technical severity of the change, the frequency of use at the code level, and the importance at the business level, providing accurate data input for subsequent overall evaluation.

[0094] S340. Sum the impact scores of all affected API calls to obtain the total impact score of all affected API calls.

[0095] In this embodiment, after the individual impact scores of all affected API calls have been calculated, these scores are summed to obtain a total impact score. This summation process aims to aggregate the impact of all individual compatibility issues, forming a quantitative representation of the overall impact of the upgrade, so that subsequent normalization processing can be based on a comprehensive benchmark value.

[0096] S350. Normalize the total impact score by dividing the total impact score by the total number of all affected API calls to obtain the overall compatibility impact score.

[0097] In this embodiment, to ensure the scoring results are standardized and comparable, the accumulated total impact score is normalized by dividing the total number of all affected API calls. This step converts the absolute score into a relative percentage, typically expressed as a percentage, thus more intuitively reflecting the overall compatibility risk level of the platform upgrade on the agent process codebase, facilitating decision-makers' quick understanding of the severity of the impact.

[0098] S360. Generate a compatibility assessment report based on the matching mapping results and the overall compatibility impact score.

[0099] Optionally, based on the above embodiments, a compatibility assessment report can be generated according to the matching mapping results and the overall compatibility impact score, which may include:

[0100] Based on all affected API calls included in the matching mapping results, a structured issue summary list is generated, wherein the issue summary list includes the location, change type, risk level, and code adaptation suggestions for each affected API call;

[0101] Based on the predefined range of the overall compatibility impact score, an overall risk assessment conclusion is generated, and the remediation priority is marked for issues of different risk levels in the structured issue summary list;

[0102] Based on the summarized list of issues and the overall risk assessment conclusions, a compatibility assessment report is generated.

[0103] Generally, after completing the matching and mapping of API calls, a structured list is generated based on all affected call information. This list records in detail the code location, specific change type, estimated risk level, and corresponding code adaptation suggestions for each API call, thereby organizing scattered compatibility issues into clear and traceable entries.

[0104] Generally, the overall risk assessment conclusion will then be generated based on the calculated overall compatibility impact score and a predefined risk range. The problem items in the list will be marked with a priority for repair according to the risk level. For example, high-scoring problems will be marked as requiring immediate handling to ensure that resources are concentrated on key risk points.

[0105] Generally, the final problem summary list is integrated with the overall risk assessment conclusions to generate a compatibility assessment report. The report includes a detailed description of each problem and remediation guidance, as well as an overall risk overview, forming a complete assessment output.

[0106] The technical solution of this invention involves parsing the technical documentation of the target intelligent agent development platform to obtain API change information and storing this change information in a structured information change database. Then, it retrieves all API call records from the original process code of the target intelligent agent and matches these records with the change information in the information change database to determine the change type corresponding to the affected call. Based on this, it calculates an impact score for each affected call according to the matching and mapping results. Specifically, it obtains the weight value of the corresponding change type from a preset weight table, calculates the call frequency of each affected call in the code, and combines this with its business criticality coefficient to calculate the individual impact value according to a predefined scoring formula. The impact scores of all affected calls are then summed to obtain a total impact score. This summation process effectively aggregates the dispersed individual impacts, achieving a comprehensive understanding of the overall impact. Next, the total impact score is divided by the total number of affected calls for normalization, resulting in a standardized overall compatibility impact score. This normalization process makes the scoring results comparable and intuitive, facilitating comparisons of evaluation results across codebases of different sizes. Finally, a compatibility evaluation report is generated based on the matching mapping results and the overall compatibility impact score. This approach solves the problems of low efficiency and inability to quantify the impact of manual evaluation, achieving the beneficial effects of automated and accurate evaluation while significantly improving evaluation efficiency and reliability.

[0107] Example 4

[0108] Figure 4 This is a schematic diagram of the structure of an intelligent agent development platform upgrade compatibility evaluation device provided in Embodiment 4 of the present invention. Figure 4 As shown, the device includes: an information change database construction module 410, a matching mapping module 420, an impact score calculation module 430, and a report generation module 440, wherein:

[0109] The information change database construction module 410 is used to parse the technical documents of the target intelligent agent development platform, obtain API change information, and store the API change information in a structured manner in the information change database;

[0110] The matching and mapping module 420 is used to obtain the original process code of the target intelligent agent, extract all API call records in the process code, and match and map the extracted API call records with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result.

[0111] The impact score calculation module 430 is used to calculate the impact score value for each affected API call based on the matching mapping results, and to summarize the impact score values ​​of all affected API calls. After normalization, the overall compatibility impact score value is obtained.

[0112] The report generation module 440 is used to generate a compatibility assessment report based on the matching mapping results and the overall compatibility impact score.

[0113] The technical solution of this invention involves parsing the technical documentation of the target intelligent agent development platform to obtain API change information, and then storing this API change information in a structured manner in an information change database. Next, the original process code of the target intelligent agent is obtained, and all API call records are extracted. These extracted API call records are matched and mapped with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching mapping result. An impact score is calculated for each affected API call based on the matching mapping result, and the impact scores of all affected API calls are summarized. After normalization, an overall compatibility impact score is obtained. Finally, a compatibility assessment report is generated based on the matching mapping result and the overall compatibility impact score. This solution solves the problems of low efficiency, poor accuracy, and inability to quantify risks when manually assessing platform upgrade compatibility. It achieves automated, precise, and quantitative assessment, significantly improving assessment efficiency and reliability, and providing clear guidance for the smooth upgrade of intelligent agent processes.

[0114] Based on the above embodiments, the information change database construction module 410 can be specifically used for:

[0115] API change information in the technical documents is extracted using a pre-trained large language model or document parsing tool.

[0116] The extracted API change information is transformed into structured data containing change type, new signature, old signature, and estimated impact risk level;

[0117] The structured data is stored in an information change database, where the data supports manual input of supplementary information.

[0118] Furthermore, based on the above embodiments, the matching mapping module 420 may further include:

[0119] The identification submodule is used to identify the fully qualified names and method signatures of all API calls in the original process code of the target intelligent agent using a pre-trained large language model.

[0120] The matching result generation submodule is used to match each API call with the API change information recorded in the information change database according to the preset mapping rules, and generate a mapping report of the affected API calls as the matching mapping result.

[0121] Based on the above embodiments, the matching result generation submodule can be specifically used for:

[0122] The information change database is retrieved using the fully qualified name of each API call as the primary key. If the corresponding API change information is successfully located, the current API call is determined to be an affected API call.

[0123] The method signature of each affected API call is compared with the old version signature and the new version signature recorded in the corresponding API change information to determine the change type corresponding to each affected API call.

[0124] Based on the change type corresponding to each of the affected API calls, generate code adaptation suggestions for each affected API call;

[0125] Summarize the change types and code adaptation suggestions for all affected API calls, and generate a structured call mapping report.

[0126] Based on the above embodiments, the influence scoring calculation module 430 can be specifically used for:

[0127] Based on the change type of each affected API call, the corresponding weight value is obtained from a pre-defined weight table, where the weight value can be adjusted according to the actual situation;

[0128] The frequency of each affected API call in the original process code of the target agent is counted. The more times the affected API is called, the greater the total impact of the affected API.

[0129] Determine the business criticality coefficient for each affected API call based on the importance of each affected API in the business process;

[0130] The impact score for each affected API call is calculated using a predefined scoring formula, which is:

[0131]

[0132] in, Score the impact of the i-th affected API call. Let i be the weight value of the i-th affected API call. Let i be the call frequency of the i-th affected API call. Let be the business criticality coefficient for the i-th affected API call.

[0133] Based on the above embodiments, the influence scoring calculation module 430 can be specifically used for:

[0134] The total impact score of all affected API calls is obtained by summing up the impact scores of all affected API calls.

[0135] The overall compatibility impact score is obtained by normalizing the total impact score by dividing the total impact score by the total number of all affected API calls.

[0136] Based on the above embodiments, the report generation module 440 can be specifically used for:

[0137] Based on all affected API calls included in the matching mapping results, a structured issue summary list is generated, wherein the issue summary list includes the location, change type, risk level, and code adaptation suggestions for each affected API call;

[0138] Based on the predefined range of the overall compatibility impact score, an overall risk assessment conclusion is generated, and the remediation priority is marked for issues of different risk levels in the structured issue summary list;

[0139] Based on the summarized list of issues and the overall risk assessment conclusions, a compatibility assessment report is generated.

[0140] The intelligent agent development platform upgrade compatibility assessment device provided in this embodiment of the invention can execute the intelligent agent development platform upgrade compatibility assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0141] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0142] Example 5

[0143] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0144] like Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0145] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0146] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as performing a smart agent development platform upgrade compatibility assessment method as described in any embodiment of the present invention, namely:

[0147] The technical documentation of the target intelligent agent development platform is parsed to obtain API change information, and the API change information is structured and stored in the information change database.

[0148] Obtain the original process code of the target intelligent agent, extract all API call records in the process code, and match and map the extracted API call records with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result;

[0149] Based on the matching mapping results, an impact score is calculated for each affected API call, and the impact scores of all affected API calls are aggregated and normalized to obtain the overall compatibility impact score.

[0150] A compatibility assessment report is generated based on the matching mapping results and the overall compatibility impact score.

[0151] In some embodiments, the intelligent agent development platform upgrade compatibility assessment method as described in any one of the embodiments of the present invention can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the intelligent agent development platform upgrade compatibility assessment method described above as described in any one of the embodiments of the present invention can be performed. Alternatively, in other embodiments, processor 11 can be configured by any other suitable means (e.g., by means of firmware) to perform the intelligent agent development platform upgrade compatibility assessment method as described in any one of the embodiments of the present invention.

[0152] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0154] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0155] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0156] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0157] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0158] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0159] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for evaluating the upgrade compatibility of an intelligent agent development platform, characterized in that, The method includes: The technical documentation of the target intelligent agent development platform is parsed to obtain the API change information, and the API change information is structured and stored in the information change database. Obtain the original process code of the target intelligent agent, extract all API call records in the process code, and match and map the extracted API call records with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result; Based on the matching mapping results, an impact score is calculated for each affected API call, and the impact scores of all affected API calls are aggregated and normalized to obtain the overall compatibility impact score. A compatibility assessment report is generated based on the matching mapping results and the overall compatibility impact score.

2. The method according to claim 1, characterized in that, The technical documentation of the target intelligent agent development platform is parsed to obtain API change information, and this API change information is structured and stored in an information change database, including: API change information in the technical documents is extracted using a pre-trained large language model or document parsing tool. The extracted API change information is transformed into structured data containing change type, new signature, old signature, and estimated impact risk level; The structured data is stored in an information change database, where the data supports manual input of supplementary information.

3. The method according to claim 1, characterized in that, Obtain the original process code of the target intelligent agent, extract all API call records from the process code, and match and map the extracted API call records with the API change information in the information change database to obtain the change type corresponding to the affected API call as the matching and mapping result, including: By using a pre-trained large language model, the fully qualified names and method signatures of all API calls in the original process code of the target intelligent agent are identified. According to the preset mapping rules, each API call is matched with the API change information recorded in the information change database, and a mapping report of the affected API calls is generated as the matching mapping result.

4. The method according to claim 3, characterized in that, Based on preset mapping rules, each API call is matched with the API change information recorded in the information change database, and a mapping report of the affected API calls is generated, including: The information change database is retrieved using the fully qualified name of each API call as the primary key. If the corresponding API change information is successfully located, the current API call is determined to be an affected API call. The method signature of each affected API call is compared with the old version signature and the new version signature recorded in the corresponding API change information to determine the change type corresponding to each affected API call. Based on the change type corresponding to each of the affected API calls, generate code adaptation suggestions for each affected API call; Summarize the change types and code adaptation suggestions for all affected API calls, and generate a structured call mapping report.

5. The method according to claim 1, wherein an impact score is calculated for each affected API call based on the matching mapping result, including: Based on the change type of each affected API call, the corresponding weight value is obtained from a pre-defined weight table, where the weight value can be adjusted according to the actual situation; The frequency of each affected API call in the original process code of the target agent is counted. The more times the affected API is called, the greater the total impact of the affected API. Determine the business criticality coefficient for each affected API call based on the importance of each affected API in the business process; The impact score for each affected API call is calculated using a predefined scoring formula, which is: in, Score the impact of the i-th affected API call. Let i be the weight value of the i-th affected API call. Let i be the call frequency of the i-th affected API call. Let be the business criticality coefficient for the i-th affected API call.

6. The method according to claim 1, characterized in that, The impact scores of all affected API calls are summarized and normalized to obtain the overall compatibility impact score, including: The total impact score of all affected API calls is obtained by summing up the impact scores of all affected API calls. The overall compatibility impact score is obtained by normalizing the total impact score by dividing the total impact score by the total number of all affected API calls.

7. The method according to any one of claims 1-6, characterized in that, Based on the matching mapping results and the overall compatibility impact score, a compatibility assessment report is generated, including: Based on all affected API calls included in the matching mapping results, a structured issue summary list is generated, wherein the issue summary list includes the location, change type, risk level, and code adaptation suggestions for each affected API call; Based on the predefined range of the overall compatibility impact score, an overall risk assessment conclusion is generated, and the remediation priority is marked for issues of different risk levels in the structured issue summary list; Based on the summarized list of issues and the overall risk assessment conclusions, a compatibility assessment report is generated.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the intelligent agent development platform upgrade compatibility assessment method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the intelligent agent development platform upgrade compatibility evaluation method according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the intelligent agent development platform upgrade compatibility assessment method according to any one of claims 1-7.

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