Intelligent front-end and rear-end joint debugging method and system

Abstract

The invention discloses an intelligent front end and rear end joint debugging method and system. The intelligent front-end and rear-end joint debugging method comprises the steps of S1, performing intelligent management on historical records, S2, adopting an intelligent association mechanism for version numbers, S3, binding state machines, S4, performing instant abnormity early warning and notification, and S5, automatically generating interface documents and test cases. According to the method, the functions of intelligent historical record management, intelligent version number association, instant abnormity early warning and the like are introduced, so that the joint debugging efficiency and quality are remarkably improved, a powerful supporting tool is provided for fast-paced software development, and more stable and reliable internet products can be constructed.

Description

Technical Field

[0001] The present invention relates to the field of testing technology, and in particular to an intelligent front-end and back-end joint debugging method and system. Background Art

[0002] In modern software development, separation of front-end and back-end has become the mainstream architecture, but it brings with it various challenges in the joint debugging process. Although existing tools can effectively assist API testing and debugging, they are insufficient in version control, historical change review, and abnormal warning, making it difficult to quickly locate the source of problems in complex projects, affecting development progress.

[0003] Therefore, the prior art has defects and needs to be improved. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent front-end and back-end joint debugging method and system, an efficient joint debugging solution integrating historical record management, intelligent association of version numbers and instant notification functions, so as to optimize the pain points in the existing front-end and back-end development collaboration process.

[0005] The technical solution of the present invention is as follows: an intelligent front-end and back-end joint debugging method is provided, comprising the following steps.

[0006] S1: Intelligent management of historical records; specifically including: S11: Data collection and storage; that is, storage of joint debugging logs, the request details of each joint debugging log include: request timestamp, URL, HTTP method, request header, request body, response status code, response body and execution environment configuration; S12: Build an interactive timeline view; that is, each joint debugging log record is marked with a time point, color coding indicates different status (success / failure / warning), and mouse hover displays detailed log information; provides zoom and pan functions to facilitate viewing of joint debugging history of different time spans; S13: Implement intelligent search and comparison; implement fuzzy search, exact matching and advanced filtering functions; introduce difference comparison tools, such as Git Diff algorithm, to intuitively display the changes in requests and responses between different versions, and assist in quickly locating differences; S14: Problem reproduction and analysis; re-initiate the request according to the log record to simulate the request environment and parameters at that time.

[0007] S2: Adopt an intelligent association mechanism for version numbers; specifically including: S21: Version number tracking; Use continuous integration tools to listen to code submission events through Webhook, parse the version tags in the submission information, and automatically update the version mapping table in the database; S22: Dynamic routing and version matching; Based on the dynamic routing method, the front-end carries the expected API version number when requesting, and the back-end routes to the corresponding API service instance according to the version number; If the version does not match, the system automatically returns a prompt message to explain the reason and recommend a matching version; S23: Version conflict prediction model; Build a machine learning model to predict upcoming version compatibility issues based on historical version change data and conflict patterns; The machine learning model should consider the frequency of API changes and dependency changes, and regularly train and evaluate the model accuracy; S24: Early warning and collaboration platform integration; Once a version conflict risk is discovered, an early warning message is automatically sent to the relevant development team and project managers through instant messaging tools such as DingTalk robots and Slack, with conflict details and suggested solutions.

[0008] S3: Bind the state machine; specifically includes: S31: State machine model definition; clarify the interface state transfer rules, such as: New -> Under Development -> Under Testing -> Released, use the state machine library (such as XState) to implement the state logic to ensure the atomicity and consistency of state changes; S32: Authority control and review process; add approval links in the state transition process, for example, before switching from Under Development to Released state, it must be verified by the testing department and reviewed by the product manager, and provide the corresponding operation interface and notification mechanism; S33: Versioned API management; when the interface is released or updated, it is mandatory to associate the version number, and automatically create an independent route for the new version; at the same time, display the interface change details between versions.

[0009] S4: Instant abnormal warning and notification; specifically includes: S41: Anomaly detection algorithm; use machine learning algorithms, such as isolation forest or time series analysis, to analyze request response time and data structure in real time and identify abnormal patterns; set reasonable thresholds to distinguish normal fluctuations from abnormal situations; S42: Warning classification and customized notification; divide the warning level according to the severity of the abnormality (such as minor warning, serious error), and set different notification strategies for different levels (such as information notification for minor warnings; voice phone notification for serious errors); and allow developers to personalize subscription to warning notification channels and time periods; S43: Fault diagnosis suggestions; combine the historical fault library and repair database to provide preliminary diagnosis reports and repair suggestions for each warning; use natural language generation technology to convert technical details into easy-to-understand guidance copy.

[0010] S5: Automatically generate interface documents and test cases; specifically including: S51: Develop an OpenAPI specification parser to support automatic generation of OpenAPI documents from code comments, Swagger annotations or YAML / JSON files; ensure that the document is synchronized with the actual interface implementation; S52: Dynamic document rendering; use tools such as Swagger UI or ReDoc to dynamically display the generated OpenAPI document as an interactive API document page, and developers can try to call the interface directly on the page; S53: Test case generation and automated execution; based on the OpenAPI specification, automatically generate unit test and integration test cases.

[0011] Furthermore, in step S11, Elasticsearch or MongoDB technology is used to store the joint debugging log.

[0012] Furthermore, in step S12, D3.js or AntV chart library is used to construct an interactive timeline view.

[0013] Further, in step S21, the continuous integration tool is Jenkins or GitLab CI / CD.

[0014] Furthermore, in step S23, the machine learning model should consider factors such as API change frequency and dependency change, and regularly train and evaluate the model accuracy.

[0015] Furthermore, in step S53, the integrated test framework uses JUnit or TestNG to realize the automated execution of test cases and result feedback; and through the continuous integration test suite, it is ensured that each build can perform API function verification.

[0016] The present invention also provides an intelligent front-end and back-end joint debugging system, which adopts the intelligent front-end and back-end joint debugging method to perform front-end and back-end joint debugging.

[0017] By adopting the above scheme, the present invention provides an intelligent front-end and back-end joint debugging method and system, which integrates historical record management, intelligent association of version numbers and instant notification functions to provide an efficient joint debugging solution, so as to optimize the pain points in the existing front-end and back-end development collaboration process, such as information asynchrony, difficulty in error tracing and version mismatch. Compared with existing tools such as Postman and ApiFox, this system innovatively integrates advanced automated tracking technology and machine learning algorithms to ensure that in a fast-iterative development environment, the front-end and back-end development teams can work together more seamlessly, improve development efficiency and reduce online failures. By introducing functions such as intelligent historical record management, intelligent association of version numbers and instant abnormality warning, the efficiency and quality of joint debugging are significantly improved, providing a powerful support tool for fast-paced software development, and helping to build more stable and reliable Internet products. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0019] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0020] See also Figure 1 The present invention provides an intelligent front-end and back-end joint debugging method, which includes the following steps.

[0021] S1: Intelligent management of historical records; specifically including: S11: Data collection and storage; that is, storage of joint debugging logs, request details of each joint debugging log include: request timestamp, URL, HTTP method, request header, request body, response status code, response body and execution environment configuration; S12: Build an interactive timeline view; that is, each joint debugging log record is marked with a time point, color coding indicates different states (success / failure / warning), and mouse hovering displays detailed log information; provide zoom and pan functions to facilitate viewing of joint debugging processes of different time spans; S13: Implement intelligent search and comparison; implement fuzzy search, exact matching and advanced filtering functions; introduce difference comparison tools, such as Git Diff algorithm, to intuitively display the request and response changes between different versions, and assist in quickly locating differences; S14: Problem reproduction and analysis; re-initiate requests based on log records to simulate the request environment and parameters at the time. In step S11, Elasticsearch or MongoDB technology is used to store joint debugging logs. In step S12, D3.js or AntV chart library is used to build an interactive timeline view.

[0022] S2: Adopt intelligent association mechanism for version number; specifically include: S21: version number tracking; use continuous integration tools to listen to code submission events through Webhook, parse the version tag in the submission information, and automatically update the version mapping table in the database; S22: dynamic routing and version matching; based on the dynamic routing method, the front-end carries the expected API version number when requesting, and the back-end routes to the corresponding API service instance according to the version number; if the version does not match, the system automatically returns a prompt message to explain the reason and recommend a matching version; S23: version conflict prediction model; build a machine learning model to predict upcoming version compatibility issues based on historical version change data and conflict patterns; the machine learning model should consider the frequency of API changes and dependency changes, and regularly train and evaluate the model accuracy; S24: early warning and collaboration platform integration; once the risk of version conflict is found, the warning message is automatically sent to the relevant development team and project manager through instant messaging tools such as DingTalk robots and Slack, with conflict details and suggested solutions. In step S21, the continuous integration tool is Jenkins or GitLab CI / CD. In step S23, the machine learning model should consider the frequency of API changes and dependency changes, and regularly train and evaluate the model accuracy.

[0023] S3: Bind the state machine; specifically includes: S31: State machine model definition; clarify the interface state transfer rules, such as: New -> Under Development -> Under Testing -> Released, use the state machine library (such as XState) to implement the state logic to ensure the atomicity and consistency of state changes; S32: Authority control and review process; add approval links in the state transition process, for example, before switching from Under Development to Released state, it must be verified by the testing department and reviewed by the product manager, and provide the corresponding operation interface and notification mechanism; S33: Versioned API management; when the interface is released or updated, it is mandatory to associate the version number, and automatically create an independent route for the new version; at the same time, display the interface change details between versions.

[0024] S4: Instant abnormal warning and notification; specifically includes: S41: Anomaly detection algorithm; use machine learning algorithms, such as isolation forest or time series analysis, to analyze request response time and data structure in real time and identify abnormal patterns; set reasonable thresholds to distinguish normal fluctuations from abnormal situations; S42: Warning classification and customized notification; divide the warning level according to the severity of the abnormality (such as minor warnings, serious errors), and set different notification strategies for different levels; and allow developers to personalize subscriptions to warning notification channels and time periods; S43: Fault diagnosis suggestions; combine the historical fault library and repair database to provide preliminary diagnosis reports and repair suggestions for each warning; use natural language generation technology to convert technical details into easy-to-understand guidance copy.

[0025] S5: Automatically generate interface documents and test cases; specifically including: S51: Develop an OpenAPI specification parser to support automatic generation of OpenAPI documents from code comments, Swagger annotations or YAML / JSON files; ensure that the document is synchronized with the actual interface implementation; S52: Dynamic document rendering; use tools such as Swagger UI or ReDoc to dynamically display the generated OpenAPI document as an interactive API document page, and developers can try to call the interface directly on the page; S53: Test case generation and automated execution; based on the OpenAPI specification, automatically generate unit test and integration test cases.

[0026] In step S53, the integrated test framework uses JUnit or TestNG to realize the automated execution of test cases and result feedback; and through the continuous integration test suite, it is ensured that the API function can be verified in each build.

[0027] The present invention also provides an intelligent front-end and back-end joint debugging system, which adopts the intelligent front-end and back-end joint debugging method to perform front-end and back-end joint debugging.

[0028] In summary, the present invention provides an intelligent front-end and back-end joint debugging method and system, which integrates historical record management, intelligent association of version numbers and instant notification functions to provide an efficient joint debugging solution, so as to optimize the pain points in the existing front-end and back-end development collaboration process, such as information asynchrony, difficulty in error tracing and version mismatch. Compared with existing tools such as Postman and ApiFox, this system innovatively integrates advanced automated tracking technology and machine learning algorithms to ensure that in a fast-iterative development environment, the front-end and back-end development teams can work together more seamlessly, improve development efficiency and reduce online failures. By introducing functions such as intelligent historical record management, intelligent association of version numbers and instant anomaly warning, the efficiency and quality of joint debugging are significantly improved, providing a powerful support tool for fast-paced software development, and helping to build more stable and reliable Internet products.

[0029] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent front-end and back-end joint debugging method, characterized in that: include: S1: Intelligent management of historical records; specifically including: S11: Collect and store data; that is, store joint debugging logs. The request details of each joint debugging log include: request timestamp, URL, HTTP method, request header, request body, response status code, response body and execution environment configuration; S12: Build an interactive timeline view. Each joint debugging log record is marked with a time point, and color coding indicates different states. Hovering the mouse will display detailed log information. Zooming and panning functions are provided to facilitate viewing of the joint debugging process in different time spans. S13: Implement intelligent search and comparison; implement fuzzy search, exact matching and advanced filtering functions; introduce a difference comparison tool to intuitively display the request and response changes between different versions, and assist in quickly locating differences; S14: Problem reproduction and analysis: Re-initiate the request based on the log records and simulate the request environment and parameters at that time; S2: Adopt intelligent association mechanism for version number; specifically: S21: Version number tracking: Using continuous integration tools, monitoring code submission events through Webhooks, parsing version tags in submission information, and automatically updating the version mapping table in the database; S22: Dynamic routing and version matching: Based on the dynamic routing method, the front-end carries the expected API version number when making a request, and the back-end routes to the corresponding API service instance based on the version number; if the version does not match, the system automatically returns a prompt message to explain the reason and recommend a matching version; S23: Version conflict prediction model: Build a machine learning model to predict upcoming version compatibility issues based on historical version change data and conflict patterns; S24: Early warning and collaboration platform integration; once a version conflict risk is discovered, an early warning message is automatically sent to the relevant development team and project manager through instant messaging tools, along with conflict details and suggested solutions; S3: Bind the state machine; specifically includes: S31: State machine model definition; clarify the interface state transfer rules, use the state machine library to implement state logic, and ensure the atomicity and consistency of state changes; S32: Authority control and review process; add approval process in the state transition process; S33: Versioned API management: When an interface is released or updated, it is mandatory to associate the version number and automatically create an independent route for the new version; at the same time, the interface change details between versions are displayed; S4: Immediate abnormal warning and notification; specifically including: S41: Anomaly detection algorithm: Use machine learning algorithms to analyze request response time and data structure in real time and identify abnormal patterns; set reasonable thresholds to distinguish normal fluctuations from abnormal situations; S42: Warning classification and customized notifications: Classify warning levels according to the severity of abnormalities, and set different notification strategies for different levels; and allow developers to customize subscription warning notification channels and time periods; S43: Fault diagnosis suggestions: Combine the historical fault database and repair database to provide a preliminary diagnosis report and repair suggestions for each warning; use natural language generation technology to convert technical details into easy-to-understand guidance copy; S5: Automatically generate interface documentation and test cases; specifically including: S51: Develop an OpenAPI specification parser to support automatic generation of OpenAPI documents from code comments, Swagger annotations or YAML / JSON files; ensure that the documents are synchronized with the actual interface implementation; S52: Dynamic document rendering: Use Swagger UI or ReDoc tools to dynamically display the generated OpenAPI document as an interactive API document page, so that developers can try to call the interface directly on the page; S53: Test case generation and automated execution; automatically generate unit test and integration test cases based on the OpenAPI specification.

2. According to claim 1, an intelligent front-end and back-end joint debugging method is characterized in that: In step S11, Elasticsearch or MongoDB technology is used to store the joint debugging log.

3. According to the intelligent front-end and back-end joint debugging method of claim 1, it is characterized in that: In step S12, an interactive timeline view is constructed using D3.js or AntV chart library.

4. According to claim 1, an intelligent front-end and back-end joint debugging method is characterized in that: In step S21, the continuous integration tool is Jenkins or GitLab CI / CD.

5. The intelligent front-end and back-end joint debugging method according to claim 1 is characterized in that: In step S23, the machine learning model should take into account the frequency of API changes and dependency changes, and regularly train and evaluate the model accuracy.

6. The intelligent front-end and back-end joint debugging method according to claim 1 is characterized in that: In step S53, the integrated test framework uses JUnit or TestNG to realize the automated execution of test cases and result feedback; and through the continuous integration test suite, it is ensured that the API function can be verified in each build.

7. An intelligent front-end and back-end joint debugging system, characterized in that: The intelligent front-end and back-end joint debugging method described in any one of claims 1 to 6 is used to perform front-end and back-end joint debugging.

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