A compliance test report generation system based on data mapping and template engine

Abstract

The application discloses a compliance test report generation system based on data mapping and template engine and relates to the technical field of computer software. The system comprises a test data acquisition module, a data collection and storage module, a national standard characteristic mapping engine, a template engine, a report generation module, a double-layer data verification module, a historical feedback learning module, a multi-standard compatible interface module and a control module. The test data acquisition module is used for connecting various test tools and management systems to collect original execution data and complete data preprocessing and standardization. The application realizes the structured collection of test data into eight quality characteristics through the national standard characteristic mapping engine, combines the automatic report generation architecture driven by the template engine, realizes the accurate compliance control of the report content through the double-layer data verification mechanism and the historical feedback learning module, provides a multi-standard compatible interface scheme, significantly improves the report generation efficiency, and guarantees the content integrity, format standardization and national standard compliance of the test report.

Description

Technical Field

[0001] This invention relates to the field of computer software technology, and in particular to a compliance test report generation system based on data mapping and template engine. Background Technology

[0002] With the continuous expansion of software system scale and the increasing complexity of application scenarios, test reports, as a core output for verifying software product quality and compliance with specifications, have been widely used in several key stages such as software product acceptance, system auditing, and industry supervision. These application scenarios place increasingly stringent requirements on the completeness of test report content, format standardization, and compliance with national standards. The issue of compliant test report generation has gradually become a core bottleneck restricting software product quality control and audit traceability, posing unprecedented challenges to related technical fields.

[0003] The GB / T 25000.51 standard, "Systems and Software Engineering: Requirements and Evaluation of System and Software Quality," organizes test reports into eight quality characteristics: functionality, performance efficiency, usability, reliability, information security, maintainability, compatibility, and portability. Each characteristic corresponds to specific evaluation indicators and scoring rules. Report writing must strictly adhere to these eight characteristics in a structured manner to meet the mandatory requirements of national standard compliance for acceptance review and regulatory audit. It is worth noting that different application scenarios have significantly different focuses on these eight characteristics. For example, the financial regulatory field emphasizes information security and reliability, while the industrial control field focuses more on performance efficiency and maintainability.

[0004] However, in existing technical solutions, test report generation is typically considered an adjunct to the test execution process, employing a decentralized, post-testing, and manually compiled model. Log data, performance data, and defect data generated during test execution are usually scattered in unstructured form across various testing tools and management systems, lacking a unified data collection and storage mechanism. This makes it difficult to obtain complete and accurate raw execution data in a timely manner when writing reports. Furthermore, the organization of report content relies entirely on the writer's personal experience and understanding of national standards, failing to establish a fixed mapping between report templates and the eight key characteristics of national standards. This easily leads to omissions or descriptions of crucial sections such as functionality, performance efficiency, usability, reliability, information security, maintainability, compatibility, and portability. In addition, existing technical solutions lack the ability to effectively accumulate and learn from historical test data, failing to automatically optimize report structure and presentation based on feedback from past reports, resulting in recurring compliance issues. This technical solution, which separates test execution from report generation, not only leads to low report writing efficiency but also insufficient compliance between report content and national standard requirements. Meanwhile, with the deepening of software product exports and cross-border regulatory cooperation, ensuring that test reports meet the compliance requirements of different domestic and international standards, and ensuring national standard compliance while reducing the workload of report writing, have become key technical challenges that urgently need to be addressed by those skilled in the art. The traditional approach of relying on human experience for report preparation is no longer sufficient to meet the higher compliance requirements of these emerging application scenarios. Therefore, there is an urgent need to provide a compliance test report generation system based on data mapping and template engines. Summary of the Invention

[0005] To address the existing technical problems, this invention provides a compliance test report generation system based on data mapping and a template engine. It achieves structured aggregation of test data into eight quality characteristics through a national standard feature mapping engine, combines an automated report generation architecture driven by a template engine, and integrates a two-layer data verification mechanism and a historical feedback learning module to achieve precise compliance control of report content. Furthermore, it provides a multi-standard compatible interface solution, significantly improving report generation efficiency and ensuring the completeness of test report content, format standardization, and national standard compliance.

[0006] In the first aspect, you have invented a compliance test report generation system based on data mapping and template engine, including a test data acquisition module, a data collection and storage module, a national standard feature mapping engine, a template engine, a report generation module, a two-layer data verification module, a historical feedback learning module, a multi-standard compatible interface module, and a control module. The test data acquisition module is used to interface with various test tools and management systems to collect raw execution data and to complete data preprocessing and standardization. The data collection and storage module is used to build a unified test data warehouse to realize the structured storage and unified management of unstructured test data such as logs, performance data, and defects. The national standard characteristic mapping engine is used to construct the mapping relationship between test data and the eight quality characteristics of the national standard, and to classify and collect the collected test data according to the eight quality characteristics. The template engine is used to generate structured test report content by calling predefined report templates based on the classification results of the national standard feature mapping engine. The report generation module is used to integrate the output of the template engine and generate a complete test report that meets national standards. The dual-layer data verification module is used to automatically verify the completeness and compliance with national standards of the report content, and triggers a correction process when a deviation is detected. The historical feedback learning module is used to store feedback data from previous report reviews and automatically optimize the mapping rules of the national standard feature mapping engine and the content organization logic of the report template based on the feedback information. The multi-standard compatible interface module is used to support the conversion of compliance requirements of different domestic and international standards, and to ensure the cross-border regulatory cooperation needs of test reports.

[0007] Preferably, the data collection and storage module includes a data warehouse storage engine, a metadata management unit, a data indexing unit, and a data retrieval unit; The data warehouse storage engine adopts a distributed storage architecture, with the underlying storage layer built on the Hadoop HDFS distributed file system, supporting elastic expansion of petabyte-level test data. The metadata management unit is responsible for maintaining the data dictionary, data lineage, and data quality statistics of the data warehouse. The metadata is stored in a MySQL relational database, and the high availability of the metadata service is ensured through a master-slave replication mechanism. The data indexing unit is built on the ElasticSearch full-text search engine and supports multi-dimensional retrieval by test project, test type, time range and keywords. The data retrieval unit provides a unified data query interface, supporting both standard SQL query syntax and native ElasticSearch DSL query syntax.

[0008] Preferably, the national standard feature mapping engine includes a mapping rule configuration unit, a mapping relationship storage unit, an automatic classification unit, and a mapping result output unit; The mapping rule configuration unit supports user-defined mapping relationships between test data fields and national standard quality characteristics, and multiple mapping rules can be configured under each quality characteristic. The mapping relationship storage unit uses Neo4j graph database to store the mapping relationship graph. The graph nodes include test data field nodes and national standard quality characteristic nodes, and the edges represent the mapping relationship and its weight. The automatic classification unit automatically classifies and labels the test data in the data collection and storage module according to the rules preset by the mapping rule configuration unit, and assigns a corresponding quality characteristic label to each test data, supporting batch classification and incremental classification. The mapping result output unit outputs the classification results to the template engine in the form of structured data.

[0009] Preferably, the template engine includes a template management unit, a template parsing unit, a content filling unit, and an output formatting unit; The template management unit is written in FreeMarker template language, supports loops, conditional judgments, and macro definitions, and the template files are stored in the template library and support version control. The template parsing unit is responsible for parsing the template file and generating an executable template object. The parsing process includes syntax checking, variable binding and caching. The parsed template object is stored using an LRU caching strategy. The content filling unit fills the template with the corresponding test data according to the classification results output by the national standard feature mapping engine; The output formatting unit converts the filled template content into the target output format.

[0010] Preferably, the dual-layer data verification module includes an integrity verification unit, a compliance verification unit, a deviation alarm unit, and an automatic correction unit; The integrity verification unit checks the report content item by item according to the GB / T 25000.51 standard chapter list to see if all eight quality characteristic chapters are included. If a chapter is missing, it outputs a list of missing chapters and an explanation of the reason for the missing chapters. The compliance verification unit verifies the standardization of the report content and the compliance of the indicators. When the deviation alarm unit detects an integrity and compliance issue, it sends an alarm notification to the staff that includes the deviation type, deviation location, deviation details, and repair suggestions. The automatic correction unit automatically corrects the deviations that can be automatically repaired according to the preset correction rule template.

[0011] Preferably, the historical feedback learning module includes a feedback data storage unit, a feedback analysis unit, a rule optimization unit, and an optimization effect evaluation unit; The feedback data storage unit uses a MySQL database to store feedback records from each report review, and employs a table partitioning strategy to partition tables by year. The feedback analysis unit uses natural language processing technology to perform semantic analysis on feedback records, extract feedback keywords and feedback intent, and classifies and statistically analyzes similar feedback through clustering algorithms to identify high-frequency feedback issues and trend changes. The rule optimization unit automatically generates mapping rule optimization suggestions based on the analysis results of the feedback analysis unit; The optimization effect evaluation unit is used to compare the verification pass rate and report review pass rate before and after optimization, and to quantitatively evaluate the effect of the optimization measures.

[0012] Secondly, the present invention also provides a compliance test report generation method based on data mapping and a template engine, applied to the compliance test report generation system based on data mapping and a template engine as described above, the method comprising: S100: Perform data collection periodically according to the preset full / incremental collection strategy to generate raw execution data; S200: Perform quality verification, preprocessing, and standardization on the raw execution data; qualified data is stored in the data warehouse according to the partitioning strategy, and abnormal data generates alarm records. S300: Obtain the test data to be classified and complete the quality characteristic classification and labeling according to the preset rules; S400: Match the corresponding report template according to the quality characteristic classification results, load and parse the template file and cache the template object, and complete the test data placeholder filling; S500: Assemble the report according to the chapter order of GB / T 25000.51 standard, automatically fill in the cover information and generate a table of contents with page number links; S600: Verify the completeness and standardization of the chapters and contents specified in the standard, and output a deviation report; and repair manageable deviations according to preset rules. S700: Regularly collect review feedback data, identify high-frequency problem patterns through semantic analysis and clustering, generate optimization suggestions, and update mapping rules and template configurations after manual review; S800 generates the final report file according to the user-specified format, adopts an adaptation strategy to ensure the output effect of different formats, and triggers multi-standard compatibility conversion after the report is archived and pushes it to the target recipient according to the configuration. S900 enables cross-standard report conversion in cross-border scenarios, completes chapter mapping, indicator conversion and template adaptation, uses encrypted channels to ensure transmission security and generates audit traceability reports.

[0013] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. This invention uses a test data acquisition module to connect with various testing tools and management systems to complete the collection, cleaning, and standardization of raw execution data. It relies on a data aggregation and storage module to build a distributed unified test data warehouse to achieve structured storage and retrieval of multi-source unstructured test data. Furthermore, it establishes configurable mapping rules between test data fields and the eight quality characteristics of the GB / T 25000.51 standard through a national standard characteristic mapping engine. After automatic classification and labeling of the test data, a template engine completes content filling and report assembly according to the standard chapter structure. This effectively addresses the core pain points of traditional manual compilation methods, such as scattered and difficult-to-aggregate data and easy omissions or deviations in national standard chapters. It achieves full-chain national standard compliance constraints in test report generation from data to content, significantly improving the completeness of report content and compilation efficiency.

[0014] 2. By setting up a two-layer data verification module, the generated report is automatically verified from two dimensions: chapter completeness and content standardization. When compliance deviations are detected, multi-channel alarms are triggered and correctable issues are automatically corrected. At the same time, a historical feedback learning module performs semantic analysis and cluster statistics on review feedback data, automatically generates optimization suggestions for mapping rules and report templates, and quantifies the optimization effectiveness. This builds a self-optimizing closed-loop system of "generation-verification-feedback-iteration". It can not only correct report compliance issues in real time, but also continuously optimize the report generation logic based on historical experience, effectively reducing the probability of the same compliance issues recurring and significantly improving the first-time pass rate of report review.

[0015] 3. By configuring a multi-standard compatible interface module to build a domestic and international standard conversion rule base, it supports bidirectional adaptation and conversion of quality characteristics, evaluation indicators and report formats between different standards. It also meets the security requirements of cross-border data transmission through an encrypted transmission channel. At the same time, relying on the control module and the workflow engine, it realizes the automated scheduling, dynamic resource allocation and real-time operation monitoring of the entire report generation process. While ensuring national standard compliance, it adapts to the multi-standard compliance requirements of cross-border regulatory cooperation, realizes intelligent management and control of the entire report generation process, and effectively expands the application scenarios and adaptation scope of the system. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of a compliance test report generation system based on data mapping and template engine.

[0017] Figure 2 This is a logical diagram of the national standard feature mapping engine, template engine, and report generation module of the present invention.

[0018] Figure 3 This is a flowchart of a compliance test report generation method based on data mapping and template engine. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention. It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0020] Please see Figures 1-3 This embodiment proposes a compliance test report generation system based on data mapping and template engine. The system includes a test data acquisition module, a data collection and storage module, a national standard feature mapping engine, a template engine, a report generation module, a two-layer data verification module, a historical feedback learning module, a multi-standard compatible interface module, and a control module. The modules are interconnected through a data bus and control signal lines to form a complete closed-loop process for data processing and report generation.

[0021] The test data acquisition module is used to connect with various testing tools and management systems to collect raw execution data and perform data preprocessing and standardization. This module includes a data acquisition unit, a data preprocessing unit, and a data standardization unit. The data acquisition unit connects to external testing tools through a standardized interface, supporting data access from Jenkins continuous integration platform, JMeter performance testing tool, Appium automated testing framework, SonarQube code quality management tool, and Bugzilla defect management system. The data types that can be collected cover test execution logs, performance indicator data, code coverage data, and defect record data. The data preprocessing unit cleans, filters, and converts the collected raw execution data, removing invalid data and duplicate records, and uniformly converting data from different sources into a standardized intermediate format. The data standardization unit performs field mapping and type standardization on the preprocessed data according to preset metadata definitions to ensure data consistency and usability.

[0022] The data aggregation and storage module is used to build a unified test data warehouse, enabling structured storage and unified management of unstructured test data such as logs, performance data, and defect data. This module includes a data warehouse storage engine, a metadata management unit, a data indexing unit, and a data retrieval unit. The data warehouse storage engine adopts a distributed storage architecture, building the underlying storage layer based on the Hadoop HDFS distributed file system, supporting elastic expansion of petabyte-level test data, and configuring three storage replicas to ensure high data availability. The metadata management unit is responsible for maintaining the data dictionary, data lineage, and data quality statistics of the data warehouse. Metadata is stored using a MySQL relational database, and a master-slave replication mechanism ensures high availability of metadata services. The data indexing unit is built based on the ElasticSearch full-text search engine, providing millisecond-level retrieval capabilities for all test data, supporting multi-dimensional searches by test project, test type, time range, and keywords. The data retrieval unit provides a unified data query interface, supporting both standard SQL query syntax and native ElasticSearch DSL query syntax.

[0023] The national standard characteristic mapping engine is used to construct the mapping relationship between test data and the eight quality characteristics of the GB / T 25000.51 standard. It categorizes the collected test data according to eight dimensions: functionality, performance efficiency, usability, reliability, information security, maintainability, compatibility, and portability. The engine includes a mapping rule configuration unit, a mapping relationship storage unit, an automatic classification unit, and a mapping result output unit. The mapping rule configuration unit provides a visual mapping rule configuration interface, supporting user-defined mapping relationships between test data fields and national standard quality characteristics. Mapping rules are stored in JSON format, and multiple mapping rules can be configured under each quality characteristic. The conditional expressions of the rules support operators such as greater than, equal to, less than, contain, and regular expression matching; the mapping relationship storage unit uses the Neo4j graph database to store the mapping relationship graph, and the graph nodes include test data field nodes and national standard quality characteristic nodes, with edges representing mapping relationships and their weights; the automatic classification unit automatically classifies and labels the test data in the data collection and storage module according to the rules preset by the mapping rule configuration unit, and assigns corresponding quality characteristic labels to each test data, supporting batch classification and incremental classification; the mapping result output unit outputs the classification results to the template engine in the form of structured data.

[0024] The template engine is used to generate structured test report content by calling predefined report templates based on the classification results of the national standard feature mapping engine. The engine includes a template management unit, a template parsing unit, a content filling unit, and an output formatting unit. The template management unit provides functions for creating, editing, version management, and previewing report templates. Template files are written in the FreeMarker template language and support advanced template features such as loops, conditional statements, and macro definitions. Template files are stored in a template library and support version control. The template parsing unit is responsible for parsing template files and generating executable template objects. The parsing process includes syntax checking, variable binding, and caching. Parsed template objects are stored using an LRU caching strategy, with a configurable default value of 1000 template objects. The content filling unit fills the corresponding test data into the placeholder positions of the template based on the classification results output by the national standard feature mapping engine. The filling process supports data processing functions such as data formatting, numerical calculation, and date conversion. The output formatting unit converts the filled template content into the target output format, supporting PDF, Word, HTML, and JSON. PDF output uses the Apache FOP formatter, and Word output uses the Apache POI library.

[0025] The report generation module integrates the output of the template engine to generate a complete test report conforming to the GB / T 25000.51 standard format. This module includes a report assembly unit, a report cover generation unit, a report table of contents generation unit, and a report output unit. The report assembly unit assembles the content of each quality characteristic chapter generated by the template engine according to the order specified in the GB / T 25000.51 standard: cover, table of contents, overview, functional test results, performance efficiency test results, usability test results, reliability test results, information security test results, maintainability test results, compatibility test results, portability test results, conclusion, and appendix. The report cover generation unit automatically generates a report cover conforming to the standard format based on project information. The report table of contents generation unit automatically extracts the chapter titles to generate a report table of contents, which supports click-to-jump functionality. The report output unit outputs the assembled report content in the final target file format and supports automatic report archiving and version management.

[0026] The dual-layer data verification module is used to automatically verify the completeness and national standard compliance of the report content, and triggers a correction process when a deviation is detected. This module includes an integrity verification unit, a compliance verification unit, a deviation alarm unit, and an automatic correction unit. The integrity verification unit is based on GB / T... The report content is checked against the mandatory chapter list specified in standard 25000.51 to ensure that all eight quality characteristic chapters are included. If any chapter is missing, a list of missing chapters and an explanation of the reason for the missing chapters are output. The compliance verification unit verifies the standardization of the report content's descriptions and the conformity of the indicators. Verification rules include checking the minimum word count for descriptions, the format of indicator values, the compliance of scoring ranges, and the standardization of conclusion statements. The minimum word count for each quality characteristic chapter is set at 500 characters, and indicator values ​​must conform to the standard's requirements for units of measurement and significant figures. When a completeness or compliance issue is detected, the deviation alarm unit sends alarm notifications to relevant personnel via email, instant messaging tools, and system messages. The alarm content includes the deviation type, deviation location, deviation details, and remediation suggestions. The automatic correction unit automatically corrects remediable deviations based on preset correction rule templates. Corrections include format standardization, typo correction, and indicator value standardization. The entire correction process is logged to support audit traceability.

[0027] The historical feedback learning module stores feedback data from previous report reviews and automatically optimizes the mapping rules of the national standard feature mapping engine and the content organization logic of the report template based on the feedback information. This module includes a feedback data storage unit, a feedback analysis unit, a rule optimization unit, and an optimization effect evaluation unit. The feedback data storage unit uses a MySQL database to store feedback records from previous report reviews. Each feedback record includes the feedback number, report number, feedback provider, feedback time, feedback type, feedback content, and processing status. The feedback records are stored using a table partitioning strategy, partitioned by year. The feedback analysis unit uses natural language processing technology to perform semantic analysis on the feedback content, extracting feedback keywords and feedback intent. It then uses clustering algorithms to classify and statistically analyze similar feedback, identifying high-frequency feedback issues and trend changes. The rule optimization unit automatically generates mapping rule optimization suggestions and template content optimization suggestions based on the analysis results of the feedback analysis unit. Optimization suggestions take effect after manual review and confirmation, and the optimized content supports version management and can be rolled back to historical versions. The optimization effect evaluation unit compares the verification pass rate and report review pass rate before and after optimization, quantifying the effectiveness of the optimization measures. Evaluation indicators include the percentage increase in the first-time verification pass rate, the percentage decrease in the report review rework rate, and the percentage increase in report generation efficiency.

[0028] The multi-standard compatibility interface module supports the conversion of compliance requirements of different domestic and international standards, ensuring the cross-border regulatory collaboration needs of test reports. This module includes a standard definition unit, a standard conversion rule base, a format conversion unit, and a cross-border transmission unit. The standard definition unit provides functions for importing and managing custom standards, pre-configuring supported standards, and the standard definition content includes standard number, standard name, quality characteristic definition, evaluation index system, and report format requirements. The standard conversion rule base stores the conversion mapping relationships between different standards, including quality characteristic name mapping, index name mapping, and scoring rule mapping; the conversion rules support bidirectional conversion. The format conversion unit adaptively converts the report content and format according to the requirements of the target standard, covering chapter structure adjustment, index name replacement, scoring value conversion, and format style adjustment. The cross-border transmission unit provides a transmission channel that meets the security requirements of cross-border data transmission, supports encrypted data transmission, transmission signature verification, and transmission log recording, and uses HTTPS and SFTP as optional protocols.

[0029] The control module coordinates the operation of each module and enables intelligent management of the test report generation process. This module includes a process scheduling unit, a task management unit, a resource scheduling unit, and a runtime monitoring unit. The process scheduling unit, based on a workflow engine, automates the report generation process. The process steps include data acquisition, data aggregation, feature mapping, template rendering, report generation, two-layer verification, feedback learning, and report output. Both serial and parallel execution modes are supported between steps, with parallel tasks communicating via message queues. The task management unit provides functions for creating, pausing, resuming, canceling, and querying report generation tasks, and supports scheduled tasks and triggering. There are two task types: scheduled tasks use Cron expressions to define execution times, and triggered tasks support both API calls and Webhooks. The resource scheduling unit dynamically allocates computing resources based on task priority and system load. Resource types include CPU resources, memory resources, and storage I / O resources. Resource scheduling uses a fair scheduling algorithm to ensure that high-priority tasks are executed first. The operation monitoring unit monitors the running status and performance indicators of each module in real time. Monitoring indicators include module response time, task execution success rate, queue backlog length, and system resource utilization. Monitoring data is displayed through a Grafana visualization panel and supports historical data querying.

[0030] The compliance test report generation system based on data mapping and template engine described in this embodiment works as follows: S100: Perform data collection periodically according to the preset full / incremental collection strategy to generate raw execution data; S200: Perform quality verification, preprocessing, and standardization on the raw execution data; qualified data is stored in the data warehouse according to the partitioning strategy, and abnormal data generates alarm records. S300: Obtain the test data to be classified and complete the quality characteristic classification and labeling according to the preset rules; S400: Match the corresponding report template according to the quality characteristic classification results, load and parse the template file and cache the template object, and complete the test data placeholder filling; S500: Assemble the report according to the chapter order of GB / T 25000.51 standard, automatically fill in the cover information and generate a table of contents with page number links; S600: Verify the completeness and standardization of the chapters and contents specified in the standard, and output a deviation report; and repair manageable deviations according to preset rules. S700: Regularly collect review feedback data, identify high-frequency problem patterns through semantic analysis and clustering, generate optimization suggestions, and update mapping rules and template configurations after manual review; S800 generates the final report file according to the user-specified format, adopts an adaptation strategy to ensure the output effect of different formats, and triggers multi-standard compatibility conversion after the report is archived and pushes it to the target recipient according to the configuration. S900 enables cross-standard report conversion in cross-border scenarios, completes chapter mapping, indicator conversion and template adaptation, uses encrypted channels to ensure transmission security and generates audit traceability reports.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A compliance test report generation system based on data mapping and template engine, characterized in that, It includes a test data acquisition module, a data collection and storage module, a national standard feature mapping engine, a template engine, a report generation module, a two-layer data verification module, a historical feedback learning module, a multi-standard compatible interface module, and a control module; The test data acquisition module is used to interface with various test tools and management systems to collect raw execution data and to complete data preprocessing and standardization. The data collection and storage module is used to build a unified test data warehouse to realize the structured storage and unified management of unstructured test data such as logs, performance data, and defects. The national standard characteristic mapping engine is used to construct the mapping relationship between test data and the eight quality characteristics of the national standard, and to classify and collect the collected test data according to the eight quality characteristics. The template engine is used to generate structured test report content by calling predefined report templates based on the classification results of the national standard feature mapping engine. The report generation module is used to integrate the output of the template engine and generate a complete test report that meets national standards. The dual-layer data verification module is used to automatically verify the completeness and compliance with national standards of the report content, and triggers a correction process when a deviation is detected. The historical feedback learning module is used to store feedback data from previous report reviews and automatically optimize the mapping rules of the national standard feature mapping engine and the content organization logic of the report template based on the feedback information. The multi-standard compatible interface module is used to support the conversion of compliance requirements of different domestic and international standards, and to ensure the cross-border regulatory cooperation needs of test reports.

2. The compliance test report generation system based on data mapping and template engine according to claim 1, characterized in that, The data collection and storage module includes a data warehouse storage engine, a metadata management unit, a data indexing unit, and a data retrieval unit; The data warehouse storage engine adopts a distributed storage architecture, with the underlying storage layer built on the Hadoop HDFS distributed file system, supporting elastic expansion of petabyte-level test data. The metadata management unit is responsible for maintaining the data dictionary, data lineage, and data quality statistics of the data warehouse. The metadata is stored in a MySQL relational database, and the high availability of the metadata service is ensured through a master-slave replication mechanism. The data indexing unit is built on the ElasticSearch full-text search engine and supports multi-dimensional retrieval by test project, test type, time range and keywords. The data retrieval unit provides a unified data query interface, supporting both standard SQL query syntax and native ElasticSearch DSL query syntax.

3. The compliance test report generation system based on data mapping and template engine according to claim 2, characterized in that, The national standard feature mapping engine includes a mapping rule configuration unit, a mapping relationship storage unit, an automatic classification unit, and a mapping result output unit; The mapping rule configuration unit supports user-defined mapping relationships between test data fields and national standard quality characteristics, and multiple mapping rules can be configured under each quality characteristic. The mapping relationship storage unit uses Neo4j graph database to store the mapping relationship graph. The graph nodes include test data field nodes and national standard quality characteristic nodes, and the edges represent the mapping relationship and its weight. The automatic classification unit automatically classifies and labels the test data in the data collection and storage module according to the rules preset by the mapping rule configuration unit, and assigns a corresponding quality characteristic label to each test data, supporting batch classification and incremental classification. The mapping result output unit outputs the classification results to the template engine in the form of structured data.

4. The compliance test report generation system based on data mapping and template engine according to claim 3, characterized in that, The template engine includes a template management unit, a template parsing unit, a content filling unit, and an output formatting unit; The template management unit is written in FreeMarker template language, supports loops, conditional judgments, and macro definitions, and the template files are stored in the template library and support version control. The template parsing unit is responsible for parsing the template file and generating an executable template object. The parsing process includes syntax checking, variable binding and caching. The parsed template object is stored using an LRU caching strategy. The content filling unit fills the template with the corresponding test data according to the classification results output by the national standard feature mapping engine; The output formatting unit converts the filled template content into the target output format.

5. The compliance test report generation system based on data mapping and template engine according to claim 4, characterized in that, The dual-layer data verification module includes an integrity verification unit, a compliance verification unit, a deviation alarm unit, and an automatic correction unit. The integrity verification unit checks the report content item by item according to the GB / T 25000.51 standard chapter list to see if all eight quality characteristic chapters are included. If a chapter is missing, it outputs a list of missing chapters and an explanation of the reason for the missing chapters. The compliance verification unit verifies the standardization of the report content and the compliance of the indicators. When the deviation alarm unit detects an integrity and compliance issue, it sends an alarm notification to the staff that includes the deviation type, deviation location, deviation details, and repair suggestions. The automatic correction unit automatically corrects the deviations that can be automatically repaired according to the preset correction rule template.

6. The compliance test report generation system based on data mapping and template engine according to claim 5, characterized in that, The historical feedback learning module includes a feedback data storage unit, a feedback analysis unit, a rule optimization unit, and an optimization effect evaluation unit; The feedback data storage unit uses a MySQL database to store feedback records from each report review, and employs a table partitioning strategy to partition tables by year. The feedback analysis unit uses natural language processing technology to perform semantic analysis on feedback records, extract feedback keywords and feedback intent, and classifies and statistically analyzes similar feedback through clustering algorithms to identify high-frequency feedback issues and trend changes. The rule optimization unit automatically generates mapping rule optimization suggestions based on the analysis results of the feedback analysis unit; The optimization effect evaluation unit is used to compare the verification pass rate and report review pass rate before and after optimization, and to quantitatively evaluate the effect of the optimization measures.

7. A method for generating compliance test reports based on data mapping and template engines, characterized in that: The method, applied to the compliance test report generation system based on data mapping and template engine as described in any one of claims 1-6, comprises: S100: Perform data collection periodically according to the preset full / incremental collection strategy to generate raw execution data; S200: Perform quality verification, preprocessing, and standardization on the raw execution data; qualified data is stored in the data warehouse according to the partitioning strategy, and abnormal data generates alarm records. S300: Obtain the test data to be classified and complete the quality characteristic classification and labeling according to the preset rules; S400: Match the corresponding report template according to the quality characteristic classification results, load and parse the template file and cache the template object, and complete the test data placeholder filling; S500: Assemble the report according to the chapter order of GB / T 25000.51 standard, automatically fill in the cover information and generate a table of contents with page number links; S600: Verify the completeness and standardization of the chapters and contents specified in the standard, and output a deviation report; and repair manageable deviations according to preset rules. S700: Regularly collect review feedback data, identify high-frequency problem patterns through semantic analysis and clustering, generate optimization suggestions, and update mapping rules and template configurations after manual review; S800 generates the final report file according to the user-specified format, adopts an adaptation strategy to ensure the output effect of different formats, and triggers multi-standard compatibility conversion after the report is archived and pushes it to the target recipient according to the configuration. S900 enables cross-standard report conversion in cross-border scenarios, completes chapter mapping, indicator conversion and template adaptation, uses encrypted channels to ensure transmission security and generates audit traceability reports.

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