An intelligent software development auxiliary system

Abstract

The application discloses an intelligent software development auxiliary system, and relates to the field of artificial intelligence and software engineering, and the system comprises: a cause-effect analysis module which disassembles natural language requirements into structured data; a topology modeling module which quantifies node correlation and constructs a code cause-effect topology graph; an intervention regulation module which corrects large model deviation and generates initial code; a verification optimization module which covers all logic paths through counterfactual reasoning, automatically generates test cases and iteratively repairs defects; a traceability interpretation module which traces back all link cause-effect paths and outputs a visual explanation report; the application eliminates requirement deviation and improves code generation accuracy through cause-effect analysis, topology modeling and intervention regulation, reduces artificial modeling workload and shortens development cycle; and after verification optimization and traceability interpretation, all logic paths are covered and defects are iteratively corrected, a whole-process closed loop is formed, debugging cost and error rate are reduced, and code stability and traceability are enhanced.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of artificial intelligence and software engineering, specifically to an intelligent software development assistance system. Background Technology

[0002] With the rapid development of the software development industry and the continuous advancement of digital transformation, software development needs are becoming increasingly diverse and complex. The transformation of development requirements described in natural language into executable code has become a mainstream trend in the industry. The application of large model technology in the field of software development assistance is deepening. Cutting-edge technologies such as causal analysis, topology modeling, and counterfactual reasoning are gradually being deeply integrated with the software engineering process. Currently, the market has an increasingly urgent need for efficient, accurate, and compliant software development assistance tools. In the traditional development model, the links of requirement analysis, logic modeling, code generation, testing and verification, and technology tracing are fragmented and mostly rely on manual operation, which is difficult to adapt to the rapid iteration pace of development. The industry urgently needs an assistance system that can integrate the technical capabilities of the entire process and realize the intelligent processing of natural language requirements to solve core problems such as requirement misunderstanding, inaccurate code logic, incomplete test coverage, and lack of traceability, thereby comprehensively improving the overall efficiency and quality level of software development.

[0003] Traditional software development support technologies have many functional limitations and cannot meet the actual needs of modern software development. The requirement processing stage relies solely on manual semantic decomposition and logical sorting, which cannot fully extract core elements such as business constraints, data flow, branch logic, and exception restrictions. The integration of fragmented requirement information is ineffective, and it is easy to have misunderstandings of requirements and omissions of key information. The code generation stage relies solely on probabilistic models and lacks precise control at the causal relationship level, which can easily lead to false associations, logical deviations, and other problems. The fit between code and business requirements cannot be guaranteed. The testing stage relies on manually written test cases, which cannot fully cover the entire logical path of the code. There are obvious blind spots in the testing of boundary scenarios and abnormal conditions. The logic defect location is inefficient and difficult to correct. After the code is developed, there is no end-to-end traceability capability. The decision basis for algorithm selection, structural design, and logic correction cannot be clearly retained. Technical review and subsequent optimization lack effective support. Data in each stage does not form a closed loop, ultimately resulting in a long development cycle, high labor costs, and difficulty in guaranteeing code compliance and operational stability. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent software development assistance system. This system first transforms natural language requirements into structured data, then constructs a code causal topology map through a causal coupling mining algorithm, and generates standardized intervention rules using a causal deviation correction algorithm to effectively suppress large model deviations. The verification and optimization module covers the entire logical path based on counterfactual reasoning, automatically generates boundary and abnormal test cases, locates and iteratively repairs logical defects, and finally, the source tracing and explanation module traces back the entire causal path and outputs a visual explanation report.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an intelligent software development assistance system, the system comprising: Causal analysis module: Used to receive natural language development requirements, complete semantic decomposition and logical sorting, extract business constraints, data flow relationships, branch logic and exception restrictions, and organize and output structured requirement data; Topology modeling module: Receives structured requirement data, quantifies the correlation between code nodes and business logic nodes based on causal coupling mining algorithms, constructs a code causal topology graph, and synchronously outputs coupling feature parameters; Intervention and regulation module: Based on causal topology map and coupling feature parameters, combined with causal bias correction algorithm, implement multi-dimensional variable intervention, correct the correlation bias of large model, generate standardized causal intervention rules, constrain probabilistic error fitting logic and output initial generation code; Verification and optimization module: Receives the initial generated code, covers the entire logical path based on counterfactual reasoning, automatically generates boundary and exception test cases, locates logical defects and iteratively corrects them, and outputs highly compliant optimized code; Source tracing and explanation module: Receives highly compliant optimized code and full-chain operation data, traces back the code to generate a full-chain causal path, provides the decision basis for source tracing algorithm selection and structural design, and generates a visual explanation report.

[0006] Furthermore, in the causal analysis module, the semantic decomposition and logical organization are specifically as follows: The acquired natural language development requirements are semantically analyzed layer by layer. First, the complete business description statements are broken down and key semantic entities are extracted. Then, the functional implementation content and operational constraints are distinguished. The data transmission flow and interaction forms between different business links are sorted out. Regular execution branch logic and special scenario exception constraints are divided. Implicit logical connections and implicit constraints in the requirement text are filtered out. The scattered and fragmented original requirement information is uniformly organized, classified, integrated, and formatted in a standardized manner. Content is categorized and arranged in an orderly manner, forming structured requirement data with a unified format, complete logic, and comprehensive elements.

[0007] Furthermore, in the causal analysis module, business constraints include functional implementation specifications, development and writing standards, operational permission restrictions, and business process restrictions; data flow relationships include data input sources, data output destinations, cross-unit data interaction forms, data transmission sequence, and data call correspondence; branch logic includes normal execution branches, condition judgment branches, process jump branches, and multi-scenario parallel execution branches; and anomaly restrictions include abnormal input parameters, interrupted operation processes, boundary value exceeding limits, external environment adaptation anomalies, and special working condition restrictions. These multiple logical elements are categorized, identified, uniformly organized, and systematically integrated. Information is systematically entered based on a fixed data framework, and standardized output is provided, resulting in complete, hierarchically clear, and uniformly formatted structured requirement data.

[0008] Furthermore, in the topology modeling module, the mathematical expression for the causal coupling mining algorithm is: ; in, The global causal coupling degree between code node i and business logic node j is defined. Code node i contains variable definitions, function units, conditional statements, and loop structures, while business logic node j contains business constraints, data flow relationships, branch logic, and exception restrictions in structured requirement data. This is the direct causal weighting coefficient, with a value range of 0.5-0.7, representing the proportion of association between the econometric condition branch and the variable call. The semantic matching degree of the requirement code is calculated from the cosine similarity of the semantic vectors of the structured requirement data and the code nodes, with a value ranging from 0 to 1. The calculation formula is: ,in The semantic vector of business logic node j. Let i be the semantic vector of code node i. The vector 2 norm operator; It is an indirect causal transmission coefficient, with a value range of 0.3-0.5, which is suitable for the transmission characteristics of multi-level code links; The business constraint fit coefficient is the degree of matching between the logic implemented in code node i and the business constraints in business logic node j, which measures the degree of matching between the code structure and the development rules. represents the number of levels in the causal transmission path, indicating the logical jump depth between nodes; e is a natural constant with a value of approximately 2.71828. This is the logic attenuation coefficient, with a value range of 0.05-0.15, used to control the attenuation intensity of unnecessary correlations; is the logical deviation degree, used to measure the degree of deviation between the logical implementation of code node i and the expected logic of business logic node j. Its value range is a non-negative real number, which measures the magnitude of unnecessary correlation between nodes.

[0009] Furthermore, in the topology modeling module, the construction of the code causal topology graph relies on the quantified node association degree. It sequentially completes node definition, association labeling, and graph generation. Business logic units corresponding to structured requirement data are defined as business logic nodes, and program variables, function units, conditional branches, and loop structures are defined as code nodes. Both types of nodes are configured with unique identifiers and corresponding attribute fields. Using the global causal coupling degree output by the causal coupling mining algorithm as edge weights, the association relationships between nodes are assigned and labeled. Edge attributes are divided into three categories according to association type: direct association, indirect transmission association, and conditional triggering association. Node positions are arranged according to the data flow direction and logic execution sequence to complete the topological connection between nodes and edges, forming a complete association structure. Simultaneously, edge weight data, node identifiers, and association attributes are extracted from the graph and regularized to generate coupling feature parameters.

[0010] Furthermore, in the intervention and regulation module, the mathematical expression for the causal bias correction algorithm is: ; in, This represents the code causal intervention correction amount, used to support the formulation of standardized causal intervention rules; n is the total number of logical nodes; The business priority weight for the k-th logical node is preset to a fixed value of 0.5 for core business nodes, 0.3 for key data nodes, and 0.2 for auxiliary logical nodes. The superscript T represents the preset standard causal coupling threshold, which is uniformly set to 0.7; is the global causal coupling degree between code node i and business logic node j, and is the global causal coupling degree calculation result output by the causal coupling mining algorithm; The logical defect correction coefficient is assigned a value according to the type of code exception: 1.2 when there is a false association between nodes, 1.0 when there is a logical deviation, and 0.8 when there is no exception. The intervention intensity coefficient is a variable that is dynamically configured based on the coupling characteristic parameters, and its value ranges from 0 to 1.

[0011] Furthermore, in the intervention and control module, the standardized causal intervention rules are constructed based on the causal topological graph structure and causal coupling characteristic parameters, and include four core components: variable isolation rules, logical constraint rules, association filtering rules, and boundary limitation rules. Variable isolation rules are used to cut off unfounded false association links between code nodes and avoid erroneous correlation fitting of large models. Logical constraint rules limit the timing and operation logic of conditional branches, loop execution, and data interaction. Association filtering rules filter effective causal dependencies based on the global causal coupling degree value and eliminate weak association interference items. Boundary limitation rules are used to determine code syntax specifications, exception handling scope, and data transmission restrictions.

[0012] Furthermore, in the verification optimization module, counterfactual reasoning covering the entire logical path specifically involves: relying on the constructed causal topology graph to sort out the overall code operation architecture; based on the causal coupling relationship of code nodes, completely dissecting the main execution flow, branch judgment flow, function call flow, and data flow interaction flow; and through the counterfactual hypothesis deduction mode, performing reverse deduction and condition substitution on various key logical nodes of the code, fully traversing all logical directions such as forward operation, reverse feedback, and multi-branch cross-linking, comprehensively covering various application scenarios such as normal operation, critical conditions, and abnormal triggering. At the same time, combining path association characteristics to identify hidden logical breakpoints and dependency deviations, the module covers the entire execution link of the code without omission, providing complete logical support for test case generation and accurate defect location.

[0013] Furthermore, the boundary and anomaly test cases are custom-constructed by combining counterfactual reasoning mechanisms and causal topology graph features. Boundary test cases cover extreme operating scenarios such as parameter critical thresholds, extreme value ranges, empty data input, operating load limits, and process switching critical points, closely matching the critical constraints of actual program operation. Anomaly test cases include unconventional operating scenarios such as parameter exceeding limits, data type disorder, cross-module call conflicts, instruction execution interruption, resource access anomalies, and business logic contradictions. All test cases are classified and arranged based on the causal coupling relationship of the code, fully adapting to the entire code execution chain, achieving comprehensive scenario coverage, and providing comprehensive testing support for subsequent defect localization, problem tracing, and code iteration and correction.

[0014] Furthermore, in the verification and optimization module, locating and iteratively correcting logical defects specifically involves: running boundary and abnormal test cases in batches, comparing the code execution feedback data in real time with the causal topology association rules and business logic standards, quickly identifying implicit and explicit logical defects such as variable call errors, branch judgment conflicts, data flow breaks, and causal matching imbalances, marking the code node where the defect is located and the cause of the error, and combining the causal deviation correction benchmark parameters to perform targeted adaptation and adjustment for various defects, optimizing the code execution order, condition judgment rules, and data interaction logic, reusing test cases for verification after completing a single round of rectification, iterating and optimizing in a loop, gradually eliminating logical vulnerabilities, and finally outputting optimized code with a closed logical loop, stable operation, and higher compliance.

[0015] Furthermore, the source tracing and interpretation module fully connects the core processes of the early causal topology map construction, coupling feature parameter matching, causal deviation intervention and control, counterfactual reasoning full path verification, boundary anomaly testing and verification, and code iteration correction. It records the pre-triggered causes, intermediate execution coupling relationships, and post-running feedback results of each algorithm control, logic correction, and code modification action. It clearly connects the data flow relationship, decision transmission path, and deviation correction causal relationship of each module, and completely preserves the causal evolution trajectory of the code from initial generation to optimization and finalization, covering explicit execution logic and implicit related causes without omission.

[0016] Furthermore, the visual explanation report is compiled by integrating optimized code content, full-link operation data, and complete causal backtracking results. It features a graphical display format, intuitively presenting the causal path topology of the entire code generation process, clearly marking the decision-making basis for algorithm selection logic and program structure design, and simultaneously including records of logic defect rectification, causal intervention and control data, and verification optimization and adjustment trajectory. It categorizes and sorts out the core parameters, rule application status, and deviation correction details of each link, and breaks down the underlying logic of technical decisions in a combination of text and graphics, completely restoring the entire process of code iteration and optimization. The report content is well-organized and complete, and can intuitively display the control and optimization details of each module, providing a complete and intuitive reference for code review, technical tracing, and subsequent development and optimization.

[0017] Compared with existing technologies, this intelligent software development assistance system has the following beneficial effects: I. This invention utilizes a causal analysis module to semantically decompose and logically organize natural language development requirements, accurately extracting core elements such as business constraints, data flow, branch logic, and exception restrictions. Fragmented requirements are organized into logically complete and element-rich structured data, eliminating misunderstandings of requirements at the source. A topology modeling module quantifies the correlation between code nodes and business logic nodes, constructing a causal topology graph of the code and outputting coupling feature parameters. This clearly presents the correlation attributes and transmission logic between nodes, laying a solid logical foundation for code generation. An intervention and control module implements multi-dimensional variable intervention and causal deviation correction, constraining probabilistic error fitting logic, effectively correcting large model correlation deviations, and outputting initial generated code. This improves the accuracy and standardization of the conversion from natural language requirements to code, reduces the workload of manual requirement analysis and logical modeling, and shortens the early preparation cycle of software development.

[0018] Second, this invention utilizes a verification and optimization module to cover the entire logical path of the code with counterfactual reasoning, automatically generating boundary and abnormal test cases adapted to all scenarios. It comprehensively traverses all links, including normal operation, critical conditions, and abnormal triggers, accurately locating explicit and implicit logical defects and iteratively correcting them, significantly improving code stability and business compliance. Relying on the source tracing and explanation module, it traces back the entire causal path of code generation, completely retaining the full process basis for algorithm selection, structural design, deviation correction, and defect rectification, generating a visual explanation report that intuitively presents the decision logic and relationships of each link. The entire system forms a closed loop of the entire process of requirement analysis, code generation, optimization verification, and source tracing and explanation, eliminating the need for extensive manual participation in test review and logic tracing, reducing software development and debugging costs and error rates, and providing a complete reference basis for code technical tracing and subsequent iterative optimization.

[0019] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0021] Figure 1 A flowchart for an intelligent software development support system; Figure 2 A schematic diagram of data transmission for an intelligent software development support system; Figure 3 This is a schematic diagram of data transmission in the intervention and regulation module of the present invention. Detailed Implementation

[0022] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0023] Example 1: This embodiment addresses the actual development needs of the access control module in an enterprise's internal management system. It leverages the intelligent software development assistance system of this invention to automate the entire development process, from natural language requirement input to highly compliant code output. The entire process relies on the collaborative operation of the system's five built-in core modules, eliminating the need for extensive manual intervention in requirement analysis, logic modeling, code debugging, and traceability review. This improves the development efficiency and code quality of the access control module, effectively meeting the core requirements of enterprise internal management systems for rigorous access control, operational stability, and data security. Figure 1 As shown, the specific implementation process is as follows: The causal analysis module receives permission management module development requirements submitted by enterprise business personnel in natural language. It performs layer-by-layer semantic analysis and comprehensive logical analysis of the requirement text, completely breaking down business descriptions and extracting key semantic entities. It accurately distinguishes between permission function implementation content and system operation restrictions, comprehensively analyzes the data transmission flow and interaction forms between different permission business links, clearly delineates the execution branch logic of regular permissions and the abnormal restriction conditions of special permission scenarios, and deeply filters the implicit logical connections and implicit constraints in the requirement text. It unifies, classifies, integrates, and standardizes the originally scattered and fragmented natural language requirement information, completing the classification and orderly arrangement of all requirement elements. Finally, it outputs structured requirement data with a unified format, complete logic, and comprehensive elements. This fundamentally avoids problems such as misunderstanding biases, omissions of core elements, and unidentified implicit constraints that occur when manually analyzing requirements. It transforms vague natural language requirements into standard data that can be directly used for logical modeling, providing an accurate and complete input foundation for subsequent topology modeling and ensuring a highly rigorous logical starting point for the entire development process.

[0024] The topology modeling module receives structured requirement data output from the causal analysis module and uses a causal coupling mining algorithm to quantitatively analyze the correlation between code nodes and business logic nodes in the permission management scenario. The mathematical expression of the causal coupling mining algorithm is: ; in, The global causal coupling degree between code node i and business logic node j is defined. Code node i contains variable definitions, function units, conditional statements, and loop structures, while business logic node j contains business constraints, data flow relationships, branch logic, and exception restrictions in structured requirement data. This is the direct causal weighting coefficient, with a value range of 0.5-0.7, representing the proportion of association between the econometric condition branch and the variable call. The semantic matching degree of the requirement code is calculated from the cosine similarity of the semantic vectors of the structured requirement data and the code nodes, with a value ranging from 0 to 1. The calculation formula is: ,in The semantic vector of business logic node j. Let i be the semantic vector of code node i. The vector 2 norm operator; It is an indirect causal transmission coefficient, with a value range of 0.3-0.5, which is suitable for the transmission characteristics of multi-level code links; This is the business constraint fit coefficient, which represents the degree of matching between the logic implemented in code node i and the business constraints in business logic node j. ,in For code node i, specify the number of clauses that satisfy the business constraints in business logic node j. The preset baseline constraint matching number, k is an adjustment coefficient (ranging from 1 to 3), which measures the degree of matching between the code structure and the development rules; represents the number of levels in the causal transmission path, indicating the logical jump depth between nodes; e is a natural constant with a value of approximately 2.71828. This is the logic attenuation coefficient, with a value range of 0.05-0.15, used to control the attenuation intensity of unnecessary correlations; The logical deviation is used to measure the degree of deviation between the logical implementation of code node i and the expected logic of business logic node j. Where M is the total number of logical feature dimensions, For code node ii, the actual value of the logical feature in the mm-th dimension is... For the expected logical value of business logic node jj in this dimension, Let be the weight coefficient of the m-th dimension feature, with a range of non-negative real numbers. This represents the magnitude of unnecessary associations between nodes. Following system-defined rules, node definition, association labeling, and graph generation are performed. Units such as permission business constraints, data flow relationships, branch logic, and exception restrictions corresponding to structured requirement data are designated as business logic nodes. Code components such as program variables, function units, conditional branches, and loop structures are designated as code nodes. Unique identifiers and corresponding attribute fields are configured for both types of nodes. The global causal coupling degree output by the causal coupling mining algorithm is used as the edge weight to assign values ​​to the associations between nodes, classifying them as direct associations based on association type. The system identifies three types of edge attributes: indirect transmission association, conditional trigger association, and other types. Node positions are arranged according to the direction of permission data flow and the timing of logic execution, completing the topological connection between nodes and edges to form a complete association structure. Simultaneously, edge weight data, node identifiers, and association attributes are extracted from the graph, and coupling feature parameters are generated. This allows the relationship between permission business logic and code structure to be presented in the form of a visual topological graph, clearly showing the logical transmission depth and association strength between each node. This eliminates the logical gap between business and code, providing quantifiable and traceable logical basis for the intervention and control module, and ensuring that subsequent code generation closely aligns with the core logic of permission business.

[0025] The intervention and regulation module receives the code causal topology map and coupling feature parameters output by the topology modeling module, and combines them with the causal bias correction algorithm to implement multi-dimensional variable intervention in the code generation process. The mathematical expression of the causal bias correction algorithm is: ; in, This represents the code causal intervention correction amount, used to support the formulation of standardized causal intervention rules; n is the total number of logical nodes; The business priority weight for the k-th logical node is preset to a fixed value of 0.5 for core business nodes, 0.3 for key data nodes, and 0.2 for auxiliary logical nodes. The superscript T represents the preset standard causal coupling threshold, which is uniformly set to 0.7; is the global causal coupling degree between code node i and business logic node j, and is the global causal coupling degree calculation result output by the causal coupling mining algorithm; The logical defect correction coefficient is assigned a value according to the type of code exception: 1.2 when there is a false association between nodes, 1.0 when there is a logical deviation, and 0.8 when there is no exception. The variable intervention strength coefficient is dynamically configured based on coupling feature parameters, with a value range of 0-1. It accurately corrects deviations in the large model's permission logic relationships. Based on the graph structure and coupling feature parameters, standardized causal intervention rules are constructed, including variable isolation rules, logical constraint rules, association filtering rules, and boundary limitation rules. Variable isolation rules sever unfounded false association links between code nodes, avoiding erroneous correlation fitting in the large model. Logical constraint rules limit the timing and execution logic of permission condition branches, loop execution, and data interaction. Association filtering rules use causal coupling degree values ​​to filter effective causal dependencies and eliminate weak correlation interference items. Boundary limitation rules determine code syntax standards, exception handling scope, and data transmission restrictions. This constrains the probabilistic error fitting logic and outputs the initial generated code for the permission management module. This ensures the initial code adheres to the permission business logic standards from the generation stage, reducing logical defects and association errors in the initial code, lowering the workload of subsequent code optimization, and ensuring the initial code has basic business adaptability and operational compliance. Figure 3 As shown.

[0026] The verification and optimization module receives the initial generated code output by the intervention and control module. Based on counterfactual reasoning, it fully covers the entire logical path of the permission management module. Relying on the constructed causal topology graph, it sorts out the overall code operation architecture. Based on the causal coupling relationship of code nodes, it decomposes the main execution flow, branch judgment flow, function call flow, and data flow interaction flow. Through counterfactual hypothesis deduction mode, it reverse-derives and conditionally replaces various key logical nodes of the code, fully traversing all logical directions and comprehensively covering various application scenarios such as normal operation, critical conditions, and abnormal triggering. Combining path association characteristics, it identifies hidden logical breakpoints and dependency deviations, and automatically generates boundary and abnormal test cases adapted to the permission module. Boundary test cases cover extreme working conditions such as permission critical thresholds, empty permission data input, running load limits, and permission switching critical points. Abnormal test cases include permission parameter exceeding limits, data type disorder, cross-module call conflicts, and instruction execution interruption. For unconventional operational scenarios such as abnormal resource access and conflicting permission business logic, all test cases are categorized and arranged based on the causal coupling relationship of the code to adapt to the entire execution chain. By running test cases in batches and comparing the code execution feedback data in real time, implicit and explicit logical defects such as variable call errors, branch judgment conflicts, data flow breaks, and causal matching imbalances are quickly identified. The code node where the defect is located and the cause of the error are marked. Combined with the causal deviation correction benchmark parameters, targeted adaptation and adjustment are made for various defects, optimizing the code execution order, condition judgment rules and data interaction logic. After completing a single round of rectification, the test cases are reused for re-verification. Iterative optimization is carried out to gradually eliminate logical vulnerabilities. Finally, the optimized code of the permission management module with closed-loop logic, stable operation and higher compliance is output, so that the code can fully adapt to various scenarios of enterprise permission management, eliminate problems such as unauthorized permissions, logical conflicts and operation interruptions, and ensure the stability and security of system permission control.

[0027] The source tracing and interpretation module receives highly compliant optimized code and full-chain operation data output from the verification and optimization module. It traces back the entire causal path of code generation, completely connecting the core processes of causal topology map construction, coupled feature parameter matching, causal deviation intervention and control, counterfactual reasoning full-path verification, boundary anomaly testing and verification, and code iteration and correction. It records the pre-triggered causes, intermediate execution coupling relationships, and post-running feedback results of each algorithm adjustment, logic correction, and code modification action. It clearly connects the data flow relationships, decision transmission paths, and causal relationships of deviation correction between modules, completely preserving the causal evolution trajectory of the code from initial generation to optimization and finalization, covering explicit execution logic and implicit related causes without omission, and integrating the optimized code. The report provides a visual explanation of the content, full-link operation data, and complete causal backtracking results. It presents the causal path topology of the entire code generation process in a graphical form, clearly marking the decision-making basis for algorithm selection logic and program structure design. It also includes records of logic defect rectification, causal intervention and control data, and verification optimization and adjustment trajectory. The report categorizes and sorts the core parameters, rule application, and deviation correction details of each link, and breaks down the underlying logic of technical decisions in a combination of text and graphics. It fully restores the entire process of code iteration and optimization, providing a complete and intuitive reference for code review, technical traceability, and subsequent development and optimization of the permission module. This ensures that the entire development process is fully traceable, facilitating subsequent maintenance, upgrades, and problem troubleshooting for enterprise technical personnel.

[0028] This embodiment applies an intelligent software development assistance system to the development of the permission management module in an enterprise internal management system. Through the collaborative operation of five modules, it completes the entire process of requirement analysis, topology modeling, code generation, verification and optimization, and source tracing. It accurately transforms natural language requirements into highly compliant code, avoiding problems such as requirement analysis deviations, ambiguous logical modeling, numerous code defects, incomplete test coverage, and difficulty in traceability in traditional development. This improves development efficiency and code quality, ensures the stable operation and rigorous logic of the permission module, and provides complete support for code review and iterative optimization. It fully demonstrates the practical value and technical advantages of the system in enterprise management software development scenarios.

[0029] Example 2: This embodiment addresses the high concurrency, high stability, and high accuracy development requirements of the order settlement and payment verification module for e-commerce platforms. It utilizes the intelligent software development assistance system of this invention to automate the entire development process. The system closely adheres to the business characteristics of e-commerce transaction scenarios, using causal logic throughout the entire development process. This effectively solves development challenges in order settlement and payment verification scenarios, such as complex logic, numerous abnormal scenarios, and strict data verification. It ensures that the output code adapts to the actual operational needs of the e-commerce platform, guaranteeing the smoothness, security, and accuracy of the order settlement and payment process. Figure 2 As shown, the specific implementation process is as follows: The causal analysis module receives natural language-based order settlement and payment verification development requirements submitted by e-commerce platform operations and technical personnel. It performs layer-by-layer semantic analysis and comprehensive logical analysis of these requirements, breaking down complete business descriptions and extracting key semantic entities. It distinguishes between the implementation content of order settlement functions and payment operation restrictions, analyzes the data transmission flow and interaction forms between different business links such as order data, payment data, and user data, and divides execution branch logic into normal settlement, conditional settlement, process jump, and multi-order parallel execution. It clarifies abnormal constraints for special scenarios such as payment timeouts, amount exceeding limits, data verification failures, and third-party payment call anomalies. It deeply filters the implicit logical connections and implicit constraints in the requirement text, unifying, classifying, integrating, and standardizing the scattered e-commerce transaction requirement information. It completes the classification and orderly arrangement of all requirement elements, ultimately outputting structured requirement data with a unified format, complete logic, and comprehensive elements. This completely solves the problems of vague natural language requirement expressions, scattered elements, and difficulty in extracting implicit constraints, transforming e-commerce business requirements into standardized logical data. This provides accurate input support for subsequent topology modeling, ensuring that the development logic always aligns with the core business rules of e-commerce order settlement and payment verification.

[0030] The topology modeling module receives structured requirement data output from the causal analysis module. Relying on the causal coupling mining algorithm, it quantifies the correlation between code nodes and business logic nodes in order settlement and payment verification scenarios. Following system rules, it completes node definition, association labeling, and graph generation. Units such as order settlement constraints, payment data flow, settlement branch logic, and payment anomaly restrictions in the structured requirement data are designated as business logic nodes, while code components such as program variables, function units, conditional branches, and loop structures are designated as code nodes. Unique identifiers and corresponding attribute fields are configured for both types of nodes. The global causal coupling degree output by the causal coupling mining algorithm is used as the edge weight to label the correlation between nodes. The system categorizes edge attributes into three types based on their association type: direct association, indirect transmission association, and conditional trigger association. Node positions are arranged according to the order data flow direction and payment logic execution sequence to complete the topological connection between nodes and edges, forming a complete association structure. Simultaneously, edge weight data, node identifiers, and association attributes are extracted from the graph, and coupling feature parameters are generated. This transforms the complex business logic of order settlement and payment into a clear topological association structure, accurately presenting the causal relationship strength and transmission path between code and business, eliminating the information gap between business logic and code implementation, providing quantitative logical references for intervention and control modules, and ensuring that subsequent code generation can accurately match the business logic of e-commerce transactions.

[0031] The intervention and regulation module receives the causal topology map and coupling feature parameters output by the topology modeling module, and implements multi-dimensional variable intervention based on the causal bias correction algorithm to correct the bias in the large model's order settlement and payment logic. The mathematical expression of the causal bias correction algorithm is as follows: ; in, This represents the code causal intervention correction amount, used to support the formulation of standardized causal intervention rules; n is the total number of logical nodes; The business priority weight for the k-th logical node is preset to a fixed value of 0.5 for core business nodes, 0.3 for key data nodes, and 0.2 for auxiliary logical nodes. The superscript T represents the preset standard causal coupling threshold, which is uniformly set to 0.7; is the global causal coupling degree between code node i and business logic node j, and is the global causal coupling degree calculation result output by the causal coupling mining algorithm; This is a correction factor for logical defects. ,in The number of errors that occurred in the historical verification of the kth logical node (initially 0) is given by this formula. The defect correction coefficient increases adaptively with the error frequency and takes a value in the range of [1,2). The value is assigned according to the type of code exception. The value is 1.2 when there is a false association between the node and the error, 1.0 when there is a logical deviation, and 0.8 when there is no exception. The variable intervention intensity coefficient is dynamically configured based on the coupling feature parameters, with a value range of 0-1. Standardized causal intervention rules adapted to e-commerce scenarios are constructed based on the graph structure and coupling feature parameters. Variable isolation rules sever unfounded false correlation links between code nodes, preventing erroneous correlation fitting in the large model. Logical constraint rules limit the sequence and operational logic of order settlement, payment verification, and data interaction. Association filtering rules filter effective causal dependencies and eliminate weak correlation interference items based on the causal coupling degree. Boundary constraint rules determine code syntax specifications, exception handling scope, and data transmission limitations. This constrains probabilistic erroneous fitting logic and outputs the initial generated code for the order settlement and payment verification modules. This ensures that the initial code meets the high accuracy and stability requirements of e-commerce transactions from the generation stage, reducing logical deviations and correlation errors, lowering the defect rate of the initial code, and ensuring that the initial code can adapt to the core business processes of order settlement and payment.

[0032] The verification and optimization module receives the initial generated code output by the intervention and control module. Based on counterfactual reasoning, it fully covers the entire logical path of the order settlement and payment verification modules. It uses a causal topology graph to analyze the overall code architecture, breaking down the main order settlement process, payment verification branch process, third-party interface call process, and data interaction process based on the causal coupling relationships of code nodes. Through counterfactual hypothesis deduction, it reverse-engineers and substitutes conditions for key logical nodes, completely traversing all logical paths and covering various scenarios such as regular settlement, critical amounts, and abnormal payments. It combines path association characteristics to identify hidden logical breakpoints and dependency deviations, automatically generating boundary and abnormal test cases adapted to e-commerce scenarios. Boundary test cases cover extreme conditions such as extreme order amounts, empty order data, payment load limits, and settlement process switching critical points. Abnormal test cases include... For unconventional scenarios such as exceeding limits, data type errors, payment interface call conflicts, instruction execution interruptions, resource access anomalies, and settlement logic contradictions, all test cases are categorized and arranged based on causal coupling relationships to adapt to the entire execution chain. By running test cases in batches and comparing the feedback data in real time, logical defects such as variable call errors, branch judgment conflicts, data flow breaks, and causal matching imbalances are quickly identified. Defect nodes and causes are marked, and the code is adjusted in a targeted manner based on causal deviation correction benchmark parameters. The execution order, judgment rules, and data interaction logic are optimized. After iterative optimization through repeated review, all logical vulnerabilities are eliminated, and optimized code for the order settlement and payment verification module with closed-loop logic, stable operation, and high compliance is output. This code can cope with the high concurrency and multiple anomaly operating environment of e-commerce, ensuring accurate order settlement, error-free payment verification, and smooth process operation.

[0033] The source tracing and explanation module receives highly compliant optimized code and full-chain operation data output by the verification and optimization module. It traces back the causal path of code generation, fully connecting each stage of topology modeling, coupling parameter matching, causal deviation intervention, counterfactual reasoning verification, testing and verification, and code correction. It records the triggering factors, execution correlations, and operational feedback for each algorithm adjustment, logic correction, and code modification, clearly connecting the causal relationships of data flow, decision transmission, and deviation correction in each module. It completely preserves the entire process trajectory of code from initial generation to optimization and finalization, covering explicit logic and implicit triggers. It integrates code content, operation data, and backtracking results to compile a visual explanation report, presenting the causal topology relationship of the entire process in a graphical form. It marks the decision basis for algorithm selection and structural design, includes all records of defect rectification, intervention and control, and optimization adjustments, and classifies and sorts out core parameters, rule applications, and correction details. It breaks down the technical decision-making logic in a graphical and textual form, completely restoring the code iteration process. It provides a complete reference for code review, technical traceability, and subsequent iteration upgrades of the e-commerce platform's order module, making the development process traceable, verifiable, and optimizable, and helping the e-commerce platform continuously upgrade the functions of the transaction system.

[0034] This embodiment applies the system of the present invention to the development of an order settlement and payment verification module for an e-commerce platform. Through intelligent assistance throughout the entire process, it completes the transformation from requirements to code, accurately adapts to the business characteristics and operational requirements of e-commerce transaction scenarios, and effectively solves pain points in traditional development such as inaccurate requirement analysis, vague logic modeling, numerous code defects, insufficient test coverage, and difficulty in traceability. It improves development efficiency, code stability, and accuracy, ensures a smooth and secure order settlement and payment process, and provides complete data support for subsequent system maintenance and upgrades, demonstrating the system's core technological advantages in high-requirement software development scenarios such as e-commerce.

[0035] 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. An intelligent software development assistance system, characterized in that, The system includes: Causal analysis module: Used to receive natural language development requirements, complete semantic decomposition and logical sorting, extract business constraints, data flow relationships, branch logic and exception restrictions, and organize and output structured requirement data; Topology modeling module: Receives structured requirement data, quantifies the correlation between code nodes and business logic nodes based on causal coupling mining algorithms, constructs a code causal topology graph, and synchronously outputs coupling feature parameters; Intervention and regulation module: Based on causal topology map and coupling feature parameters, combined with causal bias correction algorithm, implement multi-dimensional variable intervention, correct the correlation bias of large model, generate standardized causal intervention rules, constrain probabilistic error fitting logic and output initial generation code; Verification and optimization module: Receives the initial generated code, covers the entire logical path based on counterfactual reasoning, automatically generates boundary and exception test cases, locates logical defects and iteratively corrects them, and outputs highly compliant optimized code; Source tracing and explanation module: Receives highly compliant optimized code and full-chain operation data, traces back the code to generate a full-chain causal path, provides the decision basis for source tracing algorithm selection and structural design, and generates a visual explanation report.

2. The intelligent software development assistance system according to claim 1, characterized in that, In the causal analysis module, the semantic decomposition and logical sorting are specifically as follows: the acquired natural language development requirements are semantically analyzed layer by layer. First, the complete business description statement is decomposed and key semantic entities are extracted. Then, the functional implementation content and the running restriction content are distinguished. The data transmission path and interaction form between different business links are sorted out. Divide the logic of regular execution branches into special scenario exception constraints; filter the implicit logical connections and implicit constraints in the requirement text; unify, classify and integrate the scattered and fragmented original requirement information and standardize the format arrangement; complete the content classification and orderly arrangement; and organize it into structured requirement data with unified format, complete logic and complete elements.

3. The intelligent software development assistance system according to claim 1, characterized in that, In the causal analysis module, business constraints include functional implementation specifications, development and writing standards, operation permission restrictions, and business process restrictions; data flow relationships include data input sources, data output destinations, cross-unit data interaction forms, data transmission sequence, and the correspondence between data calls. Branching logic includes normal execution branches, conditional judgment branches, process jump branches, and parallel execution branches in multiple scenarios; Abnormal restrictions include abnormal input parameters, interrupted operation process, excessive boundary values, abnormal external environment adaptation, and special working condition restrictions.

4. The intelligent software development assistance system according to claim 1, characterized in that, In the topology modeling module, the mathematical expression for the causal coupling mining algorithm is: ； in, The global causal coupling degree between code node i and business logic node j; For direct causal weighting coefficients; For semantic matching of the requirements code; The indirect causal transmission coefficient; This is the business constraint fit coefficient; Let be the number of levels in the causal transmission path; e is the natural constant. This is the logic attenuation coefficient; This represents the logical deviation.

5. The intelligent software development assistance system according to claim 1, characterized in that, In the topology modeling module, the construction of the code causal topology graph relies on the quantified node association degree. It sequentially completes node definition, association labeling, and graph generation. The business logic units corresponding to the structured requirement data are defined as business logic nodes, and program variables, function units, conditional branches, and loop structures are defined as code nodes. Both types of nodes are configured with unique identifiers and corresponding attribute fields. The global causal coupling degree output by the causal coupling mining algorithm is used as the edge weight to assign values ​​to the association relationships between nodes. The edge attributes are divided into three types according to the association type: direct association, indirect transmission association, and conditional trigger association. The node positions are arranged according to the data flow direction and logic execution sequence to complete the topological connection between nodes and edges, forming a complete association structure.

6. The intelligent software development assistance system according to claim 1, characterized in that, In the intervention and regulation module, the mathematical expression of the causal bias correction algorithm is: ； in, , where n is the code causal intervention correction amount; n is the total number of logical nodes; The business priority weight of the kth logical node; The superscript T represents the preset standard causal coupling threshold; The global causal coupling degree between code node i and business logic node j; This is a correction factor for logical defects; The intervention intensity coefficient is a variable.

7. The intelligent software development assistance system according to claim 1, characterized in that, In the intervention and regulation module, the standardized causal intervention rules are constructed based on the causal topological graph structure and causal coupling feature parameters. The overall system includes four core components: variable isolation rules, logical constraint rules, association filtering rules, and boundary limitation rules. Variable isolation rules are used to cut off unfounded false association links between code nodes and avoid incorrect correlation fitting of large models. Logical constraint rules limit the timing and operation logic of conditional branches, loop execution, and data interaction. The association filtering rules filter valid causal dependencies based on the global causal coupling degree value and eliminate weak association interference items; the boundary constraint rules are used to determine code syntax standards, exception handling scope and data transmission restrictions.

8. The intelligent software development assistance system according to claim 1, characterized in that, In the verification optimization module, counterfactual reasoning covering the entire logical path specifically involves: relying on the constructed causal topology graph to sort out the overall code operation architecture; based on the causal coupling relationship of code nodes, completely dissecting the main execution flow, branch judgment flow, function call flow, and data flow interaction flow; and through the counterfactual hypothesis deduction mode, performing reverse deduction and condition substitution on various key logical nodes of the code, completely traversing all logical paths, comprehensively covering various application scenarios, and at the same time combining path association characteristics to identify hidden logical breakpoints and dependency deviations, covering the entire execution link of the code without omission.

9. The intelligent software development assistance system according to claim 1, characterized in that, In the verification and optimization module, locating and iteratively correcting logical defects specifically involves: running boundary and abnormal test cases in batches, comparing the code execution feedback data in real time with the causal topology association rules and business logic standards, quickly identifying implicit and explicit logical defects, marking the code node where the defect is located and the cause of the error, and combining the causal deviation correction benchmark parameters to make targeted adaptation adjustments for various defects, optimizing the code execution order, condition judgment rules and data interaction logic, and reusing test cases for verification after completing a single round of rectification, iterating and optimizing in a loop to gradually eliminate logical vulnerabilities.

10. The intelligent software development assistance system according to claim 1, characterized in that, In the source tracing and interpretation module, the entire causal path is fully connected, linking the construction of the causal topology map in the early stage, the matching of coupled feature parameters, the intervention and control of causal deviation, the full-path verification of counterfactual reasoning, the boundary anomaly testing and verification, and the code iteration and correction of each core process. It records the pre-triggering causes, intermediate execution coupling relationships and post-running feedback results of each algorithm control, logic correction and code modification action, clearly connecting the data flow relationship, decision transmission path and deviation correction causal relationship of each module, and completely retaining the causal evolution trajectory of the code from initial generation to optimization and finalization, without omitting explicit execution logic and implicit related causes.

Images