Software development scheme generation method and system

Abstract

The invention relates to the technical field of software development, and provides a method and a system for generating a software development scheme. The method comprises the following steps: S1, acquiring a user demand, extracting a functional demand and a non-functional demand, and processing; s2, generating a system architecture design based on a demand analysis result, and selecting a proper technical scheme in a manner of combining a collaborative filtering model and reinforcement learning; s3, based on the architecture design and the demand analysis result, a development task is automatically generated, and resource scheduling is dynamically adjusted; and S4, generating an automatic test based on a demand, and performing intelligent analysis on a test result. And dynamically generating system architecture design and optimizing technical stack selection through a method of combining a collaborative filtering model and reinforcement learning. Collaborative filtering is associated with implicit vectors of a technology stack through user requirements, reinforcement learning optimizes decisions according to historical experience, expandability of architecture design and reasonability of technology type selection are guaranteed, different project requirements are met, and system performance is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software development, and in particular to a method and system for generating a software development solution. Background Art

[0002] In the modern software development process, project requirements are usually dynamically changing, and as the project scale increases, the complexity and uncertainty of the requirements also increase. Traditional software development methods often rely on a large amount of manual requirements analysis, architecture design, and development process planning, which not only consumes a lot of manpower and material resources, but is also prone to problems such as analysis errors, missing requirements, or unreasonable technology selection, which in turn affects the development efficiency and quality of the project. At the same time, the development process often faces challenges such as unreasonable development task scheduling, insufficient test coverage, and complex deployment processes, which prolongs the development cycle and makes project maintenance and upgrades difficult.

[0003] Existing demand analysis tools often require manual intervention to classify and prioritize requirements, resulting in low efficiency and being easily affected by human subjective judgment, leading to misjudgment or omission of requirements. Traditional development task allocation and resource scheduling often lack intelligent support, resulting in low resource utilization efficiency and extended development cycle. In particular, when facing multiple tasks in parallel or changing requirements, resource scheduling strategies cannot be dynamically adjusted.

[0004] Therefore, a method and system for generating a software development solution are provided. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method and system for generating a software development plan, which solves the problems of inaccurate demand analysis, unreasonable technology selection, inflexible development task scheduling, and insufficient testing and deployment automation in the existing software development process.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for generating a software development plan, comprising the following steps;

[0007] S1. Obtain user needs, extract functional requirements and non-functional requirements, and process them;

[0008] S2. Generate system architecture design based on the results of demand analysis, and select appropriate technical solutions by combining collaborative filtering model with reinforcement learning;

[0009] S3: Automatically generate development tasks based on architecture design and demand analysis results, and dynamically adjust resource scheduling;

[0010] S4. Generate automated tests based on requirements and perform intelligent analysis on test results;

[0011] S5, automatically generate deployment scripts, realize automatic software deployment based on containerization technology, and optimize system operation by combining intelligent load balancing and self-healing mechanism;

[0012] S6. Collect project data for feedback and use adaptive learning mechanisms to continuously optimize the solution generation model.

[0013] Preferably, the S1 comprises the following steps:

[0014] S1.1. Obtain the natural language requirement document input by the user and preprocess it to segment the document, remove stop words and tag parts of speech;

[0015] S1.2. Use the deep learning pre-trained model to perform semantic analysis on the pre-processed natural language text;

[0016] S1.3, input the embedding vector of each requirement into the multi-task learning model to perform named entity recognition (NER), part-of-speech tagging (POS) and requirement classification;

[0017] S1.4. Prioritize based on demand characteristics, and use multi-objective optimization method for priority sorting;

[0018] S1.5, Requirements conflict detection: Construct a dependency graph G = (V, E) between requirements, where the node V represents the requirement and the edge E represents the logical relationship between requirements. Use similarity and difference detection functions to detect potential conflicts between requirements. The specific formula is:

[0019] C(u i ,u j )=sin(u i ,u j )-diff(f i ,f j )

[0020] When C(u i ,u j )>∈, it is determined that there is a conflict between the requirements, sin(u i ,u j ) represents the demand u i and u j The similarity, diff(f i ,f j ) represents the difference between their features, and ∈ is the set conflict detection threshold.

[0021] Preferably, the deep learning pre-training model in step S1.2 is a BERT model, which represents each requirement document as an embedding vector h i =BERT(d i ), where d iRepresents each sentence in the requirement document, h i is the corresponding embedding vector.

[0022] Preferably, the collaborative filtering model in step S3: preliminary technology stack recommendation is performed through the collaborative filtering model, and a scoring matrix R representing the demand u and the technology stack v is obtained based on a matrix decomposition algorithm. u ,v, using the latent vector P u and Q v Represents the potential relationship between user needs and technology stack;

[0023] The formula is:

[0024]

[0025] Among them, P u and Q v are the latent vectors of demand and technology stack respectively. The system predicts the correlation between demand and technology stack through matrix decomposition and generates preliminary technology stack recommendation results.

[0026] Preferably, the S4 comprises the following steps:

[0027] S4.1. Based on the results of requirements analysis and architecture design, the system automatically generates unit test, integration test and system test cases;

[0028] S4.2, after the test cases are generated, the system automatically executes the tests through the integrated continuous integration or continuous delivery (CI or CD) tools;

[0029] S4.3. Intelligently analyze test results through machine learning models to predict potential defects and generate optimization suggestions;

[0030] S4.4. For each code change, the system automatically analyzes the impact of the change on existing functions and intelligently generates necessary regression test cases.

[0031] Preferably, in step S4.3, the machine learning model is a classification model that analyzes the test failure case, and the model input is the test case feature x i And the corresponding test result y i , the loss function is;

[0032]

[0033] Among them, w is the weight of the classifier, C is the penalty parameter, and the system classifies and predicts the test results by optimizing the loss function L(w) to identify potential defects.

[0034] Preferably, the S6 comprises the following steps:

[0035] S6.1. Continuously collect relevant project data at all stages of software development;

[0036] S6.2. Adopt an adaptive learning mechanism to automatically optimize the parameters and decisions of the solution generation model through continuous analysis and feedback of project data;

[0037] S6.3, through the adaptive reinforcement learning model, dynamically adjust the parameters of each strategy module so that the system can automatically adapt to changes in different project scenarios;

[0038] S6.4. Continuously optimize each module of the generated solution based on real-time data of project progress.

[0039] Preferably, the relevant project data in step S6.1 includes demand changes, development progress and personnel allocation, test data and defect data.

[0040] Preferably, the formula of the adaptive reinforcement learning model in step S6.3 is:

[0041] State-action value function (Q value), that is, given the state s and action a, the long-term return Q(s,a) that the action can bring:

[0042] Q(s,a)=r+γV(s ′ )

[0043] r is the immediate reward brought by the current state s and action a; γ is a discount factor (usually between 0≤γ≤1), which is used to measure the importance of future rewards; V(s ′ ) is the new state s after executing action a ′ The value function of .

[0044] A system for generating a software development plan, comprising:

[0045] Demand analysis module; demand acquisition unit, demand preprocessing unit, semantic analysis demand embedding unit, multi-task learning classification unit, demand priority sorting unit, demand conflict detection unit;

[0046] Architecture design and technology selection module; demand conflict detection unit, architecture component selection and combination unit, technology stack selection and optimization unit, architecture performance simulation and verification unit, dynamic adjustment and feedback unit;

[0047] Development process generation module; task decomposition unit, personnel allocation and resource scheduling unit, progress tracking and monitoring unit, feedback mechanism and continuous optimization unit;

[0048] Testing and quality assurance module; test case generation unit, automated test execution unit, test result analysis unit, defect repair suggestion unit, test feedback optimization unit;

[0049] Automated deployment and operation and maintenance module; deployment plan generation unit, resource scheduling and load balancing unit, automated expansion and reduction unit, self-healing mechanism and intelligent alarm unit;

[0050] Continuous learning and optimization module; data collection and feedback unit, adaptive reinforcement learning unit, feedback data optimization unit.

[0051] Working principle: First, the user's natural language requirement document is obtained, and the document is converted into structured data through preprocessing steps. Then, the deep learning model is used to perform semantic analysis on the requirement document to generate an embedding vector. Subsequently, a multi-task learning model is used to perform named entity recognition, requirement classification, and requirement priority sorting. At the same time, the system detects potential conflicts between requirements through similarity and difference functions to ensure the accuracy and consistency of requirements. According to the results of the requirement analysis, the system generates a system architecture design and selects a suitable technology stack by combining a collaborative filtering model and reinforcement learning. The collaborative filtering model recommends a preliminary technology stack combination through matrix decomposition, and the reinforcement learning model dynamically adjusts the technology stack selection based on historical data to ensure that the best architecture solution is provided in a variety of scenarios. It automatically decomposes requirements and architecture design into development tasks, and determines the execution order of tasks by building a task dependency graph. The critical path method (CPM) is used to calculate the shortest duration of the project. Task allocation and resource scheduling are dynamically optimized based on the skills and task priorities of developers to ensure that the project is completed as planned, and corresponding test cases are generated according to requirements, including unit tests, integration tests, and system tests. After the test is executed, the system automatically tests through the integrated CI / CD tool. Through machine learning models, the system intelligently analyzes test results, predicts potential defects, and provides repair suggestions. At the same time, the system automatically generates regression test cases for code changes to ensure code stability and continuously collects project data at all stages of development, including demand changes, development progress, test results, etc. Through adaptive reinforcement learning models, the system can dynamically adjust the parameters and strategies of each module based on project data to ensure that the solution can automatically adapt to different project scenarios and be continuously optimized.

[0052] The present invention provides a method and system for generating a software development solution, which has the following beneficial effects:

[0053] 1. The present invention dynamically generates system architecture design and optimizes technology stack selection through a method combining collaborative filtering model and reinforcement learning. Collaborative filtering associates user needs with implicit vectors of technology stacks, and reinforcement learning optimizes decisions based on historical experience, ensuring the scalability of architecture design and the rationality of technology selection, adapting to different project requirements and improving system performance.

[0054] 2. The present invention introduces deep learning and multi-task learning models to automatically analyze, segment, name entity recognition (NER), part-of-speech tagging and classify demand documents, effectively improving demand processing efficiency. Similarity detection and difference functions are used to identify potential demand conflicts, ensure the rationality and consistency of requirements, reduce human errors, and reduce the impact of demand changes.

[0055] 3. Through the deep learning model and multi-task learning method, the system can automatically process natural language requirements and accurately classify, label and prioritize requirements. This not only reduces the need for manual intervention, but also ensures that there are no logical conflicts between requirements through the requirement conflict detection function, thereby improving the accuracy of requirement analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a flow chart of the method of the present invention;

[0057] Figure 2 It is a system framework diagram of the present invention;

[0058] Figure 3 It is a schematic diagram of the demand analysis module of the present invention;

[0059] Figure 4 It is a schematic diagram of the architecture design and technology selection module of the present invention;

[0060] Figure 5 Generate a module diagram for the development process of the present invention;

[0061] Figure 6 It is a schematic diagram of the testing and quality assurance module of the present invention;

[0062] Figure 7 This is a schematic diagram of the automated deployment and operation and maintenance module of the present invention;

[0063] Figure 8 It is a schematic diagram of the continuous learning and optimization module of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Example:

[0066] Please see attached Figure 1 , an embodiment of the present invention provides a method for generating a software development solution, comprising the following steps;

[0067] S1. Obtain user needs, extract functional requirements and non-functional requirements, and process them;

[0068] The S1 comprises the following steps:

[0069] S1.1. Obtain the natural language requirement document input by the user and preprocess it to segment the document, remove stop words and tag parts of speech;

[0070] S1.2. Use the deep learning pre-trained model to perform semantic analysis on the pre-processed natural language text;

[0071] S1.3, input the embedding vector of each requirement into the multi-task learning model to perform named entity recognition (NER), part-of-speech tagging (POS) and requirement classification;

[0072] S1.4. Prioritize based on demand characteristics, and use multi-objective optimization method for priority sorting;

[0073] S1.5, Requirements conflict detection: Construct a dependency graph G = (V, E) between requirements, where the node V represents the requirement and the edge E represents the logical relationship between requirements. Use similarity and difference detection functions to detect potential conflicts between requirements. The specific formula is:

[0074] C(u i ,u j )=sin(u i ,u j )-diff(f i ,f j

[0075] When C(u i ,u j )>∈, it is determined that there is a conflict between the requirements, sin(u i ,u j ) represents the demand u i and u j The similarity, diff(f i ,f j ) represents the difference between its features, ∈ is the set conflict detection threshold;

[0076] The deep learning pre-training model in step S1.2 is a BERT model, which represents each requirement document as an embedding vector h i =BERT(d i ), where d i Represents each sentence in the requirement document, h i is the corresponding embedding vector;

[0077] Specifically, the system obtains requirements through natural language requirement documents input by users. The documents may include requirement specifications, emails, meeting minutes, etc. The obtained natural language requirement data may contain redundant information, so it needs to be preprocessed. The requirement documents are preprocessed through steps such as word segmentation, stop word removal, and part-of-speech tagging. This process converts the unstructured data in the document into structured data for subsequent analysis. The processing includes the following operations: word segmentation: splitting the continuous characters in the document into meaningful phrases; stop word removal: filtering out common words that have no practical meaning; part-of-speech tagging: tagging each word with a part-of-speech tag to mark its grammatical function in the sentence, and using a multi-task learning model to classify and analyze the requirements. The multi-task model simultaneously performs named entity recognition (NER), part-of-speech tagging (POS) and requirement classification. Through this step, the system can automatically identify the key entities in the requirements and classify them into two categories: functional requirements and non-functional requirements. By extracting features from the requirements, the system uses a multi-objective optimization algorithm to prioritize the requirements based on factors such as business value and technical complexity. The multi-objective optimization algorithm calculates the priority of requirements through the following objective function. There may be logical conflicts between requirements for requirement conflict detection, especially in complex systems. By constructing the requirement dependency graph G = (V, E), the similarity and difference detection functions are used to identify potential conflicts between requirements. The similarity calculation formula between requirements is:

[0078] C(u i u j )=sin(u i ,u j )-diff(f i ,f j )

[0079] Among them, sin(u i ,u j ) is the demand u i and demand j The similarity between them, diff(f i ,f j ) represents the difference between demand characteristics. If C(u i ,u j )>∈, then it is determined that there is a conflict between the requirements;

[0080] S2. Generate system architecture design based on the results of demand analysis, and select appropriate technical solutions by combining collaborative filtering model with reinforcement learning;

[0081] The architecture design system first generates an architecture design plan based on the results of the requirements analysis. The requirements are modeled as a requirements graph G = (V, E), where the node V represents the requirements and the edge E represents the dependency between the requirements. The system processes the requirements graph through a graph neural network (GNN) to generate a component design for the system architecture. GNN updates the node representation of each layer using the following formula:

[0082]

[0083] in, is the node representation of the kth layer, is the set of neighbor nodes of node i, d i is the node degree, W (k) is the model weight matrix. Through multi-layer graph convolution operations, the system generates an embedded representation of each requirement, and then generates an architecture design plan. The technology stack selection is based on the generated architecture design. The system recommends a preliminary technology stack combination through the collaborative filtering model. The collaborative filtering model generates a technology stack rating matrix R through matrix decomposition technology u,v Where u represents demand and v represents technology stack. The matrix decomposition formula is:

[0084]

[0085] Among them, P u and Q v are the potential feature vectors of demand and technology stack, respectively.

[0086] Reinforcement learning further optimizes the technology stack selection. The system selects the technology stack through the state-action value function (Q value).

[0087] Select to optimize, the formula is:

[0088]

[0089] Among them, R(s,a) is the immediate reward after selecting the technology stack, and γ is the discount factor;

[0090] S3: Automatically generate development tasks based on architecture design and demand analysis results, and dynamically adjust resource scheduling;

[0091] The collaborative filtering model in step S3: Preliminary technology stack recommendations are made through the collaborative filtering model. Based on the matrix decomposition algorithm, the rating matrix R representing the demand u and the technology stack v is u ,v, using the latent vector P u and Q v Represents the potential relationship between user needs and technology stack;

[0092] The formula is:

[0093]

[0094] Among them, P u and Q v are the latent vectors of demand and technology stack respectively. The system predicts the correlation between demand and technology stack through matrix decomposition and generates preliminary technology stack recommendation results.

[0095] Specifically, according to the required functional modules, the system divides the project into several development tasks and generates a task dependency graph. Each node in the task dependency graph represents a development task, and the edge represents the dependency relationship between tasks. The shortest duration of the project is calculated by the critical path method (CPM), and the formula is:

[0096] T critical =max(L1,L2,…,L k )

[0097] The system automatically allocates tasks and schedules resources based on task priorities and developer skills;

[0098] S4, generating automated tests based on requirements, and performing intelligent analysis on the test results; S4 includes the following steps;

[0099] S4.1. Based on the results of requirements analysis and architecture design, the system automatically generates unit test, integration test and system test cases;

[0100] S4.2, after the test cases are generated, the system automatically executes the tests through the integrated continuous integration or continuous delivery (CI or CD) tools;

[0101] S4.3. Intelligently analyze test results through machine learning models to predict potential defects and generate optimization suggestions;

[0102] S4.4. For each code change, the system automatically analyzes the impact of the change on existing functions and intelligently generates necessary regression test cases;

[0103] In step S4.3, the machine learning model is a classification model that analyzes the test failure cases, and the model input is the test case feature x i And the corresponding test result y i , the loss function is;

[0104]

[0105] Among them, w is the weight of the classifier, C is the penalty parameter, and the system classifies and predicts the test results by optimizing the loss function L(w) to identify potential defects;

[0106] Specifically, unit test, integration test and system test cases are generated according to requirements and architecture design. The test cases are linked to the requirements through mapping algorithms to ensure that all functional requirements have corresponding test coverage. Test cases are automatically executed through integrated CI or CD tools to ensure that test cases can be executed in time every time the code is submitted. The test results are intelligently analyzed through machine learning models to identify potential defects. The system uses a classification model to analyze test failure cases, and the loss function of the classifier is;

[0107] S5, automatically generate deployment scripts, realize automatic software deployment based on containerization technology, and optimize system operation by combining intelligent load balancing and self-healing mechanism;

[0108] Specifically, based on containerization technologies such as Docker or Kubernetes, deployment scripts are automatically generated to achieve automated deployment. The system supports multi-environment deployment to ensure that deployment scripts adapt to different environments. By monitoring system performance in real time, expansion and contraction operations are automatically performed, and genetic algorithms are used to optimize resource scheduling. The objective function is;

[0109]

[0110] Among them, C i To calculate the cost, P i The system automatically identifies system anomalies and executes self-healing mechanisms through intelligent monitoring modules;

[0111] S6. Collect project data for feedback and use adaptive learning mechanisms to continuously optimize the solution generation model.

[0112] Specifically, the S6 includes the following steps:

[0113] S6.1. Continuously collect relevant project data at all stages of software development;

[0114] S6.2. Adopt an adaptive learning mechanism to automatically optimize the parameters and decisions of the solution generation model through continuous analysis and feedback of project data;

[0115] S6.3, through the adaptive reinforcement learning model, dynamically adjust the parameters of each strategy module so that the system can automatically adapt to changes in different project scenarios;

[0116] S6.4. Continuously optimize each module of the generated solution based on real-time data of project progress;

[0117] The relevant project data in step S6.1 include demand changes, development progress and personnel allocation, test data and defect data;

[0118] The formula of the adaptive reinforcement learning model in step S6.3 is:

[0119] State-action value function (Q value), that is, given the state s and action a, the long-term return Q(s,a) that the action can bring:

[0120] Q(s,a)=r+γV(s′)

[0121] r is the immediate reward brought by the current state s and action a; γ is a discount factor (usually between 0≤γ≤1), which is used to measure the importance of future rewards; V(s′) is the new state s after executing action a ′ The value function of

[0122] Specifically, at each stage of software development, the system will continuously collect multi-dimensional data related to the project to provide a basis for subsequent optimization and adjustment. The data includes demand changes: recording changes in demand during the development process, including demand addition, deletion, modification, etc., to ensure that the impact of demand changes is promptly fed back to the development process and architecture design, development progress and personnel allocation: continuously monitoring the progress of each development task, tracking the task allocation and actual workload of developers, and adjusting the development plan to maximize development efficiency, test data: including indicators such as the execution results, pass rate, test coverage, and test execution time of automated tests, and defect data: recording the number, severity, and repair time of defects found in the system during development, testing, and operation and maintenance. These data help evaluate the quality and stability of the system, and the collected project data is continuously analyzed and fed back through an adaptive learning mechanism. This mechanism automatically optimizes the parameters of the solution generation model to ensure that the system can make the best decisions at different stages. The specific optimization process is as follows: an adaptive reinforcement learning model is used to dynamically adjust the parameters of each strategy module so that the system can automatically adapt to changes in different project scenarios. According to the real-time data of project progress, the system continuously optimizes the various modules of the generated solution, and adjusts the task allocation and resource utilization of developers according to the development progress and real-time task load to ensure efficient use of development resources. Task priorities and scheduling strategies are dynamically adjusted according to the execution of development tasks to ensure that key tasks are executed first and reduce the occurrence of project bottlenecks. Through real-time analysis of test results, the system dynamically generates necessary regression test cases to ensure that the introduction of new code will not affect the stability of existing functions. Potential risks in the project are predicted based on real-time data, and measures are taken in advance, such as increasing resources and adjusting task priorities, to avoid project delays or quality issues.

[0123] Please see attached Figure 1 - Attachment Figure 8 ,A system for generating software development solutions, including;

[0124] Requirements analysis module;

[0125] Requirements acquisition unit;

[0126] Demand pre-processing unit;

[0127] Semantic analysis requires embedding unit;

[0128] Multi-task learning classification unit;

[0129] Demand prioritization unit;

[0130] Requirements conflict detection unit;

[0131] Specifically, natural language requirements are obtained from the requirements documents provided by users or other input channels. These requirements are usually unstructured texts, covering functional requirements and non-functional requirements, and can automatically receive and identify different types of requirements input. The obtained requirements documents are first processed by the preprocessing unit, including word segmentation, stop word removal, part-of-speech tagging and other operations. The word segmentation algorithm splits the text into words, removes unnecessary words, and marks the key roles, operations and objects in the requirements. The preprocessed text is input into the deep learning model, and the embedding vector of the requirements is generated through natural language processing technology to represent the semantic information of each sentence. The BERT model can understand the semantics in the context and generate a vector representation with rich semantic information. i =BERT(d i ), where d i is a sentence in the requirement document, h i The corresponding embedding vector is input into the multi-task learning model. The system simultaneously completes named entity recognition (NER), part-of-speech tagging (POS) and demand classification tasks, and prioritizes the requirements according to their business value and implementation complexity. The priority is calculated through a multi-objective optimization model, and the formula is:

[0132] P(u i )=λ1V(u i )-λ2C(u i )

[0133] Where P(u i ) is the demand u i The priority of V(u i ) represents the business value of the demand, C(u i ) is true

[0134] The technical complexity of the current demand, λ1 and λ2 are the corresponding weight coefficients;

[0135] Conflict detection is performed through the demand dependency graph G = (V, E). Node V represents the demand, and edge E represents the dependency relationship between the demands. The formula of the conflict detection function is:

[0136] c(u i ,u j )=sin(u i ,u j )-diff(f i ,f j )

[0137] Among them, sim(u i ,u j ) is the demand u i and demand j The similarity, diff(f i ,f j ) is its characteristic difference. When the value exceeds the set threshold, the system determines that there is a conflict between the requirements.

[0138] Architecture design and technology selection module;

[0139] Requirements conflict detection unit;

[0140] Architecture component selection and combination unit;

[0141] Technology stack selection and optimization unit;

[0142] Architecture performance simulation and verification unit;

[0143] Dynamic adjustment and feedback unit;

[0144] Specifically, based on the results of the demand analysis, the system selects different components in the architecture design and combines them to form a complete system architecture. The architecture design process is modeled through a graph neural network (GNN), using a graph convolutional layer to update the representation of the demand nodes and generate a modular architecture design for the system. The formula is:

[0145]

[0146] Among them, W (k) is the weight matrix, is the neighbor set of node i;

[0147] The technology stack selection is optimized through collaborative filtering model combined with reinforcement learning. First, the matching degree between demand and technology stack is predicted by matrix decomposition algorithm. The formula is:

[0148]

[0149] Where P u and Q v The latent vectors represent the demand and technology stack respectively. Afterwards, the system dynamically adjusts the technology stack selection through reinforcement learning and uses the state-action value function (Q value) to optimize the technology selection process.

[0150] The new formula is:

[0151]

[0152] Among them, α is the learning rate and γ is the discount factor is the next state after action a;

[0153] After the architecture design is completed, the performance of the architecture is simulated through simulation tools to evaluate its performance under different load conditions. The simulation process includes the evaluation of the system's response time, resource utilization, and scalability to ensure that the designed architecture can meet the needs; the architecture design and technology stack selection are dynamically adjusted based on the real-time feedback of the project. Through the adaptive learning mechanism, the system can automatically adjust and optimize the architecture based on the problems and performance bottlenecks found during the development process.

[0154] Develop process generation module;

[0155] Task decomposition unit;

[0156] Staffing and resource scheduling unit;

[0157] Progress tracking and monitoring unit;

[0158] Feedback mechanism and continuous optimization unit;

[0159] Specifically, the architecture and requirements are automatically decomposed into specific development tasks, and a task dependency graph G = (N, E) G = (N, E) G = (N, E) is constructed. Task dependencies are used to determine the priority and execution order of each task to ensure that tasks can be executed as planned; the system uses linear programming to schedule resources and optimize resource utilization based on task priority and resource availability. The optimization goal is to minimize development costs and resource usage, and the formula is:

[0160]

[0161] where c ij The cost of performing task j for developer i, x ij is the decision variable for whether to assign the task;

[0162] Track project progress in real time, monitor task completion, resource usage, and developer workload, and provide visual progress reports. During the development process, the system collects task execution data through a feedback mechanism and automatically adjusts task allocation and development plans. Through an adaptive learning mechanism, the system can continuously optimize the development process, ensure that development tasks are completed on time, and reduce resource waste.

[0163] Testing and quality assurance module;

[0164] Test case generation unit;

[0165] Automated test execution unit;

[0166] Test result analysis unit;

[0167] Defect Fix Suggestion Unit;

[0168] Test feedback optimization unit;

[0169] Specifically, different types of test cases are generated according to requirements, including unit testing, integration testing, and system testing. The generated test cases cover all functional and non-functional requirements to ensure that each requirement is verified. The integrated continuous integration or continuous delivery (CI or CD) tool automatically executes the generated test cases to ensure that each code submission is verified by the complete test process. After the test is executed, the system uses a machine learning model to intelligently analyze the test results to identify failed cases and potential defects. The classification model is optimized through the loss function:

[0170]

[0171] Classify and predict test results to ensure that defects can be repaired quickly;

[0172] Defect repair suggestions are automatically generated based on test results to prompt developers of possible code errors and optimization directions, shortening repair time; by analyzing test feedback, the test case generation rules are dynamically optimized to ensure that future test plans can cover all potential problems and improve test efficiency.

[0173] Automatically deploy operation and maintenance modules;

[0174] A deployment plan generation unit;

[0175] Resource scheduling and load balancing unit;

[0176] Automated expansion and contraction unit;

[0177] Self-healing mechanism and intelligent alarm unit;

[0178] Specifically, containerized deployment scripts are automatically generated based on the architecture design to support multi-environment deployment, ensuring that the system can be quickly launched; the system dynamically adjusts resource allocation and usage through an intelligent load balancing algorithm to ensure high availability and stability of the system. The resource optimization formula is:

[0179]

[0180] The system ensures efficient use of resources through scheduling algorithms;

[0181] Automatically expand or reduce resources according to traffic load conditions to ensure that the system can automatically expand service nodes during traffic peaks to avoid service downtime; monitor the running status of services through AIOps and detect anomalies in real time. The self-healing mechanism is implemented through the reinforcement learning model. After a failure occurs, the system can automatically recover and perform service restart or expansion operations. The alarm mechanism notifies the operation and maintenance personnel when a problem occurs to ensure that the problem can be solved in a timely manner.

[0182] Continuous learning and optimization module;

[0183] Data collection and feedback unit;

[0184] Adaptive reinforcement learning unit;

[0185] Feedback data optimization unit;

[0186] Specifically, project-related data is collected at various stages of software development, such as demand changes, development progress, test results, and system performance. These data are used for model optimization and solution adjustment. Through the adaptive reinforcement learning model, the parameters of each module are dynamically adjusted so that the system can automatically adapt to changes in different project scenarios. The Q-value function in reinforcement learning is used to optimize long-term returns;

[0187]

[0188] Through iterative updates, the system gradually optimizes the strategies of each module;

[0189] Continuously optimize model parameters based on real-time feedback data from the project to ensure that the solution generation model can adapt to dynamic changes in the project and achieve continuous optimization and adjustment.

[0190] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for generating a software development plan, characterized in that: The steps include: S1. Obtain user needs, extract functional requirements and non-functional requirements, and process them; S2. Generate system architecture design based on the results of demand analysis, and select appropriate technical solutions by combining collaborative filtering model with reinforcement learning; S3: Automatically generate development tasks based on architecture design and demand analysis results, and dynamically adjust resource scheduling; S4. Generate automated tests based on requirements and perform intelligent analysis on test results; S5, automatically generate deployment scripts, realize automatic software deployment based on containerization technology, and optimize system operation by combining intelligent load balancing and self-healing mechanism; S6. Collect project data for feedback and use adaptive learning mechanisms to continuously optimize the solution generation model.

2. A method for generating a software development plan according to claim 1, characterized in that: The S1 comprises the following steps: S1.

1. Obtain the natural language requirement document input by the user and preprocess it to segment the document, remove stop words and tag parts of speech; S1.

2. Use the deep learning pre-trained model to perform semantic analysis on the pre-processed natural language text; S1.3, input the embedding vector of each requirement into the multi-task learning model to perform named entity recognition (NER), part-of-speech tagging (POS) and requirement classification; S1.

4. Prioritize based on demand characteristics, and use multi-objective optimization method for priority sorting; S1.5, Requirements conflict detection: Construct a dependency graph G = (V, E) between requirements, where the node V represents the requirement and the edge E represents the logical relationship between requirements. Use similarity and difference detection functions to detect potential conflicts between requirements. The specific formula is: C(u i u j )=sin(u i ,u j )-diff(f i ,f j ) When C(u i ,u j )>∈, it is determined that there is a conflict between the requirements, sin(u i ,u j ) represents the demand u i and u j The similarity, diff(f i ,f j ) represents the difference between their features, and ∈ is the set conflict detection threshold.

3. The method for generating a software development plan according to claim 1, characterized in that: The deep learning pre-training model in step S1.2 is a BERT model, which represents each requirement document as an embedding vector h i =BERT(d i ), where d i Represents each sentence in the requirement document, h i is the corresponding embedding vector.

4. The method for generating a software development plan according to claim 1, characterized in that: The collaborative filtering model in step S3: Preliminary technology stack recommendations are made through the collaborative filtering model. Based on the matrix decomposition algorithm, the rating matrix R representing the demand u and the technology stack v is u ,v, using the latent vector P u and Q v Represents the potential relationship between user needs and technology stack; The formula is: Among them, P u and Q v are the latent vectors of demand and technology stack respectively. The system predicts the correlation between demand and technology stack through matrix decomposition and generates preliminary technology stack recommendation results.

5. The method for generating a software development plan according to claim 1, characterized in that: The S4 comprises the following steps: S4.

1. Based on the results of requirements analysis and architecture design, the system automatically generates unit test, integration test and system test cases; S4.2, after the test cases are generated, the system automatically executes the tests through the integrated continuous integration or continuous delivery (CI or CD) tools; S4.

3. Intelligently analyze test results through machine learning models to predict potential defects and generate optimization suggestions; S4.

4. For each code change, the system automatically analyzes the impact of the change on existing functions and intelligently generates necessary regression test cases.

6. A method for generating a software development plan according to claim 1, characterized in that: In step S4.3, the machine learning model is a classification model that analyzes the test failure cases, and the model input is the test case feature x i And the corresponding test result y i , the loss function is; Among them, w is the weight of the classifier, C is the penalty parameter, and the system classifies and predicts the test results by optimizing the loss function L(w) to identify potential defects.

7. A method for generating a software development plan according to claim 1, characterized in that: The S6 comprises the following steps: S6.

1. Continuously collect relevant project data at all stages of software development; S6.

2. Adopt an adaptive learning mechanism to automatically optimize the parameters and decisions of the solution generation model through continuous analysis and feedback of project data; S6.3, through the adaptive reinforcement learning model, dynamically adjust the parameters of each strategy module so that the system can automatically adapt to changes in different project scenarios; S6.

4. Continuously optimize each module of the generated solution based on real-time data of project progress.

8. The method for generating a software development plan according to claim 1, characterized in that: The relevant project data in step S6.1 include demand changes, development progress and personnel allocation, test data and defect data.

9. The method for generating a software development plan according to claim 1, characterized in that: The formula of the adaptive reinforcement learning model in step S6.3 is: State-action value function (Q value), that is, given the state s and action a, the long-term return Q(s,a) that the action can bring: Q(sa)=r+γV(s′) r is the immediate reward brought by the current state s and action a; γ is a discount factor (usually between 0≤γ≤1), which is used to measure the importance of future rewards; V(s′) is the value function of the new state s′ entered after executing action a.

10. A system for generating a software development plan according to claim 1, or a method for generating a software development plan according to any one of claims 1 to 9, characterized in that: include; Demand analysis module; demand acquisition unit, demand preprocessing unit, semantic analysis demand embedding unit, multi-task learning classification unit, demand priority sorting unit, demand conflict detection unit; Architecture design and technology selection module; demand conflict detection unit, architecture component selection and combination unit, technology stack selection and optimization unit, architecture performance simulation and verification unit, dynamic adjustment and feedback unit; Develop process generation module; Task decomposition unit, personnel allocation and resource scheduling unit, progress tracking and monitoring unit, feedback mechanism and continuous optimization unit; Testing and quality assurance module; test case generation unit, automated test execution unit, test result analysis unit, defect repair suggestion unit, test feedback optimization unit; Automatically deploy operation and maintenance modules; Deployment plan generation unit, resource scheduling and load balancing unit, automatic expansion and reduction unit, self-healing mechanism and intelligent alarm unit; Continuous learning and optimization module; Data collection and feedback unit, adaptive reinforcement learning unit, feedback data optimization unit.

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