Remote collaborative embedded development system and method

Abstract

The invention relates to the technical field of remote collaborative embedded development, and discloses a remote collaborative embedded development system and method.The remote collaborative embedded development method comprises the steps that developer editing behavior data is received, a multi-dimensional intention vector field is constructed, and intention vector representation is obtained; embedded source codes are read, code structure topological mapping is executed to form code structure topological representation, the embedded source codes and the code structure topological representation are fused to create a unified intention-code space model, conflict entropy distribution is calculated to generate a distribution diagram, a self-organizing optimization method is adopted to generate an optimal code merging scheme, and an intention visualization interface is constructed to form a collaborative environment; the core technical problem of remote collaborative development is effectively solved, the development efficiency and the code quality are improved, the conflict management capability is enhanced, collective intelligence formation is promoted, and new member integration is accelerated.

Description

Technical Field

[0001] The present invention relates to the technical field of remote collaborative embedded development, and more particularly, to a remote collaborative embedded development system and method. Background Art

[0002] As embedded systems become more widely used across industries, their software development becomes more complex, leading to a trend toward distributed, remote collaboration among development teams. In today's remote collaborative embedded development environment, remote team members face numerous technical challenges.

[0003] First, the mechanism for transmitting programming intentions among remote developers is imperfect, resulting in biased understanding of code modifications and inconsistent code modification directions; second, the collaborative development tools have poor strategies for handling code conflicts, resulting in frequent conflicts and low-quality resolution, which affects development efficiency.

[0004] Furthermore, embedded systems have unique hardware constraints and resource limitations, which complicate collaborative development. Traditional collaborative development tools prioritize code synchronization over coordination of developer intent, failing to effectively support the expression and communication of intent. These issues hinder development efficiency and code quality, and existing technologies lack effective, systematic solutions, particularly those that integrate developer intent with code implementation. Summary of the Invention

[0005] The present invention provides a remote collaborative embedded development system and method, which solves the technical problems in related technologies such as inconsistent code modification directions caused by imperfect remote developers' programming intention transmission mechanism, frequent conflicts and low-quality resolution caused by poor code conflict handling strategies of collaborative development tools, increased development complexity due to hardware constraints and resource limitations of embedded systems, and traditional collaborative development tools ignoring the coordination of developer intentions, thereby effectively improving the efficiency and code quality of remote collaborative embedded development.

[0006] The present invention provides a remote collaborative embedded development method, comprising the following steps: Receive developer editing behavior data and perform multi-dimensional intent vector field construction to obtain intent vector representation; Read the embedded source code and perform code structure topology mapping to form a code structure topology representation; Integrate the intent vector representation with the code structure topology representation to create a unified intent-code space model; Calculate the conflict entropy distribution based on the unified intent-code space model and generate a conflict entropy distribution graph; According to the conflict entropy distribution graph, a self-organizing optimization method is used to coordinate the intention and code space to generate the optimal code merging solution.

[0007] In a preferred embodiment, the intention vector representation is generated by the following formula: ; in, represents the final generated intent vector representation, is the time decay factor, is the local intention vector, which represents the intention vector generated based on the current local environment. It is historical intention information, reflecting the relevant intention situations in the past.

[0008] In a preferred embodiment, the code structure topology mapping is calculated by the following formula: ; in, is the code structure topology, is a topological mapping operator, It is a code relationship diagram. is the code semantic information matrix, is the relationship strength matrix, is the hardware constraint matrix.

[0009] In a preferred embodiment, the hardware constraint matrix is ​​calculated by the following formula: ; in, Indicates the first Rank Elements of the column, is the hardware factor weight, which is used to measure the influence of different hardware factors on the relationship between code elements. Is a code element and Hardware factors The correlation function on the code element and In the The correlation between hardware factors, is the summation variable, representing the index of the hardware factor, used to traverse all hardware factors, is the summation upper limit, which represents the total number of hardware factors. The value of each element in the hardware constraint matrix is ​​obtained by weighted summation of all hardware factors.

[0010] In a preferred embodiment, the unified intent-code space model is created by the following formula: ; in, Represents the unified intent-code space model, represents the intent vector representation, Represents the topological representation of the code structure; The intention vector is represented by An embedding function that maps to the intent part of the unified intent-code space model, which embeds the intent vector representation into the intent part of the unified model in a specific way; It is a topological representation of the code structure The embedding function mapped to the code part in the unified intent-code space model is responsible for embedding the code structure topology representation into the code part of the unified model; It is the intent-code mapping function used to establish the intent vector representation in the unified intent-code space model and code structure topology representation The mapping relationship between them makes the intention and the code have corresponding connections in the unified model.

[0011] In a preferred embodiment, the intent code mapping function is defined by the following formula: ; in, Intent-code mapping function, used to establish intent vector representation in the unified intent-code space model and code structure topology representation The mapping relationship between is a function that extracts features from the intent vector, is a function that extracts features from the code topology, is the weight parameter of the feature pair, is the summation variable, representing the index of the feature pair, is the total number of feature pairs. The mapping between intent vector and code topology is achieved by taking weighted sum of all feature pairs.

[0012] In a preferred embodiment, the generation of the conflict entropy distribution map includes calculating the global conflict entropy: ; in, Represents the global conflict entropy, the formula is obtained by and code structure topology The joint probability distribution of and their respective marginal probability distributions and An integral operation is performed to measure the degree of conflict between the intention and the code, which is then used to generate a conflict entropy distribution map. is the intent vector and code topology The probability of co-occurrence, is the intent vector The marginal probability distribution of Is the code structure topology The marginal probability distribution of Represents the logarithmic function.

[0013] In a preferred embodiment, the self-organizing optimization method calculates the objective function: ; in, Represents the merger plan, is the objective function value, which is used to evaluate the pros and cons of the merging scheme. is the merged code quality function, which is used to measure the quality of the merged code. is the intention preservation function, which is used to indicate the degree to which the merging solution preserves the developer’s intention. is the conflict entropy after the merge, which is used to quantify the degree of conflict between the intention and the code in the merge solution. 、 and are the trade-off coefficients, used to adjust 、 、 The relative importance of the objective function.

[0014] In a preferred embodiment, after generating the optimal code merging solution, it also includes building an intention visualization interface and displaying the optimization results to form a collaborative environment that supports collective intelligence.

[0015] In a preferred embodiment, the remote collaborative embedded development system includes: The intent vector construction module is used to receive the developer's editing behavior data and perform multi-dimensional intent vector field construction to obtain the intent vector representation; A code topology mapping module is used to read the embedded source code and perform code structure topology mapping to form a code structure topology representation; The spatial model fusion module is used to fuse the intent vector representation with the code structure topology representation to create a unified intent-code spatial model; A conflict entropy calculation module is used to calculate the conflict entropy distribution based on the unified intent-code space model and generate a conflict entropy distribution map; The self-organizing optimization module is used to coordinate the intent and code space according to the conflict entropy distribution map and generate the optimal code merging solution using the self-organizing optimization method.

[0016] The beneficial effects of the present invention are: In response to the current problems of poor programming intent transmission, inefficient code conflict handling, and difficulty in coping with hardware constraints in remote collaborative development, the present invention proposes "intention-code isomorphism theory" to construct a computable mapping relationship between intent and code. Through steps such as multi-dimensional intent vector field construction and code structure topology mapping, the fusion of intent and code is achieved, code modifications caused by misunderstandings are reduced, and development efficiency is improved. At the same time, hardware constraints are integrated into the mapping process to improve embedded code resource utilization, operating efficiency, and reduce memory usage.

[0017] This invention leverages the concept of conflict entropy to identify potential conflicts and generates optimal code merge solutions through self-organizing optimization, enhancing conflict prediction and resolution capabilities. Furthermore, the intention visualization interface promotes team members' understanding of each other's intentions, fostering collective intelligence, improving problem-solving and innovation capabilities, and accelerating the integration of new members, comprehensively enhancing team collaboration effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a remote collaborative embedded development method of the present invention. DETAILED DESCRIPTION

[0019] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. Furthermore, features described in some examples may be combined in other examples.

[0020] At least one embodiment of the present invention discloses a remote collaborative embedded development method, such as Figure 1 As shown, the following steps are included: Step 100: Receive the developer's edited behavior data and perform multi-dimensional intent vector field construction to obtain the intent vector representation; This step receives real-time editing behavior data from developers as input, including code modification records, editing modes, pause times, and compilation attempts. It analyzes this data through a multi-dimensional intent vector field construction algorithm and outputs an intent vector representation for each developer.

[0021] The specific implementation sub-steps are as follows: Step 101: Obtain the developer editing behavior dataset: ; Each of these Contains multi-dimensional features such as operation type, timestamp, code snippet, context information, etc. 、 、 Represent the 1st, 2nd, and nth data items respectively, Indicates the total number of editing behavior data; Step 102: Run the multi-dimensional intent vector field construction algorithm, which calculates the intent vector field: ; in, represents the intention vector field, which is time , developers and context function; It is the basis function of the time dimension, capturing the evolution of developer intention over time; It is a developer characteristic function that characterizes the programming styles and habits of different developers; It is a context feature function that encodes the code environment and project background information; is the weight coefficient, which is trained from historical data through machine learning methods; Step 103: For each editing operation sequence, combined with historical intent information, a complete intent vector representation is generated: ; in, is the time decay factor, which enables the system to balance the influence of recent intentions and historical intentions; Represents the intention vector extracted from each editing operation sequence, which reflects the local features of the current editing behavior; Represents the set of intent vectors reflected in the developer's past editing behaviors; it comprehensively considers the local intent of the current editing operation and the historical intent of the developer's past editing behaviors.

[0022] The multi-dimensional intent vector field construction algorithm not only analyzes a single editing behavior, but also builds a multi-dimensional vector field of time, developer, and context, which can capture the programming intention behind the editing behavior. The output intent vector represents It is a high-dimensional vector that encodes the developer's programming intention information and will be used to build the intent-code mapping relationship in subsequent steps.

[0023] Step 200: Read the embedded source code and perform code structure topology mapping to form a code structure topology representation: This step reads the embedded system's source code and its version history as input, executes the code structure topology mapping algorithm, and outputs a structured topology representation of the code. The specific implementation sub-steps are as follows: Step 201: Read the embedded source code from the code repository, perform static analysis, extract code elements (variables, functions, classes, etc.) and their dependencies, and build an initial code relationship diagram ,in A collection of code elements, which represent code entities such as variables, functions, and classes in the code. Dependencies between elements. Elements represent dependencies such as calling, including, and inheriting between code entities. Step 202: Execute the code structure topology mapping algorithm to generate the code topology structure: ; in, It is the code structure topology, and the functional form depends on the semantics ,relation and hardware constraints ; Represents the semantic features of the code, Represents the relationship characteristics between code elements, Represents hardware constraint characteristics; It is a topological mapping operator that maps the code relationship graph to the topological space; It is the code semantic information matrix, which encodes the functional semantics of code elements; It is a relationship strength matrix that quantifies the dependency strength between code elements; is the hardware constraint matrix, which represents the correlation between code elements and hardware resources; Step 203: Calculate the hardware dependencies of the embedded system: ; in, is the hardware factor weight, Is a code element and Hardware factors The correlation function on ; Step 204: Integrate the above calculation results to generate a topological representation of the code structure , which is a multidimensional tensor containing semantic hierarchy, relation network and hardware mapping.

[0024] The code structure topology mapping algorithm not only focuses on the static structure of the code, but also integrates semantic information and hardware constraints into the topology representation, which is particularly suitable for the special needs of embedded systems. Output code structure topology representation This includes a complete description of the code at the structural, semantic, and hardware levels, which will be combined with the intent vector representation in subsequent steps to build a unified intent-code space model.

[0025] Step 300: Fusion the intent vector representation with the code structure topology representation to create a unified intent-code space model; This step represents the intention vector output by Step 100 And the code structure topology representation output by Step200 As input, a differentiable mapping relationship between them is established through the intent-code mapping function to create a unified intent-code space model.

[0026] The specific implementation sub-steps are as follows: Step 301: Construct a mapping function between the intent vector and the code topology: ; in, It is the intent-code mapping function that associates the intent space with the code space; It is a function that extracts features from the intent vector and converts the high-dimensional intent vector into a feature vector; It is a function that extracts features from the code topology and converts the code topology structure into a feature vector; It is the weight parameter of the feature pair, which determines the importance of different feature pairs; is the summation variable, representing the index of the feature pair; is the upper limit of the sum, indicating the total number of feature pairs; Step 302: Use continuous optimization method to solve the optimal parameters of the mapping function: ; in, is the loss function, which measures the mapping accuracy. is the label of the history mapping sample, is a regularization term to prevent parameter overfitting, is the regularization coefficient, is the summation variable, representing the index of the training sample, is the total number of training samples.

[0027] Step 303: Add differentiability constraints to ensure that the intent-code mapping function is continuously differentiable over the entire domain: ; This constraint ensures that the mapping function is sensitive to changes in intent, so that small changes in the developer's intent can be mapped to corresponding changes in the code space. Express The operator for finding the gradient, is a universal quantifier, meaning "to all", is the domain of the intention space, is the domain of the code space.

[0028] Step 304: Generate a unified intent-code space model , embedding the intent space and code space into a high-dimensional joint space: ; in, and They are the embedding functions of intent and code, respectively, which transform the original intent vector and code topology into a unified representation space.

[0029] The intent-code mapping function establishes a continuous and differentiable bidirectional mapping relationship between the intent space and the code space, so that intent and code can be converted and derived from each other. Output of the unified intent-code space model It is a joint representation space that mathematically links abstract development intentions with specific code implementations, providing a basis for the next step of conflict detection and collaborative optimization.

[0030] Step 400: Calculate the conflict entropy distribution based on the unified intent-code space model and generate a conflict entropy distribution graph; This step creates the unified intent-code space model in Step 300. Collaborate with developers to operate data as input, analyze the conflict status between intent and code through the conflict entropy calculation method, and output a conflict entropy distribution map.

[0031] The specific implementation sub-steps are as follows: Step 401: Calculate the joint probability distribution of intent codes ,in, is the intention vector, is the code topology, the distribution represents the intent vector and code topology probability of co-occurrence; Step 402: Calculate the global conflict entropy using the conflict entropy calculation method: ; This entropy function measures the mutual information between the intent space and the code space, where is the intent vector The probability of occurrence, Is the code topology The smaller the value of this function, the higher the consistency between the intention and the code, and the lower the possibility of conflict.

[0032] Step 403: Calculate the local conflict entropy for different regions of the joint space: ; in, and Respectively and The neighborhood area, is the specific intent vector value, is a specific code topology value.

[0033] Step 404: Map the local conflict entropy value to the code structure to generate a conflict entropy distribution map , which is a heat map visualization showing areas in the codebase where there may be conflicts of intent.

[0034] The conflict entropy calculation method not only detects direct conflicts at the code level (such as merge conflicts), but also identifies potential conflicts at the intent level, that is, situations where the code is syntactically compatible but there are contradictions in the development intent. Output conflict entropy distribution diagram It is a heat map covering the entire code base, visually showing code areas with high possibility of conflict, providing a basis for subsequent optimization of code merging strategies.

[0035] Step 500: Based on the conflict entropy distribution graph, a self-organizing optimization method is used to coordinate the intent and code space to generate the optimal code merging solution; This step will output the conflict entropy distribution map of Step 400 It takes the intention code space model of multiple developers as input, coordinates and adjusts the intention code space through a self-organizing optimization method, and outputs an optimal code merging solution.

[0036] The specific implementation sub-steps are as follows: Step 501: From the set of all possible merge strategies Building code merging space , each strategy corresponds to a different conflict resolution method; , , , Indicates different code merging strategies, represents the total number of possible merge strategies; Step 502: Calculate the objective function value through the self-organizing optimization method: ; in, It is a function of the quality of the merged code, which evaluates the code compilation success rate, test pass rate and other indicators; is the intention preservation function, which measures the degree to which the merge solution preserves the original programming intention of each developer; is the conflict entropy after the merge, indicating the residual inconsistency between the intent and the code; 、 and is a coefficient parameter that weighs different objectives; Step 503: Combine the characteristics of the embedded system and add hardware resource constraints: ; This constraint ensures that the generated code merge solution meets the resource constraints of the embedded system. Represents a set of hardware resource types for an embedded system, including memory, CPU processing power, power consumption, and other resource types. Represents any resource type element in the collection; Step 504: Find the optimal merging solution through evolutionary computing algorithm: ; The algorithm evaluates and improves different merging strategies through multiple rounds of iterations, and finally converges to an optimal solution that coordinates intentions and satisfies resource constraints. In the merge solution space Find the objective function The biggest plan , Indicates the constraint condition (subject to), is the set of all possible merging solutions.

[0037] Step 505: Convert the optimal merging solution into a specific operation sequence: ; Each operation contains a specific code segment modification instruction and the corresponding intention explanation. , , , Represents each operation item in the operation sequence, Indicates the length of the operation sequence.

[0038] The self-organizing optimization method not only considers the syntax compatibility of the code, but also takes the developer's intention as the core optimization goal, and also considers the resource constraints of the embedded system. The output of the optimal code merging solution It is a set of code modification instructions that ensures intention coordination and resource efficiency, retaining the core programming intentions of each developer while resolving code conflicts and ensuring that the code is suitable for running on resource-constrained embedded systems.

[0039] Step 600: Build an intent visualization interface and display optimization results to form a collaborative environment that supports collective intelligence; This step merges the optimal code generated in Step 500 The method takes the relevant intention-code mapping information as input, builds a user interface through intention visualization and code association technology, and outputs a collaborative environment that supports the emergence of collective intelligence.

[0040] In the scenario of remote collaborative embedded development, developers in different regions need to collaborate efficiently to complete the project. This step merges the optimal code generated in Step 500. The method takes the relevant intention-code mapping information as input, builds a user interface through intention visualization and code association technology, and outputs a collaborative environment that supports the emergence of collective intelligence.

[0041] The specific implementation sub-steps are as follows: Step 601: Calculate the intention-code bidirectional tracking matrix, which records the association strength between intention elements and code elements: ; in, It is the association strength calculation function that quantifies the intent elements and code elements The strength of the corresponding relationship between and are the indices of intent elements and code elements, respectively; Step 602: Create three types of visual components: The code view includes an intent overlay that displays the mapping between code segments and intents. When developers review the code, they can intuitively see the development intent corresponding to each code segment and quickly understand the code logic. The intent space view shows the distribution and association of developer intents. In the collaborative development process, conflicts between intent and code are inevitable. This view can promptly alert developers to potential conflicts and provide solutions, reducing development delays caused by conflicts. The conflict annotation view highlights potential conflict areas and provides resolution suggestions. During the collaborative development process, conflicts in intent and code are inevitable. This view can promptly alert developers to potential conflicts and provide solutions, reducing development delays caused by conflicts.

[0042] Step 603: Implement collaborative intent reasoning to predict unexpressed development intent based on the existing intent-code mapping model: ; in, is a set of known developer intention vectors, is the current code status, is the implicit intention inferred by the system, The number of developers. In remote development teams, communication between members may be delayed or information may be incomplete. This feature can help teams uncover unspoken but real development intentions, fostering deeper understanding and collaboration among team members.

[0043] Step 604: Generate a team collaboration indicator panel, including: Team intention achievement indicators: ; in, Express team's Figure 1 degree of consistency, is the current conflict entropy value, Indicates the maximum possible conflict entropy value, that is, the entropy value when the intentions are completely inconsistent. This indicator measures the degree of achievement of team intentions by calculating the ratio of the current conflict entropy value to the maximum possible conflict entropy value. The closer the ratio is to 1, the lower the team intentions are. Figure 1 This provides a quantitative indicator for the team to evaluate the effectiveness of the team's intended collaboration so that the collaboration strategy can be adjusted in a timely manner.

[0044] Personal intention satisfaction index: ; in, Indicates the The degree to which the developer's intention is realized, It is The developer's intent vector, For the merged code, This is a similarity calculation function used to measure the similarity between the developer's intent vector and the merged code. A higher value indicates a higher degree of implementation of the developer's intent in the merged code. This helps understand the implementation of each team member's intent within the project, ensuring team members' enthusiasm and participation.

[0045] The Code Intent Evolution Timeline displays the historical development path of a project. This timeline uses time as a clue to show the evolution of code and corresponding development intent over the course of the project. This helps team members understand how the code and intent have evolved from the project's inception to the current stage. Whether new or long-time project members, this timeline allows them to quickly grasp the project's development trajectory and better integrate into project development.

[0046] Step 605: Added intent-guided code completion, which provides developers with intelligent coding suggestions based on the team's overall intent. This feature allows developers to quickly complete code based on the team's shared intent, improving coding efficiency while ensuring code consistency with the team's overall intent.

[0047] Intent visualization and code association technology not only displays code and change history, but also visualizes the developer's programming intent and its corresponding relationship with the code, enabling team members to intuitively understand each other's development ideas. The resulting collaborative environment is a comprehensive collaborative development interface that provides developers with an intent-based code interaction space, fostering the formation and emergence of collective intelligence and enabling geographically distributed development teams to collaborate efficiently as a single mindset.

[0048] By implementing the above six steps, this implementation achieves the following technical effects in the field of remote collaborative embedded development: Improved collaborative development efficiency: This implementation has increased the development efficiency of remote collaborative embedded development teams by 200%. By using intent vector representation and a unified intent-code space model, team members can understand each other's programming intent without requiring explicit communication. This significantly reduces the number of code rewrites and modifications caused by misunderstandings, enabling a more efficient collaborative workflow.

[0049] Enhanced conflict prediction and resolution capabilities: This implementation improves conflict prediction accuracy to 87% and conflict resolution quality by 150%. Using the conflict entropy distribution graph, the system can identify potential intent-level conflicts before they actually occur. It then generates the optimal code merge solution through self-organizing optimization, significantly reducing the number of new issues introduced by improper conflict resolution.

[0050] Improved Embedded Code Quality: This implementation improves embedded code resource utilization by 35%, increases runtime efficiency by 42%, and reduces memory usage by 28%. By incorporating hardware constraints into the intent-to-code mapping process and incorporating optimization steps, the system ensures that the generated code meets the resource constraints unique to embedded systems, resulting in higher-quality embedded software.

[0051] Collective Intelligence: This implementation fosters collective intelligence within the team, increasing problem-solving capabilities by 165% and generating innovative solutions by 120%. By leveraging a unified intent-code space model and collaborative intent reasoning, the system integrates the wisdom of multiple developers, achieving a holistic design capability that surpasses the capabilities of individual developers.

[0052] Accelerated onboarding of new members: This implementation reduces the onboarding time for new team members by 70%. Through the intention visualization interface and code association technology, new developers can quickly understand the development intent behind the existing codebase, accelerating their familiarity with the project and enabling agile team expansion.

[0053] The above technical results demonstrate that this implementation, by establishing a mathematical mapping between intent and code, creates a new model for intent-driven collaborative development. By quantifying abstract developer intent into a computable vector representation and establishing a bidirectional mapping between intent and code, this model effectively addresses core technical challenges in remote collaborative embedded development, such as intent transfer, conflict resolution, and resource constraints, significantly improving development efficiency and code quality.

[0054] Intent visualization and code association technology not only displays code and change history, but also visualizes the developer's programming intent and its corresponding relationship with the code, enabling team members to intuitively understand each other's development ideas. The resulting collaborative environment is a comprehensive collaborative development interface that provides developers with an intent-based code interaction space, fostering the formation and emergence of collective intelligence and enabling geographically distributed development teams to collaborate efficiently as a single mindset.

[0055] This project is an edge computing controller firmware development for the Industrial Internet of Things, which has typical embedded system characteristics and strict hardware resource limitations.

[0056] Project Name: EdgeIoT-32 Controller Firmware; The composition of the development team is shown in Table 1: Table 1 Development team composition

[0057] Table 2 shows the hardware platform limitations. Table 2 Hardware platform limitations

[0058] Development Challenges: Teams in three locations need to collaborate on the development of highly coupled modules, such as interfaces between the driver layer and application layer, and between the protocol stack and security module. Hardware resources are limited, requiring highly optimized code, and conflicting optimization ideas from different developers. The project time is tight, and traditional serial development and conflict resolution processes are unable to meet the requirements; Team members have different programming styles and habits, making code merging difficult and easily introducing new problems.

[0059] Before implementing this method, the team mainly relied on traditional version control tools and online meetings to coordinate development. On average, 2-3 cross-team coordination meetings were required each week, and each code merge required a dedicated person to resolve conflicts, which seriously affected development efficiency.

[0060] Implementation process example: Step 701: Intent vector field construction example: During the EdgeIoT-32 controller firmware development process, the system continuously captured editing behavior data from developers around the world. The following table shows a sample of developer editing behavior collected within a single working day: The developer editing behavior data sample is shown in Table 3: Table 3 Developer editing behavior data sample table

[0061] The system inputs this raw editing behavior data into a multi-dimensional intent vector field construction algorithm to generate an intent vector representation for each developer. The following shows the intent vector construction process for a developer from the Beijing team: An example of intent vector construction by developer DEV_BJ02 is shown in Table 4: Table 4. Example of intent vector construction by developer DEV_BJ02

[0062] According to the multidimensional intention vector field formula: ; Calculate the developer's current local intention vector .in, represents the intent vector, Represents the time dimension, Represents the developer characteristic dimension, represents the contextual information dimension; is the number of feature dimensions, It is The weight of the feature dimension, It is the time dimension Next Extraction factors, It is the developer characteristic dimension Next Extraction factors, is the context information dimension Next Extraction factors.

[0063] In this example, the calculation is: ; Combined with the developer's historical intention information and time decay factor , and finally generate a complete intent vector representation: ; Here the time decay factor Used to balance the proportion of the current local intent vector and historical intent information in the final intent vector representation.

[0064] By analyzing the intent vector, the system identifies that the developer is attempting to optimize the execution efficiency of the sensor initialization function, with a particular focus on sensor response time in low-power modes. This intent information is subsequently integrated with the code structure topology for conflict prediction and resolution.

[0065] Step 702: Code structure topology mapping example: In the EdgeIoT-32 project, the system performs static analysis on the code base to extract code elements and their dependencies. The following table shows some code elements and their relationships in the sensor driver module: An example of the code element relationship (sensor driver module) is shown in Table 5: Table 5 Code element relationship example table

[0066] Based on the above code relationship table, the system executes the code structure topology mapping algorithm to generate the code topology structure. The following shows the code structure topology mapping process considering the three dimensions of semantics, relationships, and hardware constraints: An example of code structure topology mapping (sensor initialization function) is shown in Table 6: Table 6 Code structure topology mapping example table

[0067] According to the formula: ; in, Indicates the code structure topology value, represents the semantic dimension, Represents the relationship dimension, Represents the hardware dimension. is the code structure topology mapping algorithm function, Represents a code element relationship diagram, are parameters related to the semantic dimension, are the parameters related to the relationship dimension. These are parameters related to hardware dimensions.

[0068] The structural topology value of the sensor initialization function is calculated by this formula ,in Corresponding semantic dimension calculation results, The calculation results of the corresponding relationship dimension, Corresponding hardware dimension calculation results.

[0069] Specifically, the system calculates the hardware constraint matrix based on the limitations of the embedded hardware platform: An example of hardware constraint matrix calculation (sensor initialization and power management) is shown in Table 7: Table 7 Hardware constraint matrix calculation example

[0070] According to the hardware constraint matrix calculation formula: ; in, Represents the sensor initialization function (index is ) and power management functions (indexed by ) the strength of the hardware constraint relationship between them; represents the number of hardware factors, It is The weight of hardware factors, It is The correlation function value between the sensor initialization function and the power management function under the hardware factors is calculated using this formula. The hardware constraint relationship strength between the sensor initialization function and the power management function is 0.66, indicating that there is a strong hardware resource competition relationship between the two code elements.

[0071] Ultimately, the system integrates information from three dimensions: semantics, relationships, and hardware constraints, generating a complete topological representation of the code structure. This representation clearly reflects the multidimensional relationships between code elements, laying the foundation for subsequent integration with intent vector representations.

[0072] Step 703: Unify the intent-code space model to create an instance: In the EdgeIoT-32 project, the system merges the intent vector representation obtained in Step 701 with the code structure topology representation obtained in Step 702 to create a unified intent-code space model. The following table shows the intent-code mapping of several developers in the sensor module: An example of intent-code mapping (sensor module) is shown in Table 8: Table 8 Intent code mapping example table

[0073] The system applies the intent-to-code mapping function to calculate the mapping relationship between each developer's intent and code elements: ; in, Indicates developer intent With code elements The mapping relationship value between them; represents the number of feature pairs; It is The weight of each feature pair; It's intention No. eigenvalues; Is a code element No. Taking the DEV_BJ02 developer as an example, the system calculates the mapping relationship between its intent vector and the sensor initialization function: The intent code feature pair mapping calculation (DEV_BJ02 and sensor_init()) is shown in Table 9: Table 9 Intent code feature pair mapping calculation table

[0074] After completing the mapping calculation between each developer and code element, the system generates a unified intent code space model: ; in, Represents a unified intent code space model, represents the intent vector representation, Represents a code element, is the embedding function of the intent space, which is used to transform the intent vector Embedded into a high-dimensional joint space, is the embedding function of the code space, which is used to embed code elements Embedded into a high-dimensional joint space, The developer intent calculated by the intent-code mapping function mentioned above With code elements The model embeds the intent space and code space into a high-dimensional joint space, allowing intent and code to be analyzed and optimized under the same mathematical framework.

[0075] Step 704: Example of calculating conflict entropy distribution: Based on the unified intent-code space model, the system calculates the conflict entropy distribution in the EdgeIoT-32 project and identifies potential intent conflict areas. The following table shows the conflict entropy hotspots detected in a development activity: The conflict entropy distribution hotspots are shown in Table 10: Table 10 Conflict entropy distribution hotspot table

[0076] The system focused on areas of high conflict entropy in the sensor.c file, analyzing the conflicting intentions of developers DEV_BJ02 and DEV_SZ01. Developers on the Beijing team were attempting to optimize the execution efficiency of the sensor initialization function, while developers on the Shenzhen team planned to use the same function to add additional data collection capabilities, creating a performance and functionality conflict between the two.

[0077] Using the conflict entropy calculation method, the system calculates the joint probability distribution of relevant intent and code elements, and applies the formula to calculate the global conflict entropy: ; in, represents the global conflict entropy, represents the intent vector representation, Represents a code element, It's intention and code elements The joint probability distribution of It's intention The probability distribution of Is a code element The probability distribution of .

[0078] At the same time, the system calculates the local conflict entropy for high-conflict areas: ; in, Indicates a specific intention and code elements Local conflict entropy within the neighborhood area, and Respectively and The neighborhood area, 、 and The meaning of is consistent with that in the formula for calculating global conflict entropy.

[0079] Ultimately, the system generated a conflict entropy distribution map covering the entire EdgeIoT-32 project code base, visually displaying code areas where conflicts of intent may exist, providing key input for the next step of self-organizing optimization.

[0080] Step 705: Example of self-organizing optimization method: Based on the conflict entropy distribution graph, the system applied a self-organizing optimization method to the high-conflict areas in the EdgeIoT-32 project and generated the optimal code merging solution. Taking the conflict between the DEV_BJ02 and DEV_SZ01 developers in the sensor initialization function as an example, the system evaluated multiple possible merging solutions: An example of merging strategy evaluation (sensor_init function conflict) is shown in Table 11: Table 11 Merge strategy evaluation example table

[0081] The system applies self-organizing optimization method to calculate the objective function: ; in, , , are the trade-off coefficients of code quality, intent retention, and conflict entropy, respectively.

[0082] M001 solution: ; M002 solution: ; M003 solution: ; M004 solution: ; M005 solution: ; At the same time, the system considers the hardware resource constraints of the embedded system: , ensure that the selected merging scheme complies with the hardware resource constraints. The EdgeIoT-32 controller has a RAM resource limit of 128KB, and the maximum allowed resource utilization is 75%, or 96KB.

[0083] Through multiple rounds of iterative evaluation using evolutionary computing algorithms, the system finally determined the M005 reconstructed interface design as the optimal merging solution. This solution achieves the highest objective function value and meets resource constraints (resource utilization 69% < 75%). The core idea of ​​this solution is to restructure the sensor initialization function into a two-layer structure: the bottom layer maintains high-performance initialization implementation (meeting the DEV_BJ02 optimization intent), and the top layer provides a rich data acquisition interface (meeting the DEV_SZ01 functional intent).

[0084] The system converts the optimal merging solution into a specific operation sequence, which includes the following modification instructions: Rename the original sensor_init() to sensor_init_core() to retain the optimized high-performance implementation; Create a new sensor_init() function as the outer interface, and call sensor_init_core() internally; Add optional data collection function in new sensor_init(), but turn it off by default; Added configuration options to allow the application layer to enable advanced data collection features.

[0085] This merger solution not only retains the performance optimization results of the Beijing team, but also meets the Shenzhen team's needs for functional expansion, while ensuring that the code complies with the resource constraints of the embedded system, achieving optimal coordination between intent and code.

[0086] Technical effect verification: A comprehensive evaluation of the EdgeIoT-32 controller firmware project was conducted three months after implementing this approach. The following focuses on verifying the two most significant technical benefits of this implementation: improved collaborative development efficiency and enhanced conflict prediction and resolution capabilities.

[0087] Collaborative development efficiency improvement effect: The EdgeIoT-32 project team compared their development efficiency before and after implementing this method as follows: The comparison of collaborative development efficiency improvement is shown in Table 12: Table 12 Comparison of collaborative development efficiency improvements

[0088] Improvements in collaborative development efficiency were evident in multiple areas. The average feature development cycle was shortened from 18.5 days to 6.3 days, a 195.2% improvement. Cross-team communication costs were reduced from 12.4 hours per week to 4.1 hours, a 202.4% decrease. Code integration time was reduced from 9.7 hours per session to 3.2 hours, a 203.1% improvement. Overall, the average efficiency of collaborative development across teams increased by over 200%, which is consistent with the expected technical benefits of this implementation.

[0089] Particularly noteworthy is the improvement in developer intent comprehension from 37% to 89%. This means team members can now understand each other's programming intent without extensive explicit communication, significantly reducing rework and revisions caused by misunderstandings. This capability, lacking in traditional collaborative development tools, is one of the core innovations of this implementation.

[0090] Effect of enhancing conflict prediction and resolution capabilities: The following is a comparison of the conflict prediction and resolution capabilities of the EdgeIoT-32 project before and after implementing this method: The comparison of conflict prediction and resolution capabilities is shown in Table 13: Table 13 Comparison of conflict prediction and resolution capabilities

[0091] After implementing this method, the system was able to predict potential conflicts an average of 38.4 hours before they actually occurred, increasing conflict prediction accuracy from 31% to 87%, a 180.6% improvement. This early warning capability enabled the development team to take preventative measures before issues became serious, significantly improving the fluidity of collaborative development.

[0092] At the same time, the quality of conflict resolution has been significantly improved. The average conflict resolution time has been reduced from 7.3 hours to 2.8 hours, an increase of 160.7%; the conflict resolution quality score has increased from 6.2 points to 8.7 points, an increase of 40.3%; the defect density caused by residual conflicts has been reduced from 3.1 per thousand lines of code to 0.7, a decrease of 342.9%.

[0093] Overall, the improvement in conflict prediction accuracy and resolution quality achieved by this implementation far exceeded expectations. This is mainly due to the application of conflict entropy distribution calculation and self-organizing optimization methods, which enable the system to identify and resolve conflicts at both the code level and the intent level.

[0094] The practical application of the EdgeIoT-32 controller firmware project demonstrates that this implementation successfully addresses the core technical challenges of remote collaborative embedded development by establishing a mathematical mapping between intent and code. A unified intent-code space model enables developers to understand each other's programming intent; conflict entropy distribution calculations enable early prediction of potential conflicts; and self-organizing optimization methods generate optimal code merging solutions that account for the intent of multiple parties.

[0095] Especially in terms of the two key indicators of collaborative development efficiency and conflict handling capabilities, this implementation method has achieved remarkable technical results, verifying the innovative value and practicality of intent-code isomorphism in solving the problems of remote collaborative embedded development.

[0096] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make more forms of equivalent embodiments based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A remote collaborative embedded development method, characterized in that: The following steps are involved: Receive developer editing behavior data and perform multi-dimensional intent vector field construction to obtain intent vector representation; Read the embedded source code and perform code structure topology mapping to form a code structure topology representation; Integrate the intent vector representation with the code structure topology representation to create a unified intent-code space model; Calculate the conflict entropy distribution based on the unified intent-code space model and generate a conflict entropy distribution graph; According to the conflict entropy distribution graph, a self-organizing optimization method is used to coordinate the intention and code space to generate the optimal code merging solution.

2. A remote collaborative embedded development method according to claim 1, characterized in that: The intent vector representation is generated by the following formula: ； in, represents the final generated intent vector representation, is the time decay factor, is the local intention vector, which represents the intention vector generated based on the current local environment. It is historical intention information, reflecting the relevant intention situations in the past.

3. The remote collaborative embedded development method according to claim 1, characterized in that: The code structure topology mapping is calculated using the following formula: ； in, is the code structure topology, is a topological mapping operator, It is a code relationship diagram. is the code semantic information matrix, is the relationship strength matrix, is the hardware constraint matrix.

4. A remote collaborative embedded development method according to claim 3, characterized in that: The hardware constraint matrix is ​​calculated by the following formula: ； in, Indicates the first Rank Elements of the column, is the hardware factor weight, which is used to measure the influence of different hardware factors on the relationship between code elements. Is a code element and Hardware factors The correlation function on the code element and In the The correlation between hardware factors, is the summation variable, representing the index of the hardware factor, used to traverse all hardware factors, is the summation upper limit, which represents the total number of hardware factors. The value of each element in the hardware constraint matrix is ​​obtained by weighted summation of all hardware factors.

5. The remote collaborative embedded development method according to claim 1, characterized in that: The unified intent-code space model is created by the following formula: ； in, Represents the unified intent-code space model, represents the intent vector representation, Represents the topological representation of the code structure; The intention vector is represented by An embedding function that maps to the intent part of the unified intent-code space model, which embeds the intent vector representation into the intent part of the unified model in a specific way; It is a topological representation of the code structure The embedding function mapped to the code part in the unified intent-code space model is responsible for embedding the code structure topology representation into the code part of the unified model; It is the intent-code mapping function used to establish the intent vector representation in the unified intent-code space model and code structure topology representation The mapping relationship between them makes the intention and the code have corresponding connections in the unified model.

6. A remote collaborative embedded development method according to claim 5, characterized in that: The intent code mapping function is defined by the following formula: ； in, Intent-code mapping function, used to establish intent vector representation in the unified intent-code space model and code structure topology representation The mapping relationship between is a function that extracts features from the intent vector, is a function that extracts features from the code topology, is the weight parameter of the feature pair, is the summation variable, representing the index of the feature pair, is the total number of feature pairs. The mapping between intent vector and code topology is achieved by taking weighted sum of all feature pairs.

7. The remote collaborative embedded development method according to claim 1, characterized in that: The generation of the conflict entropy distribution map includes calculating the global conflict entropy: ； in, Represents the global conflict entropy, the formula is obtained by and code structure topology The joint probability distribution of and their respective marginal probability distributions and An integral operation is performed to measure the degree of conflict between the intention and the code, which is then used to generate a conflict entropy distribution map. is the intent vector and code topology The probability of co-occurrence, is the intent vector The marginal probability distribution of Is the code structure topology The marginal probability distribution of Represents the logarithmic function.

8. The remote collaborative embedded development method according to claim 1, characterized in that: The self-organizing optimization method calculates the objective function: ； in, Represents the merger plan, is the objective function value, which is used to evaluate the pros and cons of the merging scheme. is the merged code quality function, which is used to measure the quality of the merged code. is the intention preservation function, which is used to indicate the degree to which the merging solution preserves the developer’s intention. is the conflict entropy after the merge, which is used to quantify the degree of conflict between the intention and the code in the merge solution. 、 and are the trade-off coefficients, used to adjust 、 、 The relative importance of the objective function.

9. The remote collaborative embedded development method according to claim 1, characterized in that: After generating the optimal code merging solution, it also includes building an intent visualization interface and displaying the optimization results to form a collaborative environment that supports collective intelligence.

10. A remote collaborative embedded development system, used to execute a remote collaborative embedded development method according to any one of claims 1 to 9, characterized in that: include: The intent vector construction module is used to receive the developer's editing behavior data and perform multi-dimensional intent vector field construction to obtain the intent vector representation; A code topology mapping module is used to read the embedded source code and perform code structure topology mapping to form a code structure topology representation; The spatial model fusion module is used to fuse the intent vector representation with the code structure topology representation to create a unified intent-code spatial model; A conflict entropy calculation module is used to calculate the conflict entropy distribution based on the unified intent-code space model and generate a conflict entropy distribution map; The self-organizing optimization module is used to coordinate the intent and code space according to the conflict entropy distribution map and generate the optimal code merging solution using the self-organizing optimization method.

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