Cross-team software development collaboration optimization system and method

Abstract

This invention discloses a cross-team software development collaborative optimization system and method, relating to the field of data collaborative optimization. The invention collects software requirement data from information sources and language libraries from multiple software development teams, processes this data, and then constructs language library collaborative awareness rules and security testing rules through rule definition. Based on these rules, a development collaboration model is built. Simultaneously, the development collaboration processes of each collaborator in multiple software development teams are collected, and the collaborative development processes of each collaborator are synchronized based on the constructed development collaboration model, the processed software requirement data, and a wireless network. Finally, the collaborative development processes of each collaborator are verified and optimized based on the processed information source software requirement data and security testing rules, thereby improving the effectiveness of software development collaborative optimization.

Description

Technical Field

[0001] This invention relates to the field of data collaborative optimization technology, specifically to a cross-team software development collaborative optimization system and method. Background Technology

[0002] With the expansion of computer applications and the continuous growth in software scale and complexity, software development increasingly reflects the characteristics of engineering projects involving multiple roles and collaborative stages. Especially against the backdrop of the rapid popularization of computer networks and the rapid increase in the number of multinational software companies, software development models have evolved from centralized to loosely distributed development activities, such as software outsourcing and component-based development. This development model helps software organizations reduce costs and improve efficiency, and also facilitates communication and skill complementarity among developers with different fields and expertise. Distributed collaborative development also presents new challenges to software engineering theory and methods: how to standardize the software development process among geographically dispersed development teams; how to bridge the information gap among stakeholders in a software project; how to ensure the timeliness and effectiveness of geographically dispersed communication; and how to standardize project management, control costs, schedules, and risks, rationally utilize resources, and resolve conflicts.

[0003] Existing technologies, such as the invention patent application CN112969107B, disclose a resource optimization method for multi-dimensional collaborative optical networks in data centers. This method includes: S1. Optical network information content: the optical network information connection content to be processed; based on the optical network information connection content, finding a reliable service point suitable for the current service content; S2. Obtaining optical network content: obtaining the optical network service content suitable for the current service point and submitting it to the data center for content optimization; S3. Allocating resources to the data center: based on the usage of data center resources. The data center of this invention can simultaneously serve multiple optical network service types and rationally allocate resources considering data center resource usage, realizing a resource optimization scheme for multiple types of optical network services. This facilitates multi-dimensional collaborative resource optimization of optical networks. The data center simultaneously serves different optical network service types and performs classification and grading, enabling more convenient resource utilization.

[0004] As can be seen from the above solutions, current data collaboration is mostly applied to the allocation and optimization of network resources, and its application in software development is not high. Summary of the Invention

[0005] The purpose of this invention is to provide a cross-team software development collaborative optimization system and method, which solves the problems existing in the background technology.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a cross-team software development collaborative optimization method, specifically including the following steps: S1. Collect software requirement data from information sources, and process the collected software requirement data through data processing methods to obtain processed software requirement data. S2. Collect the language libraries of multiple software development teams, process the language libraries of multiple software development teams, and obtain the processed language libraries; S21. Collect the language libraries of multiple software development teams, and process the language libraries of multiple software development teams through data processing methods to obtain the processed language libraries. S22. Based on the language library obtained after data processing, construct language library collaborative perception rules and security testing rules through rule definition. S23. Summarize the language library collaborative perception rules, security test rules and the processed language library to obtain the processed language library; S3. Develop a collaborative model based on collaborative perception rules, security testing rules, and the processed language library; S4. Collect the development collaboration process of each collaborator in multiple software development teams, and synchronize the development collaboration process of each collaborator based on the constructed development collaboration model, processed software requirement data and wireless network. S5. Based on the processed information source software requirements data and security testing rules, verify and optimize the development collaboration process of each collaborator, and determine the optimized development collaboration model based on the optimized development collaboration process.

[0007] Preferably, the process of collecting software requirement data from information sources and processing the collected software requirement data to obtain processed software requirement data includes the following steps: S11. Collect software requirement data from information sources and standardize the definition of the collected software requirement data; The software requirement data includes: software requirement order number, software requirement time, and software requirement content; The standard data for any invoice must include the software requirement order number, the software requirement time, and the software requirement content. S12. Process the collected software requirement data using data processing methods; The collected software requirement data from information sources was filtered using the Bloom filter method. Create an array of length m, and select... Each hash function iterates through each set of data in the software requirements data and stores the results in an array; During the traversal using the hash function, if two sets of data have the same traversal result, each data in these two sets is compared. When the comparison results are consistent, the two sets of data are set to be the same data, and the software requirement data that arrives after the software requirement time is deleted. After the traversal is complete, the software requirement data stored in the array during the traversal is summarized to obtain the filtered software requirement data.

[0008] Preferably, the step of collecting language libraries from multiple software development teams and processing these language libraries to obtain the processed language libraries includes the following steps: The software development team's language library is configured to consist of multiple sets of development functions; A set of development functions is randomly selected from the language libraries of multiple software development teams. All function pairs of these development functions are extracted, and a set of function pairs is constructed. ; For the set of function pairs For each function pair in the dataset, record the frequency of the function pair's occurrence and determine the development topic of the function based on the frequency; ; in, Indicates the function pair set function pairs Become the The probability of the topic of the group development function. Indicates the first Function pairs in group development functions Number of times it appears; Based on the topic of development functions, multiple groups of development functions in the language library are classified to obtain the language library after data processing.

[0009] Preferably, the step of constructing language library collaborative awareness rules and security testing rules based on the obtained processed language library through rule definition includes the following steps: Setting security testing rules includes: ACT testing and OPR testing; ACT testing = {Define evaluation tasks, verify test techniques, verify build stability, test and evaluate, complete acceptance tasks, and improve test assets}; OPR test = {ROL test, RTF test, TOL test}; Setting language library collaborative awareness rules includes: context awareness; Context awareness = Context(Entity, Type, Time); Where Entity is the context function name, Type is the context function type, Time is the timestamp, and Context represents the context-aware rule.

[0010] Preferably, the construction of the collaborative development model based on collaborative awareness rules, security testing rules, and the processed language library includes the following steps: S31. Set the collaborative perception strength. The collaborative perception strength is proportional to the number of developers online at the same time in multiple software development teams. The more developers online at the same time, the higher the collaborative perception strength. S32. Determine the characteristics of various development functions in the processed language library using a convolutional neural network; S33. Summarize the characteristics of various development functions and construct a development collaboration model based on the similarity function; The similarity function is as follows: ; in, This represents the similarity value between the first set of development function features and the second set of development function features. A collaborative model is developed by combining similarity functions and convolutional neural networks.

[0011] Preferably, the step of determining the features of various development functions in the processed language library using a convolutional neural network includes the following steps: The convolutional neural network includes an input layer, a convolutional layer, a pooling layer, and a fully connected layer, wherein the convolutional layers and pooling layers are stacked continuously, and the output of the upper layer serves as the input of the lower layer. The various development functions in the processed language library are then input into the input layer of the convolutional neural network. After the input is complete, initialize the convolutional kernels, corresponding weights, and stride of the input layer of the convolutional neural network; After receiving various development functions, the input layer transmits them to the convolutional layer, which then performs convolution operations on the features of the development functions. The convolution operation involves moving the convolution kernel and its corresponding weights within various input development functions according to a set convolution stride. Each time the convolution kernel moves, it calculates a set of feature data based on the data in each development function. After the convolutional layer extracts the features of various development functions, the extracted development function features are processed by the activation function, and the development function features processed by the activation function are transmitted to the pooling layer. The pooling layer downsamples the features to reduce the feature dimensionality. By continuously stacking convolutional and pooling layers, features are extracted from various development functions input until the feature extraction converges, the convolution stops, and the extracted features are aggregated and input into the fully connected layer. Finally, the fully connected layer integrates the extracted features and outputs the features of various development functions.

[0012] Preferably, the step of collecting the development collaboration process of each collaborator in multiple software development teams, and synchronizing the development collaboration process of each collaborator based on the constructed development collaboration model, processed software requirement data, and wireless network includes the following steps: S41. Based on the processed software requirements data, tasks are assigned to collaborators in multiple software development teams. S42. Based on task allocation results, construct a development collaboration model and wireless network for development collaboration; After the tasks are assigned, the development tasks of each collaborator are identified and similarity analyzed based on the development collaboration model and wireless network; During the analysis process, a buffer cache is set for each task. When accessing the corresponding buffer cache, if the buffer is not locked, the first collaborator will lock it first and then enter the corresponding buffer cache. Once the buffer cache is locked, the permissions of other collaborators are set to read-only access. At the same time, the development tasks of each collaborator are identified and similarity analyzed through the read-only access of other collaborators. When you are finished processing and want to leave the cache, the cache will be unlocked. S43. Synchronize the development collaboration process of each collaborator; Set a synchronization period for the development collaboration process, and upload and synchronize the development collaboration process of each collaborator based on the set synchronization period.

[0013] Preferably, the process of verifying and optimizing the development collaboration process of each collaborator based on the processed information source software requirements data and security testing rules, and determining the optimized development collaboration model based on the optimized development collaboration process, includes the following steps: S51. Verify the development collaboration process of each collaborator based on the processed information source software requirements data and security testing rules; Based on the collaborative awareness rules and security testing rules in step S22, analyze the development collaboration process of each collaborator after uploading; Based on context awareness and similarity functions, the development collaboration process of each collaborator is analyzed after uploading, the similarity of the context is determined, and the development collaboration process of each collaborator is verified according to the similarity judgment results. Similarity is set as verification passed, and dissimilarity is set as verification failed. S52. After successful verification, optimize the development collaboration process for each collaborator.

[0014] Preferably, optimizing the development collaboration process for each collaborator after successful verification includes the following steps: Real-time monitoring of the bandwidth status of the wireless network during the collaborative development process among various collaborators; Calculate the data transmission rate between collaborators in the development collaboration process based on the bandwidth status of the wireless network. The formula for calculating data transmission rate is as follows: ; in, This indicates the maximum data transmission rate for the current road segment. This indicates the bandwidth of the current road segment being collected. Indicates data symbols; Calculate data synchronization time based on the maximum data transmission rate and the distance between the development collaboration processes of various collaborators; ; in, Indicates the data synchronization time. This indicates the current data transmission rate of the wireless network. This indicates the distance between the collaborative development processes of each collaborator. This indicates the speed at which data is transmitted over the network. Indicates the time of data transmission process; Based on the calculated data synchronization time, the development collaboration process of each collaborator is optimized and compensated through CPU clock synchronization.

[0015] This invention also provides a cross-team software development collaborative optimization system for implementing a cross-team software development collaborative optimization method. The system includes: a data acquisition module, a data processing module, a development collaboration module, a collaborative synchronization module, a display unit, and a collaborative synchronization optimization module. The data acquisition module is used to collect software requirement data from information sources and language libraries from multiple software development teams. The data processing module is used to process the collected information source software requirement data and the language libraries of multiple software development teams; The development collaboration module is used to construct a development collaboration model based on collaboration awareness rules, security testing rules, and the processed language library. The collaborative synchronization module is used to synchronize the development collaboration process of each collaborator based on the constructed development collaboration model, the processed software requirement data, and the wireless network. The collaborative synchronization optimization module is used to verify and optimize the development collaboration process of each collaborator using the processed information source software requirement data and security test rules; The display unit can display the development collaboration process of each collaborator in real time.

[0016] The beneficial effects of this invention are as follows: This invention collects software requirement data from information sources and language libraries from multiple software development teams. It then processes this data using data processing methods. After processing, it constructs language library collaboration awareness rules and security testing rules through rule definition. Based on these rules and the processed language libraries, it builds a development collaboration model. Simultaneously, it collects the development collaboration processes of each collaborator in multiple software development teams and synchronizes these processes using the constructed development collaboration model, the processed software requirement data, and a wireless network. Finally, it verifies and optimizes the development collaboration processes of each collaborator based on the processed information source software requirement data and security testing rules, thereby improving the effectiveness of software development collaboration optimization. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the cross-team software development collaborative optimization system module of the present invention.

[0019] Figure 2 This is a schematic diagram of the cross-team software development collaborative optimization method of the present invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the drawings, not all structures. Moreover, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0021] It should also be noted that, for ease of description, the accompanying drawings show only the parts relevant to the invention and not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it may also have additional steps not included in the drawings. The process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0022] Example 1, Reference Figure 1 , Figure 2 As shown, this invention provides a cross-team software development collaborative optimization method, including the following steps: S1. Collect software requirement data from information sources, and process the collected software requirement data through data processing methods to obtain processed software requirement data. S2. Collect the language libraries of multiple software development teams, process the language libraries of multiple software development teams, and obtain the processed language libraries; S21. Collect the language libraries of multiple software development teams, and process the language libraries of multiple software development teams through data processing methods to obtain the processed language libraries. S22. Based on the language library obtained after data processing, construct language library collaborative perception rules and security testing rules through rule definition. S23. Summarize the language library collaborative perception rules, security test rules and the processed language library to obtain the processed language library; S3. Develop a collaborative model based on collaborative perception rules, security testing rules, and the processed language library; S4. Collect the development collaboration process of each collaborator in multiple software development teams, and synchronize the development collaboration process of each collaborator based on the constructed development collaboration model, processed software requirement data and wireless network. S5. Based on the processed information source software requirements data and security testing rules, verify and optimize the development collaboration process of each collaborator, and determine the optimized development collaboration model based on the optimized development collaboration process.

[0023] Furthermore, referring to Figure 1 , Figure 2 As shown, the process of collecting software requirement data from information sources and processing the collected software requirement data to obtain the processed software requirement data includes the following steps: S11. Collect software requirement data from information sources and standardize the definition of the collected software requirement data; The software requirement data includes: software requirement order number, software requirement time, and software requirement content; The standard data for any invoice must include the software requirement order number, the software requirement time, and the software requirement content. Set the software requirement time T to be defined in a standard time format, accurate to milliseconds, and the software requirement content M represents the operation to be performed in the current software requirement; After the standard structure for software requirements data is defined, it is then... The collected software requirement data is stored in the following format; Where T represents the time when the software requirement is issued, M represents the content of the software requirement, F represents the software requirement data, and the software requirement order number A is a unique identifier for identifying the number of software requirements; S12. Process the collected software requirement data using data processing methods; The collected software requirement data from information sources was filtered using the Bloom filter method. Create an array of length m, and select... Each hash function iterates through each set of data in the software requirements data and stores the results in an array; During the traversal using the hash function, if two sets of data have the same traversal result, each data in these two sets is compared. When the comparison results are consistent, the two sets of data are set to be the same data, and the software requirement data that arrives after the software requirement time is deleted. After the traversal is complete, the software requirement data stored in the array during the traversal is summarized to obtain the filtered software requirement data. Furthermore, referring to Figure 1 , Figure 2 As shown, language libraries from multiple software development teams are collected and processed to obtain the processed language libraries, which includes the following steps: S21. Collect the language libraries of multiple software development teams, and process the language libraries of multiple software development teams through data processing methods to obtain the processed language libraries. The software development team's language library is configured to consist of multiple sets of development functions; A set of development functions is randomly selected from the language libraries of multiple software development teams. All function pairs of these development functions are extracted, and a set of function pairs is constructed. ; Furthermore, for the set of function pairs For each function pair in the dataset, record the frequency of the function pair's occurrence and determine the development topic of the function based on the frequency; ; in, Indicates the function pair set function pairs Become the The probability of the topic of the group development function. Indicates the first Function pairs in group development functions Number of times it appears; Furthermore, based on the topic of development functions, multiple groups of development functions in the language library are classified to obtain the language library after data processing; S22. Based on the language library obtained after data processing, construct language library collaborative perception rules and security testing rules through rule definition. Setting security testing rules includes: ACT testing and OPR testing; ACT testing = {Define evaluation tasks, verify test techniques, verify build stability, test and evaluate, complete acceptance tasks, and improve test assets}; OPR test = {ROL test, RTF test, TOL test}; ROL testing is used to assign test managers and test analysts; RTF testing is testing for development tasks. Specifically, it is used to develop test plans for development tasks, write test evaluation summaries for development tasks, build a list of test ideas for development tasks, select test cases for development tasks, collect test data for development tasks, and record test results for development tasks. TOL testing is a type of testing specifically designed for testing tools. It is used to develop test plans for testing tools, schedule the execution of tests, automate the testing process, capture test results, and evaluate those results. Setting language library collaborative awareness rules includes: context awareness; Context awareness = Context(Entity, Type, Time); Where Entity is the context function name, Type is the context function type, Time is the timestamp, and Context represents the context-aware rule; S23. Summarize the language library collaborative perception rules, security test rules and the processed language library to obtain the processed language library; Furthermore, referring to Figure 1 , Figure 2 As shown, building a collaborative development model based on collaborative awareness rules, security testing rules, and a processed language library includes the following steps: S31. Set the collaborative perception strength. The collaborative perception strength is proportional to the number of developers online at the same time in multiple software development teams. The more developers online at the same time, the higher the collaborative perception strength. S32. Determine the characteristics of various development functions in the processed language library using a convolutional neural network; For the various development functions in the processed language library, features of each development function are extracted using a convolutional neural network; The convolutional neural network includes an input layer, a convolutional layer, a pooling layer, and a fully connected layer, wherein the convolutional layers and pooling layers are stacked continuously, and the output of the upper layer serves as the input of the lower layer. Furthermore, the various development functions in the processed language library are input into the input layer of the convolutional neural network; After the input is complete, initialize the convolutional kernels, corresponding weights, and stride of the input layer of the convolutional neural network; Furthermore, after receiving various development functions, the input layer transmits the received development functions to the convolutional layer, which then performs convolution operations on the features of the various development functions. The convolution operation involves moving the convolution kernel through various input expansion functions according to a set convolutional stride and weights. Each time the kernel moves, it calculates a set of feature data based on the data in each expansion function. The specific formula is shown below: ; in, This represents the various development functions that are input. This represents the weights of the corresponding convolution kernel. Indicates the bias value. Indicates output features; Furthermore, after the convolutional layer extracts the features of various development functions, it processes the extracted features of various development functions through activation functions, and then transmits the features of various development functions processed by activation functions to the pooling layer. The pooling layer downsamples the features to reduce the feature dimensionality. Furthermore, by continuously stacking convolutional and pooling layers, features are extracted from the various development functions input until the feature extraction converges, the convolution stops, and the extracted features are aggregated and input into the fully connected layer. Finally, the fully connected layer integrates the extracted features and outputs the features of various development functions; S33. Summarize the characteristics of various development functions and construct a development collaboration model based on the similarity function; The similarity function is as follows: ; in, This represents the similarity value between the first set of development function features and the second set of development function features. A collaborative model is developed by combining similarity functions, collaborative perception rules, and convolutional neural networks. Furthermore, collaborative perception rules are established using similarity values ​​as perception thresholds. The similarity between contexts is determined by the collaborative perception rules and perception thresholds, and the collaborative development process is determined based on the similarity between contexts. Set a perception threshold. When the similarity between contexts is less than or equal to the perception threshold, it indicates that there is an anomaly in the current development collaboration process; otherwise, it indicates that the current development collaboration process is normal. Furthermore, referring to Figure 1 , Figure 2 As shown, the process of collecting the collaborative development processes of various collaborators in multiple software development teams, and synchronizing the collaborative development processes of each collaborator based on the constructed collaborative development model, processed software requirement data, and wireless network, includes the following steps: S41. Based on the processed software requirements data, tasks are assigned to collaborators in multiple software development teams. Scheduling methods for n different processed software requirement data include: Obtain the arrival time of the processed software requirements data Art[i,j], the estimated processing time of each stage of the processed software requirements data Est[i,j], and the collaborators Ava[i,...,j] in multiple software development teams; Furthermore, the predetermined processing time periods for each stage of the processed software requirement data are obtained through Art[i,j]+Est[i,j]. Furthermore, the scheduled processing time periods for each stage of the obtained processed software requirements data will be compared with Ava. Perform matching to query whether each collaborator in multiple software development teams is available for the corresponding time period; If Ava If a matching idle time period exists, according to the principle of synchronization and mutual exclusion, the allocation is successful first, and then the user confirms. S42. Based on task allocation results, construct a development collaboration model and wireless network for development collaboration; After the tasks are assigned, the development tasks of each collaborator are identified and similarity analyzed based on the development collaboration model and wireless network; Furthermore, during the analysis process, a buffer cache is set for each task. When accessing the corresponding buffer cache, if the buffer is not locked, the first collaborator will lock it first and then enter the corresponding buffer cache. Once the buffer cache is locked, the permissions of other collaborators are set to read-only access. At the same time, the development tasks of each collaborator are identified and similarity analyzed through the read-only access of other collaborators. Furthermore, the buffer cache will be unlocked when the process is complete and the user is about to leave the buffer cache. S43. Synchronize the development collaboration process of each collaborator; Set a synchronization period for the development collaboration process, and upload and synchronize the development collaboration process of each collaborator based on the set synchronization period; Furthermore, referring to Figure 1 , Figure 2 As shown, the development collaboration process of each collaborator is verified and optimized based on the processed information source software requirements data and security testing rules. The optimized development collaboration model is then determined based on the optimized development collaboration process, including the following steps: S51. Verify the development collaboration process of each collaborator based on the processed information source software requirements data and security testing rules; Based on the collaborative awareness rules and security testing rules in step S22, analyze the development collaboration process of each collaborator after uploading; Furthermore, based on context awareness and similarity functions, the development collaboration process of each collaborator after uploading is analyzed to determine whether the contexts are similar. Based on the similarity judgment results, the development collaboration process of each collaborator is verified, with similarity being considered as verification passing and dissimilarity as verification failing. S52. After verification, optimize the development collaboration process of each collaborator; Real-time monitoring of the bandwidth status of the wireless network during the collaborative development process among various collaborators; Furthermore, the data transmission rate between collaborators in the development collaboration process is calculated based on the bandwidth status of the wireless network; The formula for calculating data transmission rate is as follows: ; in, This indicates the maximum data transmission rate for the current road segment. This indicates the bandwidth of the current road segment being collected. Indicates data symbols; Furthermore, the data synchronization time is calculated based on the data transmission limit and the distance between the development collaboration processes of each collaborator; The formula for calculating data synchronization time is as follows: ; in, Indicates the data synchronization time. This indicates the current data transmission rate of the wireless network. This indicates the distance between the collaborative development processes of each collaborator. This indicates the speed at which data is transmitted over the network. Indicates the time of data transmission process; Furthermore, based on the calculated data synchronization time, the development collaboration process of each collaborator is optimized and compensated through CPU clock synchronization. The synchronous optimization compensation is shown below: The CPU receives data synchronization content from the development collaboration process through I / O peripherals; After receiving the data, the CPU will retrieve and process the synchronized data based on the clock cycle. Furthermore, the CPU will cycle through three processes: instruction fetch cycle, execution cycle, and interrupt cycle. When the CPU is in the instruction fetch cycle, it analyzes and decodes the synchronization content of the most recently received data development collaboration process. After analysis and decoding, the CPU enters the execution cycle, generates corresponding control signals according to the decoding results, and transmits the control signals to the display module through the I / O interface. The display module then displays the data synchronization content of the development collaboration process according to the control signals. After the data synchronization content is displayed, control information will be fed back to the CPU. After receiving the feedback control signal, the CPU will end the execution cycle and enter the interrupt cycle. After the CPU enters an interrupt cycle, it checks whether it has received emergency control data. If emergency control data is detected, it executes the emergency control data; if no emergency control data is detected, the interrupt cycle ends and the CPU enters the next instruction fetch cycle to perform the next data synchronization.

[0024] Example 2, In one specific embodiment, the cross-team software development collaborative optimization system is used to implement a cross-team software development collaborative optimization method. The system includes: a data acquisition module, a data processing module, a development collaboration module, a collaborative synchronization module, a display unit, and a collaborative synchronization optimization module. The data acquisition module is used to collect software requirement data from information sources and language libraries from multiple software development teams. The data processing module is used to process the collected information source software requirement data and the language libraries of multiple software development teams; The development collaboration module is used to construct a development collaboration model based on collaboration awareness rules, security testing rules, and the processed language library. The collaborative synchronization module is used to synchronize the development collaboration process of each collaborator based on the constructed development collaboration model, the processed software requirement data, and the wireless network. The collaborative synchronization optimization module is used to verify and optimize the development collaboration process of each collaborator using the processed information source software requirement data and security test rules; The display unit can display the development collaboration process of each collaborator in real time.

[0025] The working principle of the cross-team software development collaborative optimization system provided by this invention is as follows: The data acquisition module is responsible for real-time synchronous collection of software requirement data from information sources and language libraries from multiple software development teams. It then constructs language library collaboration awareness rules and security testing rules through rule definition. The data processing module subsequently processes the collected software requirement data and language libraries from multiple software development teams, resulting in processed software requirement data and a processed language library. The development collaboration module uses the collaboration awareness rules, security testing rules, and the processed language library to construct a development collaboration model, determining the collaboration awareness strength, the characteristics of various development functions in the processed language library, and the similarity function. The collaboration synchronization module synchronizes the development collaboration process of each collaborator based on the constructed development collaboration model, the processed software requirement data, and the wireless network. Finally, the collaboration synchronization optimization module and display unit verify, optimize, and display the development collaboration process of each collaborator using the processed software requirement data and security testing rules, making collaborative development between collaborators more intuitive and synchronized. This achieves a fully automated, integrated cross-team software development collaboration process, encompassing data acquisition, data processing, development collaboration, and synchronization compensation.

[0026] It should be noted that, The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A cross-team software development collaborative optimization method, characterized in that, Includes the following steps: S1. Collect software requirement data from information sources, and process the collected software requirement data through data processing methods to obtain processed software requirement data. S2. Collect the language libraries of multiple software development teams, process the language libraries of multiple software development teams, and obtain the processed language libraries; S21. Collect the language libraries of multiple software development teams, and process the language libraries of multiple software development teams through data processing methods to obtain the processed language libraries. S22. Based on the language library obtained after data processing, construct language library collaborative perception rules and security testing rules through rule definition. S23. Summarize the language library collaborative perception rules, security test rules and the processed language library to obtain the processed language library; S3. Develop a collaborative model based on collaborative perception rules, security testing rules, and the processed language library; S4. Collect the development collaboration process of each collaborator in multiple software development teams, and synchronize the development collaboration process of each collaborator based on the constructed development collaboration model, processed software requirement data and wireless network. S5. Based on the processed information source software requirement data and security testing rules, verify and optimize the development collaboration process of each collaborator, and determine the optimized development collaboration model based on the optimized development collaboration process.

2. The cross-team software development collaborative optimization method according to claim 1, characterized in that, Step S1 includes the following steps: S11. Collect software requirement data from information sources and standardize the definition of the collected software requirement data; The software requirement data includes: software requirement order number, software requirement time, and software requirement content; The standard data for any invoice must include the software requirement order number, the software requirement time, and the software requirement content. S12. Process the collected software requirement data using data processing methods; The collected software requirement data from information sources was filtered using the Bloom filter method. Create an array of length m, and select... Each hash function iterates through each set of data in the software requirements data and stores the results in an array; During the traversal using the hash function, if two sets of data have the same traversal result, each data in these two sets is compared. When the comparison results are consistent, the two sets of data are set to be the same data, and the software requirement data that arrives after the software requirement time is deleted. After the traversal is complete, the software requirement data stored in the array during the traversal is summarized to obtain the filtered software requirement data.

3. The cross-team software development collaborative optimization method according to claim 1, characterized in that, Step S21 includes the following steps: The software development team's language library is configured to consist of multiple sets of development functions; A set of development functions is randomly selected from the language libraries of multiple software development teams. All function pairs of these development functions are extracted, and a set of function pairs is constructed. ; For the set of function pairs For each function pair in the database, record the frequency of the function pair's occurrence and determine the development topic of the function based on the frequency; ； in, Indicates the function pair set function pairs Become the The probability of the topic of the group development function. Indicates the first Function pairs in group development functions Number of times it appears; Based on the topic of development functions, multiple groups of development functions in the language library are classified to obtain the language library after data processing.

4. The cross-team software development collaborative optimization method according to claim 1, characterized in that, Step S22 includes the following steps: Setting security testing rules includes: ACT testing and OPR testing; ACT testing = {Define evaluation tasks, verify test techniques, verify build stability, test and evaluate, complete acceptance tasks, and improve test assets}; OPR test = {ROL test, RTF test, TOL test}; Setting language library collaborative awareness rules includes: context awareness; Context awareness = Context(Entity, Type, Time); Where Entity is the context function name, Type is the context function type, Time is the timestamp, and Context represents the context-aware rule.

5. The cross-team software development collaborative optimization method according to claim 1, characterized in that, Step S3 includes the following steps: S31. Set the collaborative perception strength. The collaborative perception strength is proportional to the number of developers online at the same time in multiple software development teams. The more developers online at the same time, the higher the collaborative perception strength. S32. Determine the characteristics of various development functions in the processed language library using a convolutional neural network; S33. Summarize the characteristics of various development functions and construct a development collaboration model based on the similarity function; The similarity function is as follows: ； in, This represents the similarity value between the first set of development function features and the second set of development function features. A collaborative model is developed by combining similarity functions and convolutional neural networks.

6. The cross-team software development collaborative optimization method according to claim 5, characterized in that, Step S32 includes the following steps: For the various development functions in the processed language library, features of each development function are extracted using a convolutional neural network; The convolutional neural network includes an input layer, a convolutional layer, a pooling layer, and a fully connected layer, wherein the convolutional layers and pooling layers are stacked continuously, and the output of the upper layer serves as the input of the lower layer. The various development functions in the processed language library are then input into the input layer of the convolutional neural network. After the input is complete, initialize the convolutional kernels, corresponding weights, and stride of the input layer of the convolutional neural network; After receiving various development functions, the input layer transmits them to the convolutional layer, which then performs convolution operations on the features of the development functions. The convolution operation involves moving the convolution kernel and its corresponding weights within various input development functions according to a set convolution stride. Each time the convolution kernel moves, it calculates a set of feature data based on the data in each development function. After the convolutional layer extracts the features of various development functions, the extracted development function features are processed by the activation function, and the development function features processed by the activation function are transmitted to the pooling layer. The pooling layer downsamples the features to reduce the feature dimensionality. By continuously stacking convolutional and pooling layers, features are extracted from various development functions input until the feature extraction converges, the convolution stops, and the extracted features are aggregated and input into the fully connected layer. Finally, the fully connected layer integrates the extracted features and outputs the features of various development functions.

7. The cross-team software development collaborative optimization method according to claim 1, characterized in that, Step S4 includes the following steps: S41. Based on the processed software requirements data, tasks are assigned to collaborators in multiple software development teams. S42. Based on task allocation results, construct a development collaboration model and wireless network for development collaboration; After the tasks are assigned, the development tasks of each collaborator are identified and similarity analyzed based on the development collaboration model and wireless network; During the analysis process, a buffer cache is set for each task. When accessing the corresponding buffer cache, if the buffer is not locked, the first collaborator will lock it first and then enter the corresponding buffer cache. Once the buffer cache is locked, the permissions of other collaborators are set to read-only access. At the same time, the development tasks of each collaborator are identified and similarity analyzed through the read-only access of other collaborators. When you are finished processing and want to leave the cache, the cache will be unlocked. S43. Synchronize the development collaboration process of each collaborator; Set a synchronization period for the development collaboration process, and upload and synchronize the development collaboration process of each collaborator based on the set synchronization period.

8. The cross-team software development collaborative optimization method according to claim 1, characterized in that, Step S5 includes the following steps: S51. Verify the development collaboration process of each collaborator based on the processed information source software requirements data and security testing rules; Based on the collaborative awareness rules and security testing rules in step S22, analyze the development collaboration process of each collaborator after uploading; Based on context awareness and similarity functions, the development collaboration process of each collaborator is analyzed after uploading, the similarity of the context is determined, and the development collaboration process of each collaborator is verified according to the similarity judgment results. Similarity is set as verification passed, and dissimilarity is set as verification failed. S52. After successful verification, optimize the development collaboration process for each collaborator.

9. The cross-team software development collaborative optimization method according to claim 8, characterized in that, Step S52 includes the following steps: Real-time monitoring of the bandwidth status of the wireless network during the collaborative development process among various collaborators; Calculate the data transmission rate between collaborators in the development collaboration process based on the bandwidth status of the wireless network. The formula for calculating data transmission rate is as follows: ； in, This indicates the maximum data transmission rate for the current road segment. This indicates the bandwidth of the current road segment being collected. Indicates data symbols; Calculate data synchronization time based on the maximum data transmission rate and the distance between the development collaboration processes of various collaborators; The formula for calculating data synchronization time is as follows: ; in, Indicates the data synchronization time. This indicates the current data transmission rate of the wireless network. This indicates the distance between the collaborative development processes of each collaborator. This indicates the speed at which data is transmitted over the network. Indicates the time of data transmission process; Based on the calculated data synchronization time, the development collaboration process of each collaborator is optimized and compensated through CPU clock synchronization.

10. A system for implementing the cross-team software development collaborative optimization method of claim 1, characterized in that, include: The system includes a data acquisition module, a data processing module, a development collaboration module, a collaboration synchronization module, a display unit, and a collaboration synchronization optimization module. The data acquisition module is used to collect software requirement data from information sources and language libraries from multiple software development teams. The data processing module is used to process the collected information source software requirement data and the language libraries of multiple software development teams; The development collaboration module is used to construct a development collaboration model based on collaboration awareness rules, security testing rules, and the processed language library. The collaborative synchronization module is used to synchronize the development collaboration process of each collaborator based on the constructed development collaboration model, the processed software requirement data, and the wireless network. The collaborative synchronization optimization module is used to verify and optimize the development collaboration process of each collaborator using the processed information source software requirement data and security test rules; The display unit can display the development collaboration process of each collaborator in real time.

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