Open source community-oriented implicit collaborative specification mining method, device and equipment

Abstract

The invention relates to an implicit collaborative specification mining method, device and equipment for an open source community. The method comprises the following steps: acquiring a collaborative scene in a contribution guide corresponding to an open source software project, and constructing a collaborative model according to a contribution behavior list in the collaborative scene and a corresponding decision behavior; acquiring a historical value set of decision behaviors and each contribution behavior in a corresponding collaborative scene from historical open source collaborative data by using a collaborative model, constructing a corresponding data table, and performing regression analysis according to the data table to obtain a correlation list in the collaborative scene; performing correlation judgment on each element in the correlation list, and when the correlation between the contribution behavior and the decision behavior meets a preset condition, generating a corresponding implicit collaborative specification statement; and inserting the implicit collaboration specification statement into the position of the collaboration scene corresponding to the contribution guide so as to perfect the collaboration specification system of the open source community. By adopting the method, the implicit collaboration specifications in different scenes can be fully and flexibly mined.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device and equipment for mining implicit collaborative specifications for an open source community. Background Art

[0002] The open source community is a community that develops, communicates, shares and collaborates around open source software projects. Members of the open source community are committed to the development, maintenance, improvement and promotion of open source software. There are significant differences in cultural background, development experience, personal habits, etc. in the open source community. In the large-scale self-organized collaborative model, establishing complete and clear collaborative norms to ensure group collaboration efficiency is a real challenge facing the open source community.

[0003] The gradual increase in the scale of open source software and the scale of participating groups has also brought about a significant increase in the complexity of program code and the complexity of the development process, which has brought severe challenges to the open source group collaboration process in an open environment. In particular, different projects often have different participation models, different quality standards, and scattered contribution specifications, which has led to a significant increase in the connection cost between the open source software management team and the external contributor group. Providing a complete and clear set of open source internal group collaboration specifications allows participants to comply with established quality standards, process guidelines, behavioral norms and other elements, which helps reduce communication barriers and improve collaboration efficiency.

[0004] However, currently the open source community mainly sets up a contribution specification document (generally named contributing.md) in the code repository and declares various specification terms in it. However, there are problems such as incomplete or outdated content. This results in some actual specifications not being reflected in the document, which not only causes information missing, but also brings obstacles to contributors, especially new participants, affecting the efficiency and quality of community collaborative development, and to a certain extent wasting developers' time and energy resources. At the same time, unclear specifications may cause some potential security risks, such as code vulnerabilities and chaotic permission management. Summary of the invention

[0005] Based on this, it is necessary to provide an implicit collaborative specification mining method, device and equipment for the open source community to address the above technical problems.

[0006] An implicit collaborative specification mining method for an open source community, the method comprising:

[0007] Obtaining a collaborative scenario in a contribution guide corresponding to an open source software project, and building a collaborative model according to a contribution behavior list and corresponding decision-making behaviors under the collaborative scenario; the contribution behavior list includes a number of contribution behaviors;

[0008] Obtain historical open source collaborative data, use the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data, and construct a corresponding data table, perform regression analysis based on the data table, and obtain a correlation list in the collaborative scenario; each element in the correlation list represents the correlation between the contribution behavior and the decision-making behavior;

[0009] The correlation relationship of each element in the correlation list is judged. When the correlation relationship between the contribution behavior and the decision-making behavior meets the preset conditions, the corresponding implicit coordination specification statement is generated;

[0010] The implicit collaboration specification statement is inserted into the location of the collaboration scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community.

[0011] In one of the embodiments, it also includes: storing the collaborative scenario name, contribution behavior list and decision behavior in the form of fields to construct a collaborative model corresponding to the collaborative scenario; each contribution behavior in the contribution behavior list is represented by a contribution behavior indicator; the contribution behavior indicator is a triple consisting of a contribution behavior name, a value type and a reading source; the value type includes continuous type or categorical type; the decision behavior is a triple consisting of a decision behavior name, a decision meaning and a reading source; the decision meaning is the physical meaning of the positive value of the decision behavior.

[0012] In one of the embodiments, it also includes: using the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data; obtaining a contribution behavior data matrix based on the historical value set of all contribution behaviors, obtaining a decision behavior data vector based on the historical value set of the decision behavior, and constructing a corresponding data table based on the contribution behavior data matrix and the decision behavior data vector.

[0013] In one of the embodiments, it also includes: obtaining a data table, preprocessing the data table to obtain a preprocessed data table; preprocessing the data table, including: performing dummy variable processing on contribution behavior indicators in the data table whose value types are categorical; inputting the preprocessed data table into a Logistic regression model to obtain the regression coefficient and significance value corresponding to each contribution behavior; and obtaining a correlation list in a collaborative scenario based on the regression coefficient and significance value corresponding to each contribution behavior.

[0014] In one of the embodiments, it also includes: traversing each element in the correlation list, if the significance value in the current element is less than the first threshold and the correlation coefficient is greater than 0, then there is a positive correlation between the contribution behavior corresponding to the current element and the decision-making behavior, and a corresponding implicit collaborative specification statement is generated; if the significance value in the current element is less than the first threshold and the correlation coefficient is less than 0, then there is a negative correlation between the contribution behavior corresponding to the current element and the decision-making behavior, and a corresponding implicit collaborative specification statement is generated.

[0015] In one of the embodiments, it also includes: the implicit collaboration specification statement is obtained according to the contribution behavior name, the decision behavior name and the correlation relationship judgment result.

[0016] An implicit collaborative specification mining device for an open source community, the device comprising:

[0017] A collaborative model building module, used to obtain collaborative scenarios in the contribution guide corresponding to the open source software project, and build a collaborative model according to the contribution behavior list and corresponding decision-making behaviors under the collaborative scenario; the contribution behavior list includes several contribution behaviors;

[0018] A regression analysis module is used to obtain historical open source collaborative data, use the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data, and construct a corresponding data table, perform regression analysis based on the data table, and obtain a correlation list in the collaborative scenario; each element in the correlation list represents the correlation between the contribution behavior and the decision-making behavior;

[0019] The implicit coordination specification generation module is used to judge the correlation relationship of each element in the correlation list, and generate the corresponding implicit coordination specification statement when the correlation relationship between the contribution behavior and the decision-making behavior meets the preset conditions;

[0020] The specification system improvement module is used to insert the implicit collaboration specification statement into the location of the collaboration scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community.

[0021] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0022] Obtaining a collaborative scenario in a contribution guide corresponding to an open source software project, and building a collaborative model according to a contribution behavior list and corresponding decision-making behaviors under the collaborative scenario; the contribution behavior list includes a number of contribution behaviors;

[0023] Obtain historical open source collaborative data, use the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data, and construct a corresponding data table, perform regression analysis based on the data table, and obtain a correlation list in the collaborative scenario; each element in the correlation list represents the correlation between the contribution behavior and the decision-making behavior;

[0024] The correlation relationship of each element in the correlation list is judged. When the correlation relationship between the contribution behavior and the decision-making behavior meets the preset conditions, the corresponding implicit coordination specification statement is generated;

[0025] The implicit collaboration specification statement is inserted into the location of the collaboration scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community.

[0026] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0027] Obtaining a collaborative scenario in a contribution guide corresponding to an open source software project, and building a collaborative model according to a contribution behavior list and corresponding decision-making behaviors under the collaborative scenario; the contribution behavior list includes a number of contribution behaviors;

[0028] Obtain historical open source collaborative data, use the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data, and construct a corresponding data table, perform regression analysis based on the data table, and obtain a correlation list in the collaborative scenario; each element in the correlation list represents the correlation between the contribution behavior and the decision-making behavior;

[0029] The correlation relationship of each element in the correlation list is judged. When the correlation relationship between the contribution behavior and the decision-making behavior meets the preset conditions, the corresponding implicit coordination specification statement is generated;

[0030] The implicit collaboration specification statement is inserted into the location of the collaboration scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community.

[0031] The above-mentioned implicit collaboration specification mining method, device and equipment for open source communities can realize diversified scenario modeling by constructing corresponding collaboration models under collaborative scenarios. Then, historical open source collaboration data is obtained, and the collaboration model is used to obtain the historical value set of decision-making behavior and each contribution behavior under the corresponding collaborative scenario from the historical open source collaboration data, and regression analysis is performed based on the historical values. Correlation judgment is performed according to the regression analysis results. When the correlation between the contribution behavior and the decision-making behavior meets the preset conditions, the corresponding implicit collaboration specification statement is generated, which can flexibly adapt to multiple data sources and realize the dynamic generation of implicit specifications that meet the characteristics of the current scenario. Finally, the implicit collaboration specification statement is inserted into the location of the collaborative scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community, which is conducive to improving the completeness of the contribution guide of the open source software project and providing behavioral guidance for the open source contribution practice. The embodiments of the present invention have high flexibility and scalability and can fully mine implicit collaboration specifications under different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of an implicit collaborative specification mining method for an open source community in one embodiment;

[0033] Figure 2 is a structural block diagram of an implicit collaborative specification mining device for an open source community in one embodiment;

[0034] Figure 3 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] In one embodiment, Figure 1 As shown, a method for mining implicit collaborative specifications for open source communities is provided, including the following steps:

[0037] Step 102, obtaining the collaboration scenario in the contribution guide corresponding to the open source software project, and building a collaboration model according to the contribution behavior list and the corresponding decision-making behavior in the collaboration scenario.

[0038] The contribution guide of an open source software project is usually a document written by the development team, which clarifies the code of conduct and operating procedures of contributors in the collaboration process. The collaboration model clarifies the contribution behavior and decision-making behavior of participants by describing specific collaboration scenarios in a structured manner. Open source collaboration usually involves multiple dimensions of contribution behaviors, which directly or indirectly affect the final decision. In the collaboration model, the contribution behavior list includes several contribution behaviors.

[0039] It can be understood that collaborative scenario modeling breaks down complex collaborative behaviors into specific scenarios, allowing the model to handle each scenario independently, and new collaborative scenarios can be quickly incorporated into the analysis framework through simple model extensions.

[0040] Step 104, obtain historical open source collaborative data, use the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data, and construct a corresponding data table, perform regression analysis based on the data table, and obtain a correlation list in the collaborative scenario.

[0041] Historical open source group collaboration data refers to historical data that records group collaboration behaviors in the open source community, including various activities and decision records generated by open source community members during project development and collaboration, reflecting the actual operation of the open source community in collaborative development. Through the collaborative model, historical data can be mapped to the model to analyze the relationship between contribution behavior and decision-making behavior, and obtain a correlation list. Each element in the correlation list represents the correlation between contribution behavior and decision-making behavior.

[0042] By analyzing collaborative data, we can reveal implicit rules that are not covered by existing contribution guidelines, thereby improving and optimizing contribution guidelines and increasing the collaboration efficiency and standardization level of the open source community. In addition, data from different projects or time periods can reflect different collaboration modes and dynamically generate implicit specifications that meet the characteristics of the current scenario.

[0043] Step 106, performing correlation judgment on each element in the correlation list, and generating a corresponding implicit collaboration specification statement when the correlation between the contribution behavior and the decision-making behavior meets a preset condition.

[0044] Implicit collaboration norms are mined through statistical analysis to optimize the collaboration process of open source projects.

[0045] Step 108: insert the implicit collaboration specification statement into the location of the collaboration scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community.

[0046] The mined implicit norms can be fed directly into the collaboration process, helping teams collaborate more efficiently by updating contribution guidelines.

[0047] In the above-mentioned implicit collaboration specification mining method for the open source community, by constructing the corresponding collaboration model under the collaboration scenario, diversified scenario modeling can be achieved. Then, historical open source collaboration data is obtained, and the collaboration model is used to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaboration scenario from the historical open source collaboration data, and the historical values ​​are regressed from the perspective of quantitative statistics. The correlation is judged according to the regression analysis results. When the correlation between the contribution behavior and the decision-making behavior meets the preset conditions, the corresponding implicit collaboration specification statement is generated, which can flexibly adapt to a variety of data sources and realize the dynamic generation of implicit specifications that meet the characteristics of the current scenario. Finally, the implicit collaboration specification statement is inserted into the location of the collaboration scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community, which is conducive to improving the completeness of the contribution guide of the open source software project and providing behavioral guidance for the open source contribution practice. The embodiment of the present invention has high flexibility and scalability and can fully mine implicit collaboration specifications in different scenarios.

[0048] In one embodiment, a collaborative model is constructed according to the contribution behavior list and the corresponding decision behavior in the collaborative scenario, including: storing the collaborative scenario name, contribution behavior list and decision behavior in the form of fields to construct a collaborative model of the corresponding collaborative scenario; each contribution behavior in the contribution behavior list is represented by a contribution behavior indicator; the contribution behavior indicator is a triple consisting of a contribution behavior name, a value type and a reading source; the value type includes a continuous type or a categorical type; the decision behavior is a triple consisting of a decision behavior name, a decision meaning and a reading source; the decision meaning is the physical meaning of the positive value of the decision behavior.

[0049] Specifically, first, typical contribution scenarios are selected from the existing contribution guides of open source software projects, such as submitting code merge requests, reporting software issues, etc. Then, in each collaboration scenario, a collaboration model as shown below is constructed to characterize the open source contribution behaviors and decision-making behaviors involved in the scenario.

[0050] {

[0051] context:C_name;

[0052] action: [(A1_name, A1_type, A1_source), ..., (A n _name,A n _type, A n _source)];

[0053] decision: (D_name, D_meaning, D_source)

[0054] }

[0055] Among them, "context" is used to represent the scenario name, and the "action" field represents a list of contribution behaviors. Each item in the list represents an indicator related to the open-source contribution behavior, which is represented by a triple, respectively representing the name, type, and value source of the contribution behavior indicator. For example, the i-th contribution behavior indicator A i is represented by (A i _name, A i _type, A i _source). Among them, the values of the contribution behavior indicators are of two types, namely continuous type and categorical type. The value source indicates where to read the value of the contribution behavior indicator, such as a database or a local file, etc. "decision" represents the decision-making behavior D of the core management team related to the open-source contribution behavior, which is also represented by a triple, respectively representing the decision name, decision meaning, and value source. The value of the decision-making behavior is of a binary type, and the decision meaning indicates the physical meaning represented when the decision-making behavior takes a positive value. The value source indicates where to read the value of the decision.

[0056] In one embodiment, a collaborative model is used to obtain the historical value sets of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open-source collaborative data, and a corresponding data table is constructed, including: using the collaborative model to obtain the historical value sets of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open-source collaborative data; obtaining a contribution behavior data matrix according to the historical value sets of all contribution behaviors, obtaining a decision-making behavior data vector according to the historical value set of the decision-making behavior, and constructing a corresponding data table according to the contribution behavior data matrix and the decision-making behavior data vector.

[0057] Specifically, the correlation between the contribution behavior and the decision-making behavior is analyzed using a Logistic regression model. First, according to the defined open-source collaborative scenario model, the measurement values of each contribution behavior and the decision-making behavior are obtained from the historical open-source group collaborative data, obtaining a contribution behavior data matrix a and a decision-making behavior data vector d. Each element a_i (0 < i < n; i is the number of contribution behaviors) of a represents the historical value set of the i-th contribution behavior indicator A i , and d represents the historical measurement value set of the decision-making behavior. The dimensions of a_i and d are the same. A data table (a_1, a_2,..., a_n, d) is constructed based on the vectors a and d.

[0058] In one embodiment, regression analysis is performed based on a data table to obtain a list of correlations in a collaborative scenario, including: obtaining a data table, preprocessing the data table to obtain a preprocessed data table; preprocessing the data table, including: performing dummy variable processing on contribution behavior indicators in the data table whose value types are categorical; inputting the preprocessed data table into a Logistic regression model to obtain a regression coefficient and a significance value corresponding to each contribution behavior; and obtaining a list of correlations in a collaborative scenario based on the regression coefficient and the significance value corresponding to each contribution behavior.

[0059] Specifically, the data table is input into the Logistic regression model for fitting. For the behavioral indicators with categorical readings, dummy variable processing is required before fitting. After the regression analysis is completed, the regression coefficient and significance value corresponding to each behavioral indicator are extracted from the analysis results, and a correlation list C (<β1, p1>, <β2, p2>, ..., <β n ,p n >), where <β i ,p i >Corresponding contribution behavior A i and the correlation with decision-making behavior D.

[0060] In one embodiment, a correlation judgment is performed on each element in the correlation list, and when the correlation between the contribution behavior and the decision-making behavior meets the preset conditions, a corresponding implicit collaborative specification statement is generated, including: traversing each element in the correlation list, if the significance value in the current element is less than the first threshold and the correlation coefficient is greater than 0, then there is a positive correlation between the contribution behavior corresponding to the current element and the decision-making behavior, and a corresponding implicit collaborative specification statement is generated; if the significance value in the current element is less than the first threshold and the correlation coefficient is less than 0, then there is a negative correlation between the contribution behavior corresponding to the current element and the decision-making behavior, and a corresponding implicit collaborative specification statement is generated.

[0061] Specifically, based on the dependency list C, an implicit coordination specification is generated. Specifically, for each element < β in C i ,p i >, when p i The value of is less than 0.05 and β i When the value of is greater than 0, a standard statement is generated: "There is a potential positive correlation between contribution behavior A1_name and decision behavior D_name". i The value of is less than 0.05 and β i When the value of is less than 0, a standard statement is generated: "There is a potential negative correlation between the contribution behavior A1_name and the decision behavior D_name". The generated statements are inserted one by one into the paragraph where the collaborative scenario C_name is located in the open source software project contribution guide.

[0062] In one embodiment, the implicit collaboration specification statement is obtained according to the contribution behavior name, the decision behavior name and the correlation relationship judgment result.

[0063] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0064] In one embodiment, Figure 2 As shown, an implicit collaborative specification mining device for an open source community is provided, comprising:

[0065] A collaborative model building module 202 is used to obtain a collaborative scenario in a contribution guide corresponding to an open source software project, and to build a collaborative model according to a contribution behavior list and corresponding decision-making behaviors under the collaborative scenario; the contribution behavior list includes a plurality of contribution behaviors;

[0066] The regression analysis module 204 is used to obtain historical open source collaborative data, use the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data, and construct a corresponding data table, perform regression analysis based on the data table, and obtain a correlation list in the collaborative scenario; each element in the correlation list represents the correlation between the contribution behavior and the decision-making behavior;

[0067] The implicit collaboration specification generation module 206 is used to perform a correlation judgment on each element in the correlation list, and when the correlation between the contribution behavior and the decision-making behavior meets the preset conditions, generate a corresponding implicit collaboration specification statement;

[0068] The specification system improvement module 208 is used to insert the implicit collaboration specification statement into the location of the collaboration scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community.

[0069] In one of the embodiments, it is also used to store the collaborative scenario name, contribution behavior list and decision behavior in the form of fields to construct a collaborative model of the corresponding collaborative scenario; each contribution behavior in the contribution behavior list is represented by a contribution behavior indicator; the contribution behavior indicator is a triple consisting of a contribution behavior name, a value type and a reading source; the value type includes continuous type or categorical type; the decision behavior is a triple consisting of a decision behavior name, a decision meaning and a reading source; the decision meaning is the physical meaning of the positive value of the decision behavior.

[0070] In one of the embodiments, it is also used to use the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data; based on the historical value set of all contribution behaviors, a contribution behavior data matrix is ​​obtained, and based on the historical value set of the decision behavior, a decision behavior data vector is obtained; and a corresponding data table is constructed based on the contribution behavior data matrix and the decision behavior data vector.

[0071] In one of the embodiments, it is also used to obtain a data table, preprocess the data table, and obtain a preprocessed data table; preprocess the data table, including: performing dummy variable processing on contribution behavior indicators with categorical value types in the data table; inputting the preprocessed data table into a Logistic regression model to obtain the regression coefficient and significance value corresponding to each contribution behavior; and obtaining a correlation list in a collaborative scenario based on the regression coefficient and significance value corresponding to each contribution behavior.

[0072] In one of the embodiments, it is also used to traverse each element in the correlation list. If the significance value in the current element is less than the first threshold and the correlation coefficient is greater than 0, there is a positive correlation between the contribution behavior corresponding to the current element and the decision-making behavior, and a corresponding implicit collaborative specification statement is generated; if the significance value in the current element is less than the first threshold and the correlation coefficient is less than 0, there is a negative correlation between the contribution behavior corresponding to the current element and the decision-making behavior, and a corresponding implicit collaborative specification statement is generated.

[0073] In one of the embodiments, the implicit collaboration specification statement is obtained based on the contribution behavior name, the decision behavior name and the correlation relationship judgment result.

[0074] For the specific limitations of the implicit collaborative specification mining device for open source communities, please refer to the limitations of the implicit collaborative specification mining method for open source communities above, which will not be repeated here. Each module in the above-mentioned implicit collaborative specification mining device for open source communities can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0075] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an implicit collaborative specification mining method for an open source community is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0076] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0077] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0078] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0079] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0080] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for mining implicit collaborative norms for open source communities, characterized in that: The method comprises: Obtaining a collaborative scenario in a contribution guide corresponding to an open source software project, and building a collaborative model according to a contribution behavior list and corresponding decision-making behaviors under the collaborative scenario; the contribution behavior list includes a number of contribution behaviors; Obtain historical open source collaborative data, use the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data, and construct a corresponding data table, perform regression analysis based on the data table, and obtain a correlation list in the collaborative scenario; each element in the correlation list represents the correlation between the contribution behavior and the decision-making behavior; The correlation relationship of each element in the correlation list is judged. When the correlation relationship between the contribution behavior and the decision-making behavior meets the preset conditions, the corresponding implicit coordination specification statement is generated; The implicit collaboration specification statement is inserted into the location of the collaboration scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community.

2. The method according to claim 1, characterized in that A collaborative model is constructed according to the contribution behavior list and the corresponding decision-making behavior in the collaborative scenario, including: The collaborative scenario name, contribution behavior list, and decision behavior are stored in the form of fields to build a collaborative model corresponding to the collaborative scenario; Each contribution behavior in the contribution behavior list is represented by a contribution behavior indicator; the contribution behavior indicator is a triple consisting of a contribution behavior name, a value type, and a reading source; the value type includes a continuous type or a categorical type; The decision behavior is a triple consisting of a decision behavior name, a decision meaning, and a reading source; the decision meaning is the physical meaning of the positive value of the decision behavior.

3. The method according to claim 1, characterized in that The collaborative model is used to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data, and a corresponding data table is constructed, including: Using the collaborative model to obtain a historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data; According to the historical value set of all contribution behaviors, a contribution behavior data matrix is ​​obtained, and according to the historical value set of decision-making behaviors, a decision-making behavior data vector is obtained. According to the contribution behavior data matrix and the decision-making behavior data vector, a corresponding data table is constructed.

4. The method according to claim 1, characterized in that: Perform regression analysis based on the data table to obtain a list of correlations in the collaborative scenario, including: Obtain a data table, preprocess the data table, and obtain a preprocessed data table; preprocess the data table, including: performing dummy variable processing on contribution behavior indicators whose value types are classified in the data table; Input the preprocessed data table into the Logistic regression model to obtain the regression coefficient and significance value corresponding to each contribution behavior; According to the regression coefficient and significance value corresponding to each contribution behavior, a list of correlations in the collaborative scenario is obtained.

5. The method according to claim 1, characterized in that The correlation relationship of each element in the correlation list is judged. When the correlation relationship between the contribution behavior and the decision-making behavior meets the preset conditions, the corresponding implicit coordination specification statement is generated, including: Traverse each element in the correlation list. If the significance value in the current element is less than the first threshold and the correlation coefficient is greater than 0, there is a positive correlation between the contribution behavior and the decision-making behavior corresponding to the current element, and generate the corresponding implicit coordination specification statement; If the significance value in the current element is less than the first threshold and the correlation coefficient is less than 0, there is a negative correlation between the contribution behavior and the decision behavior corresponding to the current element, and a corresponding implicit collaborative specification statement is generated.

6. The method according to claim 1, characterized in that The implicit collaboration specification statement is obtained based on the contribution behavior name, decision behavior name and related relationship judgment results.

7. An implicit collaborative specification mining device for open source communities, characterized in that: The device comprises: A collaborative model building module, used to obtain collaborative scenarios in the contribution guide corresponding to the open source software project, and build a collaborative model according to the contribution behavior list and corresponding decision-making behaviors under the collaborative scenario; the contribution behavior list includes several contribution behaviors; A regression analysis module is used to obtain historical open source collaborative data, use the collaborative model to obtain the historical value set of the decision-making behavior and each contribution behavior in the corresponding collaborative scenario from the historical open source collaborative data, and construct a corresponding data table, perform regression analysis based on the data table, and obtain a correlation list in the collaborative scenario; each element in the correlation list represents the correlation between the contribution behavior and the decision-making behavior; The implicit coordination specification generation module is used to judge the correlation relationship of each element in the correlation list, and generate the corresponding implicit coordination specification statement when the correlation relationship between the contribution behavior and the decision-making behavior meets the preset conditions; The specification system improvement module is used to insert the implicit collaboration specification statement into the location of the collaboration scenario corresponding to the contribution guide to improve the collaboration specification system of the open source community.

8. The device according to claim 7, characterized in that The collaborative model construction module is also used to store the collaborative scenario name, contribution behavior list and decision behavior in the form of fields to construct a collaborative model corresponding to the collaborative scenario; each contribution behavior in the contribution behavior list is represented by a contribution behavior indicator; the contribution behavior indicator is a triple consisting of a contribution behavior name, a value type and a reading source; the value type includes continuous type or categorical type; the decision behavior is a triple consisting of a decision behavior name, a decision meaning and a reading source; the decision meaning is the physical meaning of the positive value of the decision behavior.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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