Crowdsourcing test task allocation method and system based on knowledge graph and personnel network

Abstract

The present invention discloses a crowdsourcing test task allocation method and system based on knowledge graph and personnel network, which belongs to the field of software testing technology, including: based on software testing tasks, establishing a knowledge graph with requirements as nodes, evaluating the priority and complexity of software testing tasks according to the position and importance of requirements in the software system, and generating evaluation results; based on the collaborative relationship and work allocation of project members, building a personnel network, obtaining the internal communication and cooperation mode of the team, identifying the professional fields and skills of members, and generating identification results; based on the crowdsourcing concept, according to the evaluation results and identification results, by comprehensively considering the nature, difficulty and professional skills of the tasks, the potential of the teams participating in the software testing tasks is maximized. The present invention can effectively improve the quality and efficiency of testing and ensure the quality of software development.

Description

Technical Field

[0001] The present invention relates to the field of software testing technology, and in particular to the allocation of crowdsourcing testing tasks, and specifically to a method and system for allocating crowdsourcing testing tasks based on a knowledge graph and a personnel network. Background Art

[0002] Traditional test task allocation methods have some shortcomings in dealing with team diversity and task complexity. First, conventional methods often ignore the analysis of requirements, resulting in a lack of comprehensive understanding of the dependencies and relative importance between requirements when allocating test tasks. This will limit the priority assessment and complexity analysis of test tasks, making task allocation lack of personalization and flexibility.

[0003] Second, existing methods also have shortcomings in personnel network analysis. They usually only focus on the simple collaborative relationship between members, but ignore the deeper professional fields and skill differences within the team. This superficial analysis makes it difficult to fully understand the potential contributions and applicable fields of each team member, which affects the intelligent allocation of tasks.

[0004] Those skilled in the art can clearly see that the shortcomings of these traditional methods are the lack of a comprehensive method and the inability to fully integrate demand knowledge and personnel network information for test task allocation. Therefore, a novel method is needed to make up for these shortcomings and improve the intelligence and consistency of test task allocation. Summary of the invention

[0005] In order to solve the above problems, the purpose of the present invention is to provide a crowdsourcing test task allocation technology based on knowledge graph and personnel network to solve the problems of intelligence and consistency in test task allocation.

[0006] In order to achieve the above technical objectives, this application provides a crowdsourcing test task allocation method based on knowledge graph and personnel network, including the following steps:

[0007] Based on software testing tasks, a knowledge graph is established with requirements as nodes. According to the position and importance of requirements in the software system, the priority and complexity of software testing tasks are evaluated to generate evaluation results.

[0008] Based on the collaborative relationships and work allocation of project members, build a personnel network, obtain the communication and cooperation patterns within the team, identify the professional fields and skills of members, and generate identification results;

[0009] Based on the crowdsourcing concept, according to the evaluation results and identification results, task allocation is carried out by comprehensively considering the nature, difficulty and professional skills of the tasks, so as to maximize the potential of the teams participating in the software testing tasks.

[0010] Preferably, in the process of building a knowledge graph, key information is extracted from the requirement document through text mining and semantic analysis to obtain the relationship between requirements, and the relationship is organized and queried using graph database technology to build a dynamic and scalable knowledge graph.

[0011] Preferably, in the process of extracting key information, natural language processing (NLP) is used to perform text mining and semantic analysis on the requirement document to extract key information.

[0012] Preferably, in the process of obtaining the relationship between requirements, the relevance, dependency and priority between requirements are taken as the relationship between requirements, and Neo4j technology is used to organize and query the relationship.

[0013] Preferably, in the process of building a personnel network, based on social network analysis SNA, by analyzing data from project collaboration platforms, version control systems and communication tools, a personnel network is built to reveal the relationships, cooperation frequencies and professional fields among project team members.

[0014] Preferably, in the process of obtaining the recognition results, visualization and analysis are performed based on the personnel network through the Gephi toolkit to analyze and optimize the team's collaboration structure.

[0015] Preferably, in the process of task assignment, predictions are made based on the evaluation results and recognition results based on the machine learning algorithm ML to find the most matching person, and the greedy algorithm is used to optimize the machine learning algorithm ML to solve the combinatorial optimization problem of task assignment. At the same time, the CrowdFlower tool is used to achieve large-scale distribution and collection of tasks.

[0016] Preferably, after the task allocation process, a continuous update and optimization mechanism is introduced to ensure that the knowledge graph and personnel network are synchronized with the actual situation, and regular updates are performed to adapt to project development and team changes, ensuring that the test task allocation is consistent with project requirements and team capabilities.

[0017] Preferably, in the process of introducing the continuous update and optimization mechanism, continuous integration CI and continuous deployment CD technologies are adopted, the version control system Git is used to track changes in code and requirements, and the reallocation of test tasks is automatically triggered. At the same time, monitoring tools and feedback systems are set up to detect and correct deviations in task allocation.

[0018] The present invention discloses a crowdsourcing test task allocation system based on knowledge graph and personnel network, comprising:

[0019] The evaluation module is used to establish a knowledge graph based on software testing tasks and requirements as nodes, evaluate the priority and complexity of software testing tasks according to the position and importance of requirements in the software system, and generate evaluation results;

[0020] The identification module is used to build a personnel network based on the collaborative relationship and work distribution of project members, obtain the communication and cooperation mode within the team, identify the professional fields and skills of members, and generate identification results;

[0021] The allocation module is used to allocate tasks based on the crowdsourcing concept, according to the evaluation results and identification results, by comprehensively considering the nature, difficulty and professional skills of the tasks, so as to maximize the potential of the teams participating in the software testing tasks.

[0022] The present invention discloses the following technical effects:

[0023] By deeply analyzing the consistency between the demand knowledge graph and the personnel network, the present invention can accurately assign test tasks to testers with corresponding capabilities and experience.

[0024] The present invention can deeply explore and accurately understand user needs by constructing and analyzing the demand knowledge graph, while the personnel network can accurately locate the most suitable personnel to perform specific tasks, and through the deep consistency analysis of the two, it can accurately find the personnel who best meet user needs and ensure that the test tasks can be accurately assigned to them. This method not only significantly improves the completion speed of test tasks, but also ensures the improvement of test quality, because the test tasks are accurately matched to the most suitable testers.

[0025] The present invention incorporates a crowdsourcing approach, which effectively utilizes the wisdom and power of the public, so that test tasks can be completed quickly and timely feedback can be obtained. The crowdsourcing model uses a network platform to widely distribute work tasks to the public in the network, and completes the tasks through their joint collaboration. Through crowdsourcing, the present invention can collect a large amount of test results and feedback in a very short period of time, which greatly promotes the rapid optimization of products and services by the present invention. This method not only significantly improves the execution efficiency of test tasks, but also greatly speeds up the acquisition speed of feedback information, so that the present invention can more quickly optimize products and services based on feedback to meet market demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 is a crowdsourcing test task allocation flow chart of the present invention;

[0028] Figure 2 It is a flow chart of constructing the knowledge graph described in the present invention;

[0029] Figure 3 is a flow chart of social network analysis according to the present invention;

[0030] Figure 4 is a crowdsourcing test task distribution flow chart of the present invention;

[0031] Figure 5 It is a schematic diagram of the method steps described in the present invention. DETAILED DESCRIPTION

[0032] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0033] like Figure 1-5 As shown, the present invention provides a crowdsourcing test task allocation method that analyzes consistency through knowledge graph and personnel network. The consistency of crowdsourcing test task allocation is ensured by constructing a knowledge graph (demand knowledge graph) for characterizing demand relationships and performing personnel network analysis. The steps of the method are as follows:

[0034] By constructing graph-structured data, a requirement knowledge graph is established with requirements as nodes to fully display the relationships between requirements, including dependencies, to accurately reflect their position and importance in the software system, thereby effectively evaluating the priority and complexity of testing tasks.

[0035] In-depth analysis of project members' collaborative relationships and work distribution, building a personnel network diagram, revealing the communication and cooperation patterns within the team, identifying members' professional fields and skills, and providing key clues for test task allocation.

[0036] Based on the concept of crowdsourcing, intelligent task allocation strategies are formulated according to the demand knowledge graph and personnel network analysis results. The nature, difficulty and professional skills of the tasks are comprehensively considered to achieve personalized task allocation, maximize the team's potential and improve testing efficiency.

[0037] Introduce a continuous update and optimization mechanism to ensure that the demand knowledge map and personnel network are synchronized with the actual situation. Through regular updates, adapt to project development and team changes, and ensure that the test task allocation is consistent with project requirements and team capabilities.

[0038] In the stage of building the demand knowledge graph, NLP technology is used to extract key information from the demand documents through text mining and semantic analysis technology, including the correlation, dependency and priority between requirements; graph database technology, such as Neo4j, is used to effectively organize and query these relationships and build a dynamic and scalable knowledge graph.

[0039] In the personnel network analysis stage, social network analysis (SNA) technology is used to construct a personnel network diagram by analyzing the data of the project collaboration platform, version control system and communication tools to reveal the relationships, cooperation frequency and professional fields among project team members; the present invention uses the Gephi toolkit for visualization and analysis to help understand and optimize the team's collaboration structure.

[0040] During the test task allocation phase, machine learning (ML) technology and optimization algorithms are introduced. The ML model makes predictions based on the execution data of previous tasks and the skills of team members to find the most suitable personnel. The optimization algorithm uses a greedy algorithm to solve the combinatorial optimization problem of task allocation. The CrowdFlower tool is used to achieve large-scale distribution and collection of tasks.

[0041] Adopt CI and CD technologies, use the version control system Git to track changes in code and requirements, and automatically trigger the reallocation of test tasks; in terms of knowledge graphs and personnel networks, implement regular data updates and automated optimization algorithms to ensure that the latest status of projects and teams is reflected in real time; monitoring tools and feedback systems are used to detect and correct deviations in task allocation.

[0042] Embodiment 1: A crowdsourcing test task allocation method for consistency between a demand knowledge graph and a personnel network analysis provided by the present invention is intended to improve the efficiency and effectiveness of software testing and ensure that the test task is assigned to the most suitable person. The method includes the following processes: first, construct a demand knowledge graph, which presents the relationship between requirements in the form of graph-structured data; second, construct a personnel network graph to identify the collaboration and work allocation relationship between project members; then, analyze the consistency between the demand knowledge graph and the personnel network, and find the tester who best meets the user's needs by calculating the similarity between the demand entity and the personnel entity, the similarity between the demand relationship and the personnel relationship, and the similarity between the demand attribute and the personnel node; next, using crowdsourcing, assign the test task to the most suitable person according to the nature and difficulty of the test task, as well as the professional skills and collaborative relationship of the project members; finally, through the continuous updating and optimization of the demand knowledge graph and the personnel network analysis, the consistency of the test task allocation is guaranteed. Through the method of the present invention, the quality and efficiency of testing can be effectively improved, and the quality of software development can be ensured.

[0043] Specifically, the present invention proposes a crowdsourcing test task allocation method based on the consistency of demand knowledge graph and personnel network analysis by comprehensively using natural language processing (NLP), social network analysis (SNA), machine learning (ML), continuous integration (CI) and continuous deployment (CD) technologies, which specifically includes the following steps:

[0044] Step 1: Build a demand knowledge graph:

[0045] At this stage, the present invention uses the graph model, takes requirements as nodes, and builds a knowledge graph to show the relationship between different requirements. The knowledge graph not only reflects the dependencies between requirements, but also includes their position and importance in the entire software system. Through a deep understanding of requirements, the priority and complexity of test tasks can be better evaluated.

[0046] Step 2: Conduct personnel network analysis:

[0047] At this stage, the present invention constructs a personnel network diagram by analyzing the collaborative relationship and work distribution among project members. The diagram reveals the communication and cooperation mode within the team and identifies the professional fields and skills of team members. This helps to understand the potential contribution and applicable fields of each member, and provides important clues and basis for the allocation of test tasks.

[0048] Step 3: Use crowdsourcing to distribute test tasks:

[0049] Based on the results of demand knowledge graph and personnel network analysis, the present invention uses the concept of crowdsourcing to develop an intelligent task allocation strategy. Considering the nature and difficulty of the test task, as well as the professional skills and collaborative relationships of the project members, the present invention can allocate each test task to the most suitable team member. This personalized allocation method can maximize the potential of the team and improve testing efficiency.

[0050] Step 4: Ensure consistency through continuous updates and optimization:

[0051] In order to maintain the consistency of test task allocation, the present invention introduces a continuous update and optimization mechanism for demand knowledge graph and personnel network analysis. As the project develops and team members change, the knowledge graph and personnel network are regularly updated to ensure that they are synchronized with the actual situation. This continuous optimization process helps to adapt to changes and ensure that test task allocation always meets project requirements and team capabilities.

[0052] Among the specific key technologies involved, NLP technology is used to build a demand knowledge graph, and key information is extracted from demand documents through text mining and semantic analysis technology, including the relevance, dependency and priority between requirements. Graph database technology, such as Neo4j, is used to effectively organize and query these relationships and build a dynamic and scalable knowledge graph.

[0053] In the personnel network analysis phase, social network analysis (SNA) technology is used to construct a personnel network diagram by analyzing the data of the project collaboration platform, version control system and communication tools to reveal the relationship, cooperation frequency and professional field among project team members. The present invention uses the Gephi toolkit for visualization and analysis to help understand and optimize the team's collaboration structure.

[0054] In the test task allocation stage, the present invention introduces machine learning (ML) technology and optimization algorithms. The ML model predicts based on the execution data of previous tasks and the skills of team members to find the most suitable personnel. The optimization algorithm adopts a greedy algorithm to solve the combinatorial optimization problem of task allocation. The CrowdFlower tool is used to achieve large-scale distribution and collection of tasks.

[0055] In order to maintain consistency, the present invention introduces CI and CD technologies. The version control system Git is used to track changes in code and requirements, and automatically trigger the reallocation of test tasks. In terms of knowledge graphs and personnel networks, regular data updates and automated optimization algorithms are implemented to ensure that the latest status of projects and teams is reflected in real time. Monitoring tools and feedback systems are used to detect and correct deviations in task allocation.

[0056] In summary, the present invention aims to overcome the shortcomings of traditional methods and provide a more comprehensive, intelligent and flexible crowdsourcing test task allocation method by integrating multiple technologies. The feasibility of this innovative method is reflected in the following aspects:

[0057] First, by constructing a knowledge graph for characterizing requirement relationships, the present invention can fully understand the relationships between requirements, including dependencies and relative importance, which provides a comprehensive information basis for priority evaluation and complexity analysis of test tasks.

[0058] Secondly, in the personnel network analysis stage, the present invention uses SNA technology to not only focus on collaborative relationships, but also deeply explore the professional fields and skills of team members. This helps to more accurately understand the contribution value of each member and provide strong support for personalized test task allocation.

[0059] Finally, the present invention introduces machine learning and optimization algorithms to intelligently assign test tasks to the most suitable team members during the task assignment phase. This personalized assignment method effectively improves test efficiency and fully taps the potential of the team.

[0060] In summary, the present invention makes up for the shortcomings of traditional methods by comprehensively applying a variety of advanced technologies, thereby having significant feasibility and innovation in test task allocation.

[0061] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A crowdsourcing test task allocation method based on knowledge graph and personnel network, characterized in that: The following steps are involved: Based on the software testing task, a knowledge graph is established with requirements as nodes, and the priority and complexity of the software testing task are evaluated according to the position and importance of the requirements in the software system, and an evaluation result is generated; Based on the collaborative relationships and work allocation of project members, build a personnel network, obtain the communication and cooperation patterns within the team, identify the professional fields and skills of members, and generate identification results; Based on the crowdsourcing concept, according to the evaluation results and the identification results, by comprehensively considering the nature, difficulty and professional skills of the tasks, the potential of the team participating in the software testing task is maximized; In the process of building a knowledge graph, key information is extracted from the requirement documents through text mining and semantic analysis to obtain the relationship between requirements. The relationship is organized and queried using graph database technology to build a dynamic and scalable knowledge graph. In the process of extracting key information, natural language processing (NLP) is used to perform text mining and semantic analysis on the requirement document to extract the key information; In the process of obtaining the relationship between requirements, the correlation, dependency and priority between requirements are used as the relationship between requirements, and Neo4j technology is used to organize and query the relationship; In the process of constructing the personnel network, based on social network analysis (SNA), the personnel network is constructed by analyzing the data of the project collaboration platform, version control system and communication tools to reveal the relationship between project team members, cooperation frequency and professional fields; In the process of obtaining the identification results, the personnel network is visualized and analyzed by using the Gephi toolkit to analyze and optimize the team's collaborative structure; In the process of task assignment, based on the machine learning algorithm ML, predictions are made according to the evaluation results and the recognition results to find the most matching personnel, and the greedy algorithm is used to optimize the machine learning algorithm ML to solve the combinatorial optimization problem of task assignment. At the same time, the CrowdFlower tool is used to achieve large-scale distribution and collection of tasks.

2. The crowdsourcing test task allocation method based on knowledge graph and personnel network according to claim 1 is characterized by: After the task allocation process, a continuous update and optimization mechanism is introduced to ensure that the knowledge graph and personnel network are synchronized with the actual situation, and regular updates are carried out to adapt to project development and team changes, ensuring that the test task allocation is consistent with project requirements and team capabilities.

3. The crowdsourcing test task allocation method based on knowledge graph and personnel network according to claim 2 is characterized by: In the process of introducing continuous update and optimization mechanisms, continuous integration CI and continuous deployment CD technologies are adopted, and the version control system Git is used to track changes in code and requirements, and automatically trigger the reallocation of test tasks. At the same time, monitoring tools and feedback systems are set up to detect and correct deviations in task allocation.

4. A crowdsourcing test task allocation system based on knowledge graph and personnel network, used to implement the crowdsourcing test task allocation method based on knowledge graph and personnel network as claimed in any one of claims 1 to 3, characterized in that: include: An evaluation module is used to establish a knowledge graph based on software testing tasks and with requirements as nodes, evaluate the priority and complexity of the software testing tasks according to the position and importance of the requirements in the software system, and generate evaluation results; The identification module is used to build a personnel network based on the collaborative relationship and work distribution of project members, obtain the communication and cooperation mode within the team, identify the professional fields and skills of members, and generate identification results; The allocation module is used to allocate tasks based on the crowdsourcing concept, according to the evaluation results and the identification results, by comprehensively considering the nature, difficulty and professional skills of the tasks, so as to maximize the potential of the team participating in the software testing task.

Images