CN112418803B - A crowdsourcing tester recruitment method based on social network - Google Patents

Abstract

The present invention provides a method for recruiting crowdsourcing testers based on social networks, including four stages: task segmentation, task release and dissemination, dissemination network construction, and profit distribution. First, the crowd-testing task is segmented into small tasks that can be completed by a single tester. Then the task is released to the crowd-sourcing test platform so that the task information is disseminated in the social network. Secondly, the invitation code technology is used to record the superiors of each tester, thereby forming a dissemination network. Finally, in the profit distribution stage, the profit is distributed according to the dissemination network. The present invention solves the problem that it is impossible to recruit enough testers in a short time in traditional crowdsourcing testing, and provides personnel guarantee for crowd testing. The present invention utilizes the powerful personnel recruitment capabilities of social networks to recruit enough workers for crowd testing in a short time, and at the same time creates a flexible profit distribution method, so that personnel recruitment has good incentives.

Description

A crowdsourcing tester recruitment method based on social network

Technical Field

The present invention relates to the technical field of crowdsourcing testing, and mainly to a method for recruiting crowdsourcing testers based on social networks.

Background technique

Crowdsourcing testing is an emerging trend in software testing that leverages the strengths, effectiveness, and efficiency of crowdsourcing and cloud platforms. It differs from traditional testing methods because testing is conducted by many different testers from different locations rather than hired consultants and professionals. The software is tested under different realistic platforms, making it more reliable, cost-effective, fast, and bug-free. Moreover, crowdsourcing testing allows for remote usability testing as specific target groups can be recruited through the crowd.

Recruiting testers in crowdsourcing testing is a key process in crowdsourcing testing. Too few testers will reduce the quality of software testing and fail to ensure good coverage and test quality. Therefore, we should try our best to increase the number and participation of testers. However, traditional crowdsourcing testing platforms only display task information on the platform and lack reasonable means to recruit testers. Therefore, they rely solely on testers within the platform to complete test tasks, which cannot reflect the advantages of crowdsourcing testing and the diversity of testers.

With the development of the Internet, social networks have become a part of people's lives, playing an important role in people's lives and having an inestimable impact on people's information acquisition, thinking and life. Social networks have become a window for people to obtain information, express themselves, and promote marketing. Because of the powerful capabilities of social networks, social networks can provide a suitable channel for recruiting personnel for crowdsourcing testing. Based on social networks, a large number of testers can be recruited in a short period of time, thereby increasing the number and diversity of testers and providing personnel guarantees for better completion of testing tasks.

Summary of the invention

Purpose of the invention: The present invention provides a method for recruiting crowdsourcing testers based on social networks, which solves the problem that there is a lack of reasonable means of recruiting testers in the traditional crowdsourcing testing process, so that the test tasks are completed only by testers within the platform, which cannot reflect the advantages of crowdsourcing testing and the diversity of testers.

Technical solution: To achieve the above purpose, the technical solution adopted by the present invention is:

A method for recruiting crowdsourcing testers based on social networks, comprising the following steps:

Step S1, task segmentation: segmenting the original task into several subtasks according to the type, wherein the subtasks can be completed by a single tester with specific skills;

Step S2, task release and dissemination; the subtask is released on the crowd testing platform through the crowd testing task release module; the subtask is sent to the initial disseminator, who disseminates it; when a suitable tester is recruited during the dissemination process, the parent node of the tester is recorded;

Step S3, constructing a task propagation network; constructing a number of task propagation directed edges according to the process from subtask release to recruitment of target testers; the directed edges include the following three situations:

(1) Testers can directly see the subtask release and receive the task on the crowd testing platform without any intermediary communication personnel;

(2) The tester receives the subtasks issued through a communicator. At this time, there is an intermediate communicator.

(3) The tester receives the subtask after being communicated by no less than two communicators;

During the recruitment process, the multiple directed edges together form a task propagation network;

Step S4, profit distribution: Based on the task propagation network described in step S3, a profit distribution model is constructed to distribute profits to the communicators in the task propagation network and the testers who receive and complete the tasks.

Furthermore, in step S1, the divided subtasks are identified by a four-tuple (taskID, n, R, v), where taskID represents the task number, n represents the number of testers required for the task, R represents the skill requirements required to complete the subtask, and v represents the benefits that can be obtained by completing this single task.

Furthermore, the specific steps of task publishing and propagation in step S2 include:

Step S2.1, publish the subtask information on the crowd testing platform; integrate the bshare social sharing tool into the crowd testing platform, and forward it to the initial dissemination personnel through a web page;

Step S2.2: Generate an exclusive invitation code for old users based on their ID, add it to the shared URL as a parameter, and then share the URL to the social network to complete a dissemination.

Furthermore, the specific operations of the crowd-testing platform when constructing the task propagation network in step S3 include: after the user clicks on the URL, the crowd-testing background parses the URL parameters, obtains the user's invitation code, obtains the user's parent node information in the social network through database matching, and is thus connected with a directed edge in the propagation network; at the same time, the crowd-testing platform pays attention to the propagation path information of the tester who finally receives and completes the task, and maintains and updates the tester's parent node information in real time.

Furthermore, the specific steps of profit distribution in step S4 are as follows:

Step S4.1, find the tester who finally participates in the task in the propagation network, find the parent node layer by layer according to the propagation network, and finally obtain the directed edge from the crowd testing platform to the tester;

Step S4.2: According to the three directed edge situations described in step S3, the profit distribution principle is as follows:

(1) Tester x directly sees the subtask release on the crowd-testing platform. When receiving the task, there is no communication cost. The benefit of the tester completing the task is v;

(2) Tester x passes the task to communicator y, and receives and completes the task. Let x's parent node be y. The profit calculation is as follows:

v _x =(1-r)v

v _y = rv

Where v _x represents the benefits of testers, v _y represents the benefits of communicators, and R represents the proportion of the budget used for communication when the task is established;

(3) When tester x receives and completes the task after being propagated through multiple propagators, let x's parent node be y, and the number of other people on the propagation path be M. Then the profit distribution is as follows:

v _x =(1-r)v

v _y = 0.5rv

v _i ＝0.5rv/|M|

Where _vx represents the profit of the tester, _vy represents the profit of the communicator, and _vi represents the average profit of each communicator in the propagation path.

Beneficial effects:

The present invention provides a crowdsourcing tester recruitment method based on social networks, which solves the problem that it is impossible to recruit enough testers in a short time in traditional crowdsourcing testing, and provides personnel guarantee for crowd testing. The present invention utilizes the powerful personnel recruitment capabilities of social networks to recruit enough workers for crowd testing in a short time, and at the same time creates a flexible profit distribution method, so that personnel recruitment has good incentives.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for recruiting crowdsourcing testers based on a social network provided by the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings.

As shown in FIG1 , a method for recruiting crowdsourcing testers based on a social network includes the following steps:

Step S1, task segmentation: the original task is segmented into several subtasks according to the type, and the subtasks can be completed by a single tester with specific skills. The segmented subtasks are identified by a four-tuple (taskID, n, R, v), where taskID represents the task number, n represents the number of testers required for the task, and R represents the skill requirements required to complete the subtask, specifically including the skill attribute set of the tester, such as Python language, database technology, etc. v represents the benefits that can be obtained by completing this single task.

Step S2, task release and dissemination: the subtask is released on the crowd testing platform through the crowd testing task release module; the subtask is sent to the initial disseminator, who then disseminates it; when a suitable tester is recruited during the dissemination process, the parent node of the tester is recorded. The specific steps include:

Step S2.1, publish the subtask information on the crowd testing platform; integrate the bshare social sharing tool into the crowd testing platform, forward it to the initial disseminator through a web page, and spread it on social networks such as WeChat and Weibo;

Step S2.2: Generate an exclusive invitation code for old users based on their ID, add it to the shared URL as a parameter, and then share the URL to the social network, that is, in the form of "invitedID=...", and then share the URL to the social network to complete a dissemination.

Step S3, constructing a task propagation network; constructing a number of task propagation directed edges according to the process from subtask release to recruitment of target testers; the directed edges include the following three situations:

(1) Testers can directly see the subtask release and receive the task on the crowd testing platform without any intermediary communication personnel;

(2) The tester receives the subtasks issued through a communicator. At this time, there is an intermediate communicator.

(3) The tester receives the subtask after being communicated by no less than two communicators;

During the recruitment process, the multiple directed edges together form a task propagation network.

After the user clicks the URL, the crowd-testing backend parses the URL parameters, obtains the user's invitation code invitecode and the user's ID, and obtains the user information, namely inviteID, through database matching, thereby connecting a directed edge from inviteID to ID in the communication network; at the same time, the crowd-testing platform pays attention to the communication path information of the tester who finally receives and completes the task, and maintains and updates the parent node information of the tester in real time.

Step S4, profit distribution: Based on the task propagation network described in step S3, a profit distribution model is constructed to distribute profits to the communicators in the task propagation network and the testers who receive and complete the task. Specifically, step S4.1, find the tester who finally participates in the task in the propagation network, find the parent node layer by layer according to the propagation network, and finally obtain the directed edge from the crowd testing platform to the tester;

Step S4.2: According to the three directed edge situations described in step S3, the profit distribution principle is as follows:

(1) Tester x directly sees the subtask release on the crowd-testing platform. When receiving the task, there is no communication cost. The benefit of the tester completing the task is v;

(2) Tester x passes the task to communicator y, and receives and completes the task. Let x's parent node be y. The profit calculation is as follows:

v _x =(1-r)v

v _y = rv

Where v _x represents the benefits of testers, v _y represents the benefits of communicators, and r represents the proportion of the budget used for communication when the task is established;

(3) When tester x receives and completes the task after being propagated through multiple propagators, let x's parent node be y, and the number of other people on the propagation path be M. Then the profit distribution is as follows:

v _x =(1-r)v

v _y = 0.5rv

v _i ＝0.5rv/|M|

Where _vx represents the profit of the tester, _vy represents the profit of the communicator, and _vi represents the average profit of each communicator in the propagation path.

The profit distribution method designed by the present invention encourages participants to spread information, because spreading information will get certain benefits. At the same time, we only give benefits to people on the dissemination path of the testers who finally participate in the task, not just dissemination. At the same time, we focus on the dissemination personnel who are directly connected to the testers required by the task, which can greatly encourage participants to recruit qualified testers in a targeted manner, thereby achieving the purpose of effective recruitment. Finally, we also give the task publisher a certain degree of flexibility, so that they can flexibly determine their budget ratio for disseminating information by setting the size of r. r = 0 means no dissemination budget, which greatly improves the adaptability of the crowd testing task, and different sizes of dissemination budgets can be set according to different types of crowd testing tasks.

The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims (1)

1. A crowdsourcing tester recruitment method based on social network, characterized by comprising the following steps: Step S1, task segmentation: segment the original task into several subtasks according to the type; Step S2, task release and dissemination; the subtask is released on the crowd testing platform through the crowd testing task release module; the subtask is sent to the initial disseminator, who disseminates it; when a suitable tester is recruited during the dissemination process, the parent node of the tester is recorded; Step S3, constructing a task propagation network; constructing a number of task propagation directed edges according to the process from subtask release to recruitment of target testers; the directed edges include the following three situations: (1) Testers can directly see the subtask release and receive the task on the crowd testing platform without any intermediary communication personnel; (2) The tester receives the subtasks issued through a communicator. At this time, there is an intermediate communicator. (3) The tester receives the subtask after being communicated by no less than two communicators; During the recruitment process, multiple directed edges together form the task propagation network; Step S4, profit distribution: According to the task propagation network described in step S3, a profit distribution model is constructed to distribute profits to the communicators in the task propagation network and the testers who receive and complete the tasks respectively; The divided subtasks are identified by a four-tuple (taskID, n, R, v), where taskID represents the task number, n represents the number of testers required for the task, R represents the skill required to complete the subtask, and v represents the benefit that can be obtained by completing this single task. The specific steps of task publishing and propagation in step S2 include: Step S2.1, publish the subtask information on the crowd testing platform; integrate the bshare social sharing tool into the crowd testing platform, and forward it to the initial dissemination personnel through a web page; Step S2.2: Generate an exclusive invitation code for old users based on their ID, add it to the shared URL as a parameter, and then share the URL to the social network to complete a dissemination; The specific operations of the crowd-testing platform when constructing the task propagation network in step S3 include: after the user clicks the URL, the crowd-testing backend parses the URL parameters, obtains the user's invitation code, obtains the user's parent node information in the social network through database matching, and thus connects with a directed edge in the propagation network; at the same time, the crowd-testing platform pays attention to the propagation path information of the tester who finally receives and completes the task, and maintains and updates the parent node information of the tester in real time; The specific steps of profit distribution in step S4 are as follows: Step S4.1, find the tester who finally participates in the task in the propagation network, find the parent node layer by layer according to the propagation network, and finally obtain the directed edge from the crowd testing platform to the tester; Step S4.2: The profit distribution principles are as follows: (1) Tester x directly sees the subtask release on the crowd-testing platform. When receiving the task, there is no communication cost. The benefit of the tester completing the task is v; (2) Tester x passes the task to communicator y, and receives and completes the task. Let x's parent node be y. The profit calculation is as follows: v _x =(1-r)v v _y = rv Where v _x represents the benefits of testers, v _y represents the benefits of communicators, and r represents the proportion of the budget used for communication when the task is established; (3) When tester x receives and completes the task after being propagated through multiple propagators, let x's parent node be y, and the number of other people on the propagation path be M. Then the profit distribution is as follows: v _x =(1-r)v v _y = 0.5rv v _i ＝0.5rv/|M| Where _vx represents the profit of the tester, _vy represents the profit of the communicator, and _vi represents the average profit of each communicator in the propagation path.