CN112418803B - Crowd-sourced tester recruitment method based on social network - Google Patents
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Abstract
The invention provides a crowd-sourced tester recruitment method based on a social network, which comprises four stages of task segmentation, task release and transmission, transmission network construction and income distribution; firstly, dividing a crowded test task into small tasks which can be completed by a single tester; then, the task is issued to a crowdsourcing test platform, so that task information is transmitted in a social network; secondly, recording the upper level of each tester by adopting an invitation code technology, thereby forming a propagation network; and finally, in the revenue distribution stage, revenue distribution is carried out according to the propagation network. The invention solves the problem that enough testers cannot be recruited in a short time in the traditional crowdsourcing test, and provides personnel guarantee for crowdsourcing. The invention utilizes the strong personnel recruitment capability of the social network to recruit enough workers for crowd measurement in a short time, and simultaneously creates a flexible income distribution mode, so that the personnel recruitment has good incentive.
Description
Technical Field
The invention relates to the technical field of crowdsourcing tests, in particular to a crowdsourcing tester recruitment method based on a social network.
Background
Crowd sourcing testing is an emerging trend in software testing that takes advantage of the benefits, availability and efficiency of crowd sourcing and cloud platforms. It differs from traditional testing methods in that the test is performed by many different testers from different places, rather than by employment consultants and professionals. The software is tested under different reality platforms, so that the software is more reliable, economical, efficient, quick and defect-free. In addition, crowd-sourced testing allows for remote usability testing, as a particular target group may be recruited by the crowd.
Recruitment of testers in crowdsourcing test is a key flow of crowdsourcing test, so that too few testers can reduce the quality of software testing, and good coverage and test quality cannot be guaranteed. Therefore, efforts are being made to increase the number and engagement of test persons. The traditional crowdsourcing test platform only displays task information on the platform, and lacks a reasonable means for recruiting testers, so that the testers in the platform can only finish test tasks, and the advantages of crowdsourcing tests and the diversity of the testers can not be reflected.
With the development of the internet, social networks have become a part of people's lives, play an important role in people's lives, and have a non-underestimated impact on people's information acquisition, thinking and lives. Social networks become windows for people to acquire information, display themselves, and marketing. Just as social networks are powerful, social networks can provide a suitable channel for crowd-sourced tested person recruitment. A large number of test staff can be recruited in a short time based on the social network, so that the number and diversity of the test staff are improved, and the personal guarantee is provided for better completion of test tasks.
Disclosure of Invention
The invention aims to: the invention provides a crowdsourcing tester recruitment method based on a social network, which solves the problems that a reasonable means for recruiting testers is lacking in the traditional crowdsourcing test process, so that the testers in a platform only need to complete a test task, and the advantages of the crowdsourcing test and the diversity of the testers cannot be reflected.
The technical scheme is as follows: in order to achieve the above purpose, the invention adopts the following technical scheme:
a social network-based crowd-sourced tester recruitment method, comprising the steps of:
s1, task segmentation; dividing an original task into a plurality of subtasks according to types, wherein the subtasks can be completed by a single tester who grasps specific skills;
s2, task release and transmission; the subtasks are released on a crowded test platform through a crowded test task release module; the subtasks are sent to initial propagation personnel, and the initial propagation personnel propagate the subtasks; when a proper tester is recruited in the transmission process, recording the father node of the tester;
s3, constructing a task propagation network; constructing a plurality of task propagation directed edges according to the process from release to recruitment of subtasks to target testers; the directed edge comprises the following three cases:
(1) The testers directly see the subtask release in the crowding platform and receive the tasks, and no intermediate transmission personnel exist at the moment;
(2) The testers propagate through 1 propagator and receive the issued subtasks, and 1 intermediate propagator exists at the moment;
(3) The testers propagate through at least 2 propagation personnel, and finally the issued subtasks are received;
during the recruitment process, the plurality of directed edges together form a task propagation network;
s4, profit distribution; and (3) constructing a profit distribution mode according to the task propagation network in the step (S3), and respectively distributing the profit to a propagator in the task propagation network and a tester receiving and completing the task.
Further, in the step S1, the split subtasks are identified by using quadruplets (task ids, n, R, v), where task ids represent task numbers, n represents the number of testers required by the task, R represents the skill requirements required for completing the subtasks, and v represents the benefits obtained by completing the single task.
Further, the specific steps of task publishing and propagating in the step S2 include:
step S2.1, issuing the subtask information to a mass measurement platform; integrating a bshare socialization sharing tool on a public testing platform, and forwarding to initial propagation personnel in a webpage mode;
and S2.2, generating a special invitation code for the old user according to the ID, adding the special invitation code as a parameter into the shared url, and sharing the url into the social network to finish one-time transmission.
Further, the specific operations of the mass measurement platform in constructing the task propagation network in step S3 include: after clicking url by a user, the crowd measurement background analyzes the url parameters to obtain an invitation code of the user, and parent node information of the user in a social network is obtained through database matching, so that the user is connected with a directed edge in a propagation network; meanwhile, the mass measurement platform pays attention to the propagation path information of the testers which finally receive and complete the tasks, and maintains and updates the father node information of the testers in real time.
Further, the specific steps of the profit allocation in the step S4 are as follows:
step S4.1, finding a tester finally participating in a task in a propagation network, searching a father node layer by layer according to the propagation network, and finally obtaining a directed edge from a public testing platform to the tester;
step S4.2, according to the three directed edge situations described in step S3, the rule of the allocation of benefits is as follows:
(1) The tester x directly sees the subtask release in the crowding platform, when receiving the task, there is no propagation cost, the income of the tester completing the task is v;
(2) The tester x transmits the data to the tester through the propagator y, and receives and completes the task, the father node of the tester x is marked as y, and the profit calculation is respectively as follows:
v x ＝(1-r)v
v y ＝rv
wherein v is x Representing the income of the testers, v y Representing the benefit of the propagator, R representing the proportion of budget for propagation when setting up the task;
(3) When the test person x receives and completes the task after being transmitted by a plurality of transmission persons, the father node of the test person x is marked as y, and the number of other persons on the transmission path is marked as M, the profit is distributed as follows:
v x ＝(1-r)v
v y ＝0.5rv
v i ＝0.5rv/|M|
wherein v is x Representing the income of the testers, v y Representing the benefit of the propagator, v i Representing the average benefit of each propagation path person in the propagation path.
The beneficial effects are that:
the invention provides a crowd-sourced test personnel recruitment method based on a social network, which solves the problem that enough test personnel cannot be recruited in a short time in the traditional crowd-sourced test and provides personnel guarantee for crowd-sourced test. The invention utilizes the strong personnel recruitment capability of the social network to recruit enough workers for crowd measurement in a short time, and simultaneously creates a flexible income distribution mode, so that the personnel recruitment has good incentive.
Drawings
Fig. 1 is a flowchart of a crowd-sourced tester recruitment method based on a social network provided by the invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
A social network-based crowd-sourced tester recruitment method as shown in fig. 1, comprising the steps of:
s1, task segmentation; the original task is divided into a plurality of subtasks by type, which can be completed by a single tester who has a specific skill. And identifying the sub-tasks after segmentation by adopting quadruplets (task ID, n, R, v), wherein the task ID represents a task number, n represents the number of testers required by the task, and R represents the skill requirement required by completing the sub-tasks, and specifically comprises a skill attribute set of the testers, such as python language, database technology and the like. v represents the benefit that can be achieved by performing this single task.
S2, task release and transmission; the subtasks are released on a crowded test platform through a crowded test task release module; the subtasks are sent to initial propagation personnel, and the initial propagation personnel propagate the subtasks; upon recruitment of appropriate testers during the propagation process, the testers' parent nodes are recorded. The method comprises the following specific steps:
step S2.1, issuing the subtask information to a mass measurement platform; integrating a bshare socialization sharing tool on a public testing platform, forwarding to initial propagation personnel in a webpage mode, and propagating in social networks such as WeChat, microblog and the like;
step S2.2, generating exclusive invitation codes for old users according to the IDs, adding the exclusive invitation codes as parameters into shared url, sharing url into a social network, namely in the form of 'provitedID= …', and sharing url into the social network to finish one-time transmission.
S3, constructing a task propagation network; constructing a plurality of task propagation directed edges according to the process from release to recruitment of subtasks to target testers; the directed edge comprises the following three cases:
(1) The testers directly see the subtask release in the crowding platform and receive the tasks, and no intermediate transmission personnel exist at the moment;
(2) The testers propagate through 1 propagator and receive the issued subtasks, and 1 intermediate propagator exists at the moment;
(3) The testers propagate through at least 2 propagation personnel, and finally the issued subtasks are received;
the plurality of directed edges together form a task propagation network during the recruitment process.
After clicking url by a user, the crowd measurement background analyzes the url parameters to obtain an invitation code inviteode of the user and an ID of the user, and the user information, namely an inviteID, is obtained through database matching, so that a directed edge from the inviteID to the ID is connected in a propagation network; meanwhile, the mass measurement platform pays attention to the propagation path information of the testers which finally receive and complete the tasks, and maintains and updates the father node information of the testers in real time.
S4, profit distribution; and (3) constructing a profit distribution mode according to the task propagation network in the step (S3), and respectively distributing the profit to a propagator in the task propagation network and a tester receiving and completing the task. Step S4.1, finding a tester finally participating in a task in a propagation network, finding a father node layer by layer according to the propagation network, and finally obtaining a directed edge from a public testing platform to the tester;
step S4.2, according to the three directed edge situations described in step S3, the rule of the allocation of benefits is as follows:
(1) The tester x directly sees the subtask release in the crowding platform, when receiving the task, there is no propagation cost, the income of the tester completing the task is v;
(2) The tester x transmits the data to the tester through the propagator y, and receives and completes the task, the father node of the tester x is marked as y, and the profit calculation is respectively as follows:
v x ＝(1-r)v
v y ＝rv
wherein v is x Representing the income of the testers, v y Representing the benefit of the propagator, r representing the proportion of budget for propagation when setting up the task;
(3) When the test person x receives and completes the task after being transmitted by a plurality of transmission persons, the father node of the test person x is marked as y, and the number of other persons on the transmission path is marked as M, the profit is distributed as follows:
v x ＝(1-r)v
v y ＝0.5rv
v i ＝0.5rv/|M|
wherein v is x Representing the income of the testers, v y Representing the benefit of the propagator, v i Representing the average benefit of each propagation path person in the propagation path.
The profit distribution mode designed by the invention encourages the participants to carry out the propagation information, because the propagation information can obtain a certain profit, meanwhile, only people on the propagation path of the testers finally participating in the task are given profit, not propagation is given profit, and meanwhile, the propagation personnel directly connected with the testers required by the task are biased, so that the participants can be greatly encouraged to purposefully recruit the testers meeting the conditions, and the aim of effective recruitment is achieved. Finally, the task publisher is given a certain flexibility, so that the task publisher can flexibly determine the own budget proportion for information transmission by setting the size of r, and r=0 is no transmission budget, so that the adaptability of the crowded measurement task is greatly improved, and the transmission budgets with different sizes can be set according to different types of crowded measurement tasks.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (1)
1. A social network-based crowd-sourced tester recruitment method, comprising the steps of:
s1, task segmentation; dividing an original task into a plurality of subtasks according to types;
s2, task release and transmission; the subtasks are released on a crowded test platform through a crowded test task release module; the subtasks are sent to initial propagation personnel, and the initial propagation personnel propagate the subtasks; when a proper tester is recruited in the transmission process, recording the father node of the tester;
s3, constructing a task propagation network; constructing a plurality of task propagation directed edges according to the process from release to recruitment of subtasks to target testers; the directed edge comprises the following three cases:
(1) The testers directly see the subtask release in the crowding platform and receive the tasks, and no intermediate transmission personnel exist at the moment;
(2) The testers propagate through 1 propagator and receive the issued subtasks, and 1 intermediate propagator exists at the moment;
(3) The testers propagate through at least 2 propagation personnel, and finally the issued subtasks are received;
in the recruitment process, a plurality of directed edges together form a task propagation network;
s4, profit distribution; constructing a profit distribution mode according to the task propagation network in the step S3, and respectively distributing the profit to a propagator in the task propagation network and a tester receiving and completing the task;
identifying the sub-tasks after segmentation by adopting quadruplets (task ID, n, R, v), wherein the task ID represents a task number, n represents the number of testers required by the task, R represents the skill requirement required by completing the sub-tasks, and v represents the benefit obtained by completing the single task;
the task publishing and spreading specific steps in the step S2 comprise:
step S2.1, issuing the subtask information to a mass measurement platform; integrating a bshare socialization sharing tool on a public testing platform, and forwarding to initial propagation personnel in a webpage mode;
s2.2, generating exclusive invitation codes for old users according to the IDs, adding the exclusive invitation codes as parameters into shared url, and sharing url into a social network to finish one-time transmission;
the mass measurement platform concrete operation in the step S3 for constructing the task propagation network comprises the following steps: after clicking url by a user, the crowd measurement background analyzes the url parameters to obtain an invitation code of the user, and parent node information of the user in a social network is obtained through database matching, so that the user is connected with a directed edge in a propagation network; meanwhile, the mass measurement platform pays attention to the propagation path information of the testers which finally receive and complete the tasks, and maintains and updates the father node information of the testers in real time;
the specific steps of the profit allocation in the step S4 are as follows:
step S4.1, finding a tester finally participating in a task in a propagation network, searching a father node layer by layer according to the propagation network, and finally obtaining a directed edge from a public testing platform to the tester;
step S4.2, prescribing a profit allocation principle as follows:
(1) The tester x directly sees the subtask release in the crowding platform, when receiving the task, there is no propagation cost, the income of the tester completing the task is v;
(2) The tester x transmits the data to the tester through the propagator y, and receives and completes the task, the father node of the tester x is marked as y, and the profit calculation is respectively as follows:
v x ＝(1-r)v
v y ＝rv
wherein v is x Representing the income of the testers, v y Representing the benefit of the propagator, r representing the proportion of budget for propagation when setting up the task;
(3) When the test person x receives and completes the task after being transmitted by a plurality of transmission persons, the father node of the test person x is marked as y, and the number of other persons on the transmission path is marked as M, the profit is distributed as follows:
v x ＝(1-r)v
v y ＝0.5rv
v i ＝0.5rv/|M|
wherein v is x Representing the income of the testers, v y Representing the benefit of the propagator, v i Representing the average benefit of each propagation path person in the propagation path.
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| CN108364198A (en) * | 2018-02-12 | 2018-08-03 | 南京邮电大学 | A kind of online motivational techniques of mobile crowdsourcing based on social networks |
| CN111666207A (en) * | 2020-05-18 | 2020-09-15 | 中国科学院软件研究所 | Crowdsourcing test task selection method and electronic device |
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| CN108364198A (en) * | 2018-02-12 | 2018-08-03 | 南京邮电大学 | A kind of online motivational techniques of mobile crowdsourcing based on social networks |
| CN111666207A (en) * | 2020-05-18 | 2020-09-15 | 中国科学院软件研究所 | Crowdsourcing test task selection method and electronic device |
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| Mechanism Design for Cross-Market Task Crowdsourcing;Yu Qiao等;《In IEEE/ACM International Symposium on Quality of Service (IWQoS ’19)》;20190630;第1-10页 * |
| 基于众包模式的分类法映射研究;陈瑞等;《情报理论与实践》;20200212;第43卷(第7期);第137-143页 * |
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