Who Should Be Selected to Perform a Task in Crowdsourced Testing?

Cui, Qiang; Wang, Junjie; Yang, Guowei; Xie, Miao; Wang, Qing; Li, Mingshu

doi:10.1109/compsac.2017.265

Cited by 26 publications

(31 citation statements)

References 16 publications

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“…e crowd consists of huge masses of people and according to the level of experience best workers are selected [25,42]. For selection of experienced participant for task, experience strategy is utilized [24]. Selection of crowd greatly relies on its expertise [24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…e choice of right and proper workers would boost the efficacy of crowdsourcing [22,23]. Different business organization employs a suitable worker to complete task [24]. e following are the contributions of the proposed study: (i) A DSS is presented for the appropriate selection of crowd (ii) e proposed system is implemented in the Superdecision tool (iii) e hierarchy of goals, criteria, and alternatives is plotted with various pairwise comparisons to perform the proposed research (iv) Results of the study reveal that the proposed approach is effective and efficient e organization of the paper is as follows.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Decision Support System for the Selection of Appropriate Crowd in Crowdsourcing

et al. 2021

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Crowdsourcing is a complex task-solving model that utilizes humans for solving organizational specific problems. For assigning a crowdsourced task to an online crowd, crowd selection is carried out to select appropriate crowd for achieving the task. The efficiency and effectiveness of crowdsourcing may fail if irrelevant crowd is selected for performing a task. Early decisions regarding selection of a crowd can ultimately lead to successful completion of tasks. To select most appropriate crowd from crowdsourcing, this paper presents a decision support system (DSS) for appropriate selection of crowd. The system has been implemented in the Superdecision tool by plotting hierarchy of goals, criteria, and alternatives. Various calculations have been done for performing the proposed research. Results of the study reveal that the proposed system is effective and efficient for selection of crowd in crowdsourcing by performing various pairwise computation of the study.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Decision Support System for the Selection of Appropriate Crowd in Crowdsourcing

et al. 2021

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“…Expertise [11,[26][27][28][32][33][34][35][36][37] level of the crowd is estimated by expertise filtering. According to the expertise level, a right crowd for a task is selected [33,34].…”

Section: Crowd Selection On the Basis Of Expertise Filteringmentioning

confidence: 99%

“…As an individual has task expertise, he will attempt to complete it with full attention [26]. Crowd of diverse expertise level is selected for tasks [35].…”

Section: Crowd Selection On the Basis Of Expertise Filteringmentioning

confidence: 99%

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Multicriteria‐Based Crowd Selection Using Ant Colony Optimization

et al. 2021

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Internet-enabled technologies have provided a way for people to communicate and collaborate with each other. The collaboration and communication made crowdsourcing an efficient and effective activity. Crowdsourcing is a modern paradigm that employs cheap labors (crowd) for accomplishing different types of tasks. The task is usually posted online as an open call, and members of the crowd self-select a task to be carried out. Crowdsourcing involves initiators or crowdsourcers (an entity usually a person or an organization who initiate the crowdsourcing process and seek out the ability of crowd for a task), the crowd (online participant who is a having a particular background, qualification, and experience for accomplishing task in crowdsourcing activity), crowdsourcing task (the activity in which the crowd contribute), the process (how the activity is carried out), and the crowdsourcing platform (software or market place) where requesters offer various tasks and crowd workers complete these tasks. As the crowdsourcing is carried out in the online environment, it gives rise to certain challenges. The major problem is the selection of crowd that is becoming a challenging issue with the growth in crowdsourcing popularity. Crowd selection has been significantly investigated in crowdsourcing processes. Nonetheless, it has observed that the selection is based only on a single feature of the crowd worker which was not sufficient for appropriate crowd selection. For addressing the problem of crowd selection, a novel “ant colony optimization-based crowd selection method” (ACO-CS) is presented in this paper that selects a crowd worker based on multicriteria features. By utilizing the proposed model, the efficiency and effectiveness of crowdsourcing activity will be increased.

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GUI testing of Android applications: Investigating the impact of the number of testers on different exploratory testing strategies

Di Martino,

Fasolino,

Starace

et al. 2023

J Software Evolu Process

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Graphical user interface (GUI) testing plays a pivotal role in ensuring the quality and functionality of mobile apps. In this context, exploratory testing (ET), a distinctive methodology in which individual testers pursue a creative, and experience‐based approach to test design, is often used as an alternative or in addition to traditional scripted testing. Managing the exploratory testing process is a challenging task that can easily result either in wasteful spending or in inadequate software quality, due to the relative unpredictability of exploratory testing activities, which depend on the skills and abilities of individual testers. A number of works have investigated the diversity of testers' performance when using ET strategies, often in a crowdtesting setting. These works, however, investigated ET effectiveness in detecting bugs, and not in scenarios in which the goal is to generate a re‐executable test suite, as well. Moreover, less work has been conducted on evaluating the impact of adopting different exploratory testing strategies. As a first step toward filling this gap in the literature, in this work, we conduct an empirical evaluation involving four open‐source Android apps and 20 masters students that we believe can be representative of practitioners partaking in exploratory testing activities. The students were asked to generate test suites for the apps using a capture and replay tool and different exploratory testing strategies. We then compare the effectiveness, in terms of aggregate code coverage that different‐sized groups of students using different exploratory testing strategies may achieve. Results provide deeper insights into code coverage dynamics to project managers interested in using exploratory approaches to test simple Android apps, on which they can make more informed decisions.

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Who Should Be Selected to Perform a Task in Crowdsourced Testing?

Cited by 26 publications

References 16 publications

An Efficient Decision Support System for the Selection of Appropriate Crowd in Crowdsourcing

An Efficient Decision Support System for the Selection of Appropriate Crowd in Crowdsourcing

Multicriteria‐Based Crowd Selection Using Ant Colony Optimization

GUI testing of Android applications: Investigating the impact of the number of testers on different exploratory testing strategies

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