Empirical Analysis on Parallel Tasks in Crowdsourcing Software Development

Saremi, Razieh; Yang, Ye

doi:10.1109/asew.2015.22

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…Therefore, improper scheduling would result in task starvation [5]. Parallelism in scheduling is a great method to create the chance of utilizing a greater pool of workers [18,26]. Parallelism encourages workers to specialize and complete tasks in a shorter period.…”

Section: Task Flow In Crowdsourcingmentioning

confidence: 99%

“…Although sometimes Award is simply representing a specifically required skill to perform the task, it is one of the main factors influence crowd software workers in terms of the number of registrants and consequently, the number of submissions [32]. Therefore, pricing tasks could be a huge challenge in decomposing the projects to mini-tasks and time of uploading them in the platform, yet by uploading more number parallel tasks, crowd workers would have more choice of utilized tasks to register for and consequently the chance of receiving more number of completed tasks is higher [26]. Since existing studies on general crowdsourcing reported limited or unpredictable results [5][2] [12], it is a good opportunity to focus on parallelism on uploading the same project tasks.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Task Cycle Pattern on Project Success in Software Crowdsourcing

Saremi¹,

Saremi²,

Jena³

et al. 2021

Preprint

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Crowdsourcing is becoming an accepted method of software development for different phases in the production lifecycle. Ideally, mass parallel production through Crowdsourcing could be an option for rapid acquisition in software engineering by leveraging infinite worker resource on the internet. It is important to understand the patterns and strategies of decomposing and uploading parallel tasks to maintain a stable worker supply as well as a satisfactory task completion rate. This research report is an empirical analysis of the available tasks' lifecycle patterns in crowdsourcing. Following the waterfall model in Crowdsourced Software Development (CSD), this research identified four patterns for the sequence of task arrival per project: 1) Prior Cycle, 2) Current Cycle, 3) Orbit Cycle, and 4) Fresh Cycle.

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Section: Task Flow In Crowdsourcingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Task Cycle Pattern on Project Success in Software Crowdsourcing

Saremi¹,

Saremi²,

Jena³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…4). Decomposition of a project into many smaller tasks may reduce its complexity [115]. However, when doing so, the available workforce may be limited, as crowd workers may not be able to take on several competitions at the same time.…”

Section: Theory Development and Model Specificationmentioning

confidence: 99%

“…The literature on software development project management generally suggests that a longer duration can lead to greater software development productivity [18]. A number of research studies have identified competition duration as an important factor in crowdsourcing [93], [115], [146], [150], [55]. The allotted duration for a competition on Topcoder defines the maximum possible time for software development.…”

Section: Theory Development and Model Specificationmentioning

confidence: 99%

Competition-Based Crowdsourcing Software Development: A Multi-Method Study from a Customer Perspective

Stol

Çağlayan²,

Fitzgerald

2019

IIEEE Trans. Software Eng.

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Crowdsourcing is emerging as an alternative outsourcing strategy which is gaining increasing attention in the software engineering community. However, crowdsourcing software development involves complex tasks which differ significantly from the micro-tasks that can be found on crowdsourcing platforms such as Amazon Mechanical Turk which are much shorter in duration, are typically very simple, and do not involve any task interdependencies. To achieve the potential benefits of crowdsourcing in the software development context, companies need to understand how this strategy works, and what factors might affect crowd participation. We present a multi-method qualitative and quantitative theory-building research study. Firstly, we derive a set of key concerns from the crowdsourcing literature as an initial analytical framework for an exploratory case study in a Fortune 500 company. We complement the case study findings with an analysis of 13,602 crowdsourcing competitions over a ten-year period on the very popular Topcoder crowdsourcing platform. Drawing from our empirical findings and the crowdsourcing literature, we propose a theoretical model of crowd interest and actual participation in crowdsourcing competitions. We evaluate this model using Structural Equation Modeling. Among the findings are that the level of prize and duration of competitions do not significantly increase crowd interest in competitions.

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“…To analyze this question, a personalization of the Saremi and Yang [23] approach it was applied to parameterize the complexity of CS projects. In the original approach, the projects are classified into simple (<100 microtasks) and complex (>100 microtasks).…”

Section: ) What Is the Complexity Of Cs Projectsmentioning

confidence: 99%