Leveraging crowdsourcing for team elasticity: an empirical evaluation at TopCoder

Saremi, Razieh; Yang, Ye; Ruhe, Guenther; Messinger, David W.

doi:10.1109/icse-seip.2017.2

Cited by 28 publications

(25 citation statements)

References 11 publications

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“…Karim et al in [13] proposed a recommendation system that can help mainly crowd workers (and as a side effect providers) on taking over the appropriate tasks based on technology requirements and their skills. The aforementioned factors are also explored by Saremi et al [25] who explored team reliability, velocity and reliability.…”

Section: F How To Crowdsource?mentioning

confidence: 99%

Crowdsourcing in Software Development: Empirical Support for Configuring Contests

et al. 2020

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Despite the extensive adoption of crowdsourcing for the timely, cost-effective, and high-quality completion of software development tasks, a large number of crowdsourced challenges are not able to acquire a winning solution, on time, and within the desired cost and quality thresholds. A possible reason for this is that we currently lack a systematic approach that would aid software managers during the process of designing software development tasks that will be crowdsourced. This paper attempts to extend the current knowledge on designing crowdsourced software development tasks, by empirically answering the following management questions: (a) what type of projects should be crowdsourced; (b) why should one crowdsource-in terms of acquired benefits; (c) where should one crowdsource-in terms of application domain; (d) when to crowdsource-referring to the time period of the year; (e) who will win or participate in the contest; and (f) how to crowdsource (define contest duration, prize, type of contest etc.) to acquire the maximum benefits-depending on the goal of crowdsourcing. To answer the aforementioned questions, we have performed a case study on 2,209 software development tasks crowdsourced through TopCoder platform. The results suggest that there are significant differences in the level to which crowdsourcing goals are reached, across different software development activities. Based on this observation we suggest that software managers should prioritize the goals of crowdsourcing, decide carefully upon the activity to be crowdsourced and then define the settings of the task.

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Section: F How To Crowdsource?mentioning

confidence: 99%

Crowdsourcing in Software Development: Empirical Support for Configuring Contests

et al. 2020

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“…However, this constitutes an important pool of available (e.g. green, yellow belt workers in [9]) workers in crowdsourcing market, regarding experiencing competition, acquiring new skills, and expertise buildup. As an illustrative example, we look at the first-10 and last-10 tasks registered in 2016 by worker testXuSanping.…”

Section: How Learning Workers With No Pastmentioning

confidence: 99%

Learn or Earn? - Intelligent Task Recommendation for Competitive Crowdsourced Software Development

Karim¹,

Yang²,

Messinger³

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Background: Competitive crowdsourced development encourages online software developers to register for tasks offered on the crowdsourcing platform and implement them in a competitive mode. As a large number of tasks are uploaded daily, the scenery of competition is changing continuously. Without appropriate decision support, online developers often make task decisions in an ad hoc and intuitive manner. Aims: To provide dynamic decision support for crowd developers to select the task that fit best to their personal learning versus earning objectives, taking into account the actual competitiveness situation. Method: We propose a recommendation system called EX 2 ("EX-Square") that combines both explorative ("learn") and exploitative ("earn") search for tasks, based on a systematic analysis of workers preference patterns, technologies hotness, and the projection of winning chances. The implemented prototype allows dynamic recommendations that reflect task updates and competition dynamics at any given time. Results: Based on evaluation from 4007 tasks monitored over a period of 2 years, we show that EX 2 can explore and adjust task recommendations corresponding to context changes, and individual learning preferences of workers. A survey was also conducted with 14 actual crowd workers, showing that intelligent decision support from EX 2 is considered useful and valuable. Conclusions: With support from EX 2 , workers benefit from the tool from getting customized recommendations, and the platform provider gets a higher chance to better cover the breadth of technology needs in case recommendations are taken.

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“…While we look at the software development industry, we would like to highlight that today, due to the pandemic; almost all project teams have moved to distributed team organization and work remotely [1], [2], and [3]. It would be beneficial, in our opinion, to investigate the results and effect on team performance by keeping the distributed team setup and evaluate the effectiveness of such project teams.…”

Section: Introductionmentioning

confidence: 99%

Distributed Project Team Key Performance Indicators

Краснокутська¹,

Podoprykhina²

2020

SAR Journal

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Distributed projects provide major benefits in terms of tapping team members' competencies around a globe, but still they represent a significant challenge for coordinating and monitoring teams' performance. This paper investigates distributed projects with a specific focus on performance metrics. As the result of the research, the list of suggested Key Performance Indicators (KPIs) are given to manage distributed project team as well as described the process to define project KPIs and maintain them.

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Leveraging crowdsourcing for team elasticity: an empirical evaluation at TopCoder

Cited by 28 publications

References 11 publications

Crowdsourcing in Software Development: Empirical Support for Configuring Contests

Crowdsourcing in Software Development: Empirical Support for Configuring Contests

Learn or Earn? - Intelligent Task Recommendation for Competitive Crowdsourced Software Development

Distributed Project Team Key Performance Indicators

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