Decision Support for Increasing the Efficiency of Crowdsourced Software Development
Muhammad Rezaul Karim, David Messinger, Ye Yang, Guenther Ruhe

TL;DR
This paper presents a machine learning-based decision support methodology to improve efficiency in crowdsourced software development by predicting task success, worker qualification, and potential task failure, leading to significant time savings.
Contribution
It introduces a novel predictive framework using Random Forests to optimize task-worker matching and reduce unsuccessful efforts in crowdsourced software development.
Findings
Random Forest outperforms other techniques in prediction accuracy.
Task recommendations can save up to 4.6 person-days per task.
High accuracy (over 80% F-measure) in predicting tasks unlikely to receive submissions.
Abstract
Crowdsourced software development (CSD) offers a series of specified tasks to a large crowd of trustworthy software workers. Topcoder is a leading platform to manage the whole process of CSD. While increasingly accepted as a realistic option for software development, preliminary analysis on Topcoder's software crowd worker behaviors reveals an alarming task-quitting rate of 82.9%. In addition, a substantial number of tasks do not receive any successful submission. In this paper, we report about a methodology to improve the efficiency of CSD. We apply massive data analytics and machine leaning to (i) perform comparative analysis on alternative technique analysis to predict likelihood of winners and quitters for each task, (ii) significantly reduce the amount of non-succeeding development effort in registered but inappropriate tasks, (iii) identify and rank the most qualified registered…
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Taxonomy
TopicsSoftware Engineering Research · Mobile Crowdsensing and Crowdsourcing · Open Source Software Innovations
