An Evolutionary Algorithm for Task Scheduling in Crowdsourced Software Development
Razieh Saremi, Hardik Yagnik, Julian Togelius, Ye Yang, and Guenther, Ruhe

TL;DR
This paper introduces an evolutionary algorithm-based scheduling method for crowdsourced software development that optimizes task start dates to reduce project duration and failure rates.
Contribution
It presents a multiobjective genetic algorithm incorporating neural network-based failure prediction for improved task scheduling in CSD.
Findings
Reduced project duration by 33-78% in experiments
Effective prediction of task failure probabilities
Optimized task start dates improve project outcomes
Abstract
The complexity of software tasks and the uncertainty of crowd developer behaviors make it challenging to plan crowdsourced software development (CSD) projects. In a competitive crowdsourcing marketplace, competition for shared worker resources from multiple simultaneously open tasks adds another layer of uncertainty to the potential outcomes of software crowdsourcing. These factors lead to the need for supporting CSD managers with automated scheduling to improve the visibility and predictability of crowdsourcing processes and outcomes. To that end, this paper proposes an evolutionary algorithm-based task scheduling method for crowdsourced software development. The proposed evolutionary scheduling method uses a multiobjective genetic algorithm to recommend an optimal task start date. The method uses three fitness functions, based on project duration, task similarity, and task failure…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Open Source Software Innovations · Software Engineering Research
