An Evolutionary Algorithm for Task Scheduling in Crowdsourced Software Development

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 prediction, respectively. The task failure fitness function uses a neural network to predict the probability of task failure with respect to a specific task start date. The proposed method then recommends the best tasks start dates for the project as a whole and each individual task so as to achieve the lowest project failure ratio. Experimental results on 4 projects demonstrate that the proposed method has the potential to reduce project duration by a factor of 33-78%.