众包软件开发中任务调度的进化算法
An Evolutionary Algorithm for Task Scheduling in Crowdsourced Software Development
摘要：
软件任务的复杂性和众包开发者行为的不确定性使得规划众包软件开发（CSD）项目具有挑战性。在竞争激烈的众包市场中，多个同时开放的任务对共享工作人员资源的竞争为软件众包的潜在结果增加了另一层不确定性。这些因素导致需要支持CSD管理人员进行自动调度，以提高众包流程和成果的可见性和可预测性。为此，本文提出了一种基于进化算法的众包软件开发任务调度方法。提出的进化调度方法使用多目标遗传算法推荐一个最佳的任务开始日期。该方法使用三个适应度函数，分别基于项目持续时间，任务相似性和任务失败预测。任务失败适应度函数使用神经网络来预测关于特定任务开始日期的任务失败的概率。该方法建议的最佳任务开始日期为项目作为一个整体和每个单独的任务，以实现最低的项目失败率。4个项目的实验结果表明，所提出的方法有可能减少项目工期的一个因素的33- 78%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
