• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Asia-Pacific Software Engineering Conference >Failure Prediction in Crowdsourced Software Development
【24h】

Failure Prediction in Crowdsourced Software Development

机译：众包软件开发中的故障预测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background: Despite the increasingly reported benefits of software crowdsourcing, one of the major practical concerns is the limited visibility and control over task progress. Aim: This paper reports an empirical study to develop a framework for failure prediction in software crowdsourcing. Method: This process begins with identifying 13 influencing factors in software crowdsourcing failures, across four categories including task characteristics, technology popularity, competition network, and workers reliability. Presenting an algorithm to construct worker competition network and extract its network metrics features. The proposed framework was evaluated on 4,872 software crowdsourcing tasks, extracted from TopCoder platform, using five machine learners, compared with in-house TopCoder predictor. Results: 1) Workers reliability, links in the description, number of registered workers, number of required technologies, and task-workers network modularity are the most influencing factors for predicting crowdsourcing failure; 2) The top-three learners for task failure are Naïve Bayes, Random Forest, and StackingC, with precision above 98.8%, recall above 81.2%, and F-measure above 91.2%; and 3) The proposed best learners significantly outperform the two baseline models in our evaluation. Conclusions: The performance of the proposed framework is better than those of the two baseline models. This paper offers practical recommendations for managing task failure risks.
机译：背景：尽管越来越多地报道了软件众包带来的好处，但主要的实际问题之一是对任务进度的可见性和控制力有限。目的：本文进行了一项实证研究，以开发软件众包中的故障预测框架。方法：此过程从确定4种类别的软件众包失败的13个影响因素开始，包括任务特征，技术普及度，竞争网络和工作人员可靠性。提出一种构造工人竞争网络并提取其网络指标特征的算法。与内部TopCoder预测器相比，该提议框架对使用5个机器学习器从TopCoder平台提取的4,872个软件众包任务进行了评估。结果：1）工作人员的可靠性，描述中的链接，注册工作人员的数量，所需技术的数量以及任务工作人员的网络模块化是预测众包失败的最大影响因素; 2）任务失败的前三名学习者是朴素贝叶斯，随机森林和StackingC，其精度高于98.8％，召回率高于81.2％，F值高于91.2％。 3）在我们的评估中，建议的最佳学习者的表现明显优于两个基线模型。结论：所提出的框架的性能优于两个基线模型。本文为管理任务失败风险提供了实用建议。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号