基于Wiener过程模型和遗传算法的OSS工作量费用优化

Kodai Sugisaki, Y. Tamura, S. Yamada
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引用次数: 0

摘要

各种开源软件都是通过使用几个bug跟踪系统来管理的。特别是开源软件向云服务和边缘计算的延伸。最近,OpenStack推出了OSF边缘计算组。云计算、边缘计算等互联网服务背后有大数据。其次,为了评估开源软件的可靠性,考虑大数据的影响是很重要的。一些学者提出了各种各样的软件最优发布问题。在典型的软件优化发布问题中,成本参数被定义为已知参数。然而,由于不确定性,成本参数的确定是困难的。我们研究的目的是估计我们的模型中包含的努力参数。本文提出了一种基于遗传算法的努力参数估计方法。然后,我们在第3节中展示了估计方法。通过对实际数据的分析,给出了努力参数估计方法的数值实例。研究结果表明,使用我们的方法,OSS管理者可以提前了解OSS项目之前所需的人力资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OSS Effort Expense Optimization Based on Wiener Process Model and GA
Various open source software are managed by using several bug tracking systems. In particular, the open source software extends to the cloud service and edge computing. Recently, OSF Edge Computing Group is launched by OpenStack. There are big data behind the internet services such as cloud and edge computing. Then, it is important to consider the impact of big data in order to assess the reliability of open source software. Various optimal software release problems have been proposed by specific researchers. In the typical optimal software release problems, the cost parameters are defined as the known parameter. However, it is difficult to decide the cost parameter because of the uncertainty. The purpose of our research is to estimate the effort parameters included in our models. In this paper, we propose an estimation method of effort parameter by using the genetic algorithm. Then, we show the estimation method in section 3. Moreover, we analyze actual data to show numerical examples for the estimation method of effort parameter. As the research results, we found that the OSS managers would be able to comprehend the human resources required before the OSS project in advance by using our method.
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