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利用人工神经网络进行全球软件开发的风险预测。

Risk Prediction by Using Artificial Neural Network in Global Software Development.

机构信息

College of Computer Science and Information Systems, Institute of Business Management (IoBM), Korangi Creek, Karachi, Pakistan.

Malaysian Institute of Information Technology, Universiti Kuala Lumpur (UniKL MIIT), Kuala Lumpur, Malaysia.

出版信息

Comput Intell Neurosci. 2021 Dec 9;2021:2922728. doi: 10.1155/2021/2922728. eCollection 2021.

DOI:10.1155/2021/2922728
PMID:35198017
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8860515/
Abstract

The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today's world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg-Marquardt and Scaled Conjugate Gradient approaches.

摘要

全球软件开发的需求正在增长。一个地方或一个国家缺乏软件专家是全球软件开发范围扩大的原因。位于世界不同地区的软件开发者拥有成功完成项目所需的多样化技能，他们在软件开发领域发挥着关键作用。利用世界各地软件开发人员的技能和专业知识，可以开发任何组件或解决任何与 IT 相关的问题。最好的软件技能和工具分布在全球各地，但要整合这些技能和工具并使其为解决现实世界的问题而工作是一项具有挑战性的任务。风险管理学科为软件专家在当今竞争激烈的世界中面临的风险提供了替代策略。这项研究旨在预测分布式团队在全球软件开发环境中面临的与时间、成本和资源相关的风险。为了检验这些因素的相对影响，在这项研究中，采用了神经网络方法，如 Levenberg-Marquardt、贝叶斯正则化和缩放共轭梯度，以预测与全球软件开发项目时间、成本和资源相关的风险响应。还对这三种算法进行了比较分析，以确定最高精度的算法。这项研究的结果表明，贝叶斯正则化在均方误差（验证）标准方面的表现明显优于 Levenberg-Marquardt 和缩放共轭梯度方法。

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