在分布式敏捷软件开发中利用机器学习算法进行任务分配。

IF 3.4 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Heliyon Pub Date : 2024-10-29 eCollection Date: 2024-11-15 DOI:10.1016/j.heliyon.2024.e39926
Dimah Al-Fraihat, Yousef Sharrab, Abdel-Rahman Al-Ghuwairi, Hamza Alzabut, Malik Beshara, Abdulmohsen Algarni
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引用次数: 0

摘要

分布式敏捷软件开发(DASD)已成为一种重要的软件开发方法。在 DASD 中,适当的任务分配对于避免不良后果(包括项目被客户拒绝、团队态度不佳和项目失败)至关重要。随着企业越来越多地采用 DASD 环境,以利用全球人才和知识,同时削减开发成本,协调和沟通问题也随之而来。为了克服这些挑战,高效的任务分配计划成为软件项目管理成功的关键要素。本研究的目的是利用机器学习(ML)预测算法来确定给定任务的最合适角色,从而帮助软件经理在 DASD 环境中更高效、更有效地分配任务。数据集的预处理步骤包括数据清理、归一化以及将数据划分为训练集、验证集和测试集。实验中使用了四种模型分类器:随机森林、决策树、K-近邻(K-NN)和 AdaBoost。结果显示,随机森林在任务分配预测方面的表现优于其他分类器,准确率达到 96.7%,其次是 K-NN(94.2%)、决策树(93.5%)和 AdaBoost(93%)。这项研究表明,ML 模型能有效解决 DASD 环境中的任务分配问题,其结果也很有希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing machine learning algorithms for task allocation in distributed agile software development.

Distributed agile software development (DASD) has become a prominent software development approach. Proper task allocation is crucial in DASD to avoid undesirable outcomes including project rejection by clients, unfavorable team attitudes, and project failure. Coordination and communication issues occur as businesses embrace the DASD environment more frequently to tap into global talent and knowledge while cutting development expenses. To overcome these challenges, efficient task allocation planning becomes a crucial success component in software project management. The purpose of this study is to utilize machine learning (ML) predictive algorithms to determine the most appropriate role for a given task, with the aim of assisting software managers in making task assignments more efficiently and effectively in DASD environment. Preprocessing steps applied to the dataset include data cleaning, normalization, and partitioning into training, validation, and test sets. Four model classifiers were used in the experiment: Random Forest, Decision Tree, K-Nearest Neighbors (K-NN), and AdaBoost. The results showed that Random Forest outperformed the other classifiers in task allocation prediction, achieving an accuracy of 96.7 %, followed by K-NN (94.2 %), Decision Tree (93.5 %), and AdaBoost (93 %). The study demonstrates that ML models are effective in tackling task allocation issues in DASD settings, and the outcomes are promising.

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来源期刊
Heliyon
Heliyon MULTIDISCIPLINARY SCIENCES-
CiteScore
4.50
自引率
2.50%
发文量
2793
期刊介绍: Heliyon is an all-science, open access journal that is part of the Cell Press family. Any paper reporting scientifically accurate and valuable research, which adheres to accepted ethical and scientific publishing standards, will be considered for publication. Our growing team of dedicated section editors, along with our in-house team, handle your paper and manage the publication process end-to-end, giving your research the editorial support it deserves.
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