基于双指标分组学习粒子群算法的动态敏捷软件项目调度

计算机工程与科学 ›› 2024, Vol. 46 ›› Issue (10): 1793-1806.

基于双指标分组学习粒子群算法的动态敏捷软件项目调度

申晓宁1,2,3,4,徐继勇1,毛鸣健1,陈文言1,宋丽妍5,6

(1.南京信息工程大学自动化学院,江苏南京 210044;2.江苏省大数据分析技术重点实验室,江苏南京 210044;
3.江苏省气象能源利用与控制工程技术研究中心,江苏南京 210044;
4.江苏省大气环境与装备技术协同创新中心,江苏南京 210044;
5.南方科技大学工学院,广东深圳 518055;6.广东省类脑智能计算重点实验室,广东深圳 518055)

收稿日期:2023-04-11 修回日期:2023-11-29 接受日期:2024-10-25 出版日期:2024-10-25 发布日期:2024-10-29
基金资助:
国家自然科学基金 (61502239,62002148);江苏省自然科学基金 (BK20150924);广东省重点实验室资助项目(2020B121201001)

Dynamic agile software project scheduling using dual-index group learning particle swarm optimization

SHEN Xiao-ning1,2,3,4,XU Ji-yong1,MAO Ming-jian1,CHEN Wen-yan1,SONG Li-yan5,6

(1.School of Automation,Nanjing University of Information Science & Technology,Nanjing 210044;
2.Jiangsu Key Laboratory of Big Data Analysis Technology,Nanjing 210044;
3.Jiangsu Engineering Research Center on Meteorological Energy Using and Control,Nanjing 210044;
4.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing 210044;
5.College of Engineering,Southern University of Science and Technology,Shenzhen 518055;
6.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Shenzhen 518055,China)

Received:2023-04-11 Revised:2023-11-29 Accepted:2024-10-25 Online:2024-10-25 Published:2024-10-29

摘要/Abstract

摘要： 针对敏捷软件开发中的用户故事选择和任务分配2个紧耦合子问题，考虑用户故事的新增和开发者工作时长的不确定性，构建敏捷软件项目的动态周期性调度模型，提出一种基于目标值和潜力值双指标进行分组学习的粒子群优化算法。该算法依据不同分组特征选用相异的学习对象，以提高搜索的多样性;基于投资回报率和时间利用率设计初始化和局部搜索策略，以应对环境变化并增强挖掘能力。与7种已有算法相比，所提算法能够规划出一套产出价值更大和时间利用率更高的调度方案。

关键词: 敏捷开发, 软件项目调度, 双指标, 分组学习, 粒子群优化

Abstract: To address the two tightly coupled sub-problems of user story selection and task allocation in agile software development, while considering the uncertainties of new user stories and developers' working hours, a dynamic periodic scheduling model for agile software projects is constructed. A particle swarm optimization algorithm based on grouped learning using both target values and potential values as indicators is proposed. By selecting different learning objects based on the characteristics of diffe- rent groups, the diversity of search is enhanced. Initialization and local search strategies are designed based on return on investment and time utilization, allowing the algorithm to adapt to environmental changes and improve its exploration capabilities. Compared with seven existing algorithms, the proposed algorithm can devise a scheduling plan with greater output value and higher time utilization.

Key words: agile development, software project scheduling, dual-index, group learning, particle swarm optimization

申晓宁, 徐继勇, 毛鸣健, 陈文言, 宋丽妍, . 基于双指标分组学习粒子群算法的动态敏捷软件项目调度[J]. 计算机工程与科学, 2024, 46(10): 1793-1806.

SHEN Xiao-ning, XU Ji-yong, MAO Ming-jian, CHEN Wen-yan, SONG Li-yan, . Dynamic agile software project scheduling using dual-index group learning particle swarm optimization[J]. Computer Engineering & Science, 2024, 46(10): 1793-1806.