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基于目标驱动的集成化软件需求建模方法

Integrated software requirement modeling method based on goal driven
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摘要 针对传统方法存在的软件并发事务响应时间较长,软件执行效率不高以及负载均衡效果不佳的问题,提出基于目标驱动的集成化软件需求建模方法。将集成化软件负载均衡作为目标驱动求解目标,计算资源大小收敛关系,使用遗传算法种群初始化、交叉等步骤获得最佳个体适应度,最佳个体适应度不再产生变化时停止迭代,得到最优响应时间、资源利用率与吞吐量结果。使用COM构件维持系统代码稳定运行,采用合成技术完成数据流图语义数据保存与提取,代入获取用户信息与描述用户信息的功能,利用数据储存层、中间层与应用层组建集成化软件需求模型。实验结果表明,所提方法可快速了解用户操作需求,在保证软件响应时间的同时,提高了软件开发效率。 Traditional methods have many disadvantages,such as long response time of software concurrent transactions,low execution efficiency and poor load balancing effect.In this paper,the goal-driven integrated software requirements modeling method is studied.Integrated software load balancing was regarded as the goal-driven solution goal.The convergence relationship of resource size was calculated.The genetic algorithm was used to obtain the best individual fitness through population initialization and crossover and other steps.The iteration was terminated until the optimal individual fitness does not change,thus obtaining the optimal response time,resource utilization and throughput.COM components were utilized to maintain the stable operation of the system code.Synthesis technology was introduced to save and extract semantic data of data flow graphs.The functions of user information and describing user information were substituted to obtain.The data storage layer,middle layer and application layer were adopted to build an integrated software requirements model.The experimental results show that this method can quickly understand the operation requirements of users,not only shortening the software response time,but also improving the efficiency of software development.
作者 洪蕾 谢锐 HONG Lei;XIE Rui(Jinling Institute of Technology,School of Software Engineering,Nanjing Jiangsu 211169,China;Networks&Information Center of Shanghai Jiaotong University,Shanghai 200240,China)
出处 《计算机仿真》 北大核心 2022年第5期356-360,共5页 Computer Simulation
基金 国一流专业建设资助(jwc2020002) 2020年江苏省现代教育技术研究课题(2019-R-79396)。
关键词 目标驱动 集成化软件 需求建模 遗传算法 负载均衡 Goal-driven Integrated software Requirement modeling Genetic algorithm Load balancing
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