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SRPTackle: A semi-automated requirements prioritisation technique for scalable requirements of software system projects

机译:SRPTACKLE:半自动要求提供可扩展软件系统项目的优先级化技术

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Context: Requirement prioritisation (RP) is often used to select the most important system requirements as perceived by system stakeholders. RP plays a vital role in ensuring the development of a quality system with defined constraints. However, a closer look at existing RP techniques reveals that these techniques suffer from some key challenges, such as scalability, lack of quantification, insufficient prioritisation of participating stakeholders, overreliance on the participation of professional expertise, lack of automation and excessive time consumption. These key challenges serve as the motivation for the present research.Objective: This study aims to propose a new semiautomated scalable prioritisation technique called 'SRPTackle' to address the key challenges.Method: SRPTackle provides a semiautomated process based on a combination of a constructed requirement priority value formulation function using a multi-criteria decision-making method (i.e. weighted sum model), clustering algorithms (K-means and K-means++) and a binary search tree to minimise the need for expert involvement and increase efficiency. The effectiveness of SRPTackle is assessed by conducting seven experiments using a benchmark dataset from a large actual software project.Results: Experiment results reveal that SRPTackle can obtain 93.0% and 94.65% as minimum and maximum accuracy percentages, respectively. These values are better than those of alternative techniques. The findings also demonstrate the capability of SRPTackle to prioritise large-scale requirements with reduced time consumption and its effectiveness in addressing the key challenges in comparison with other techniques.Conclusion: With the time effectiveness, ability to scale well with numerous requirements, automation and clear implementation guidelines of SRPTackle, project managers can perform RP for large-scale requirements in a proper manner, without necessitating an extensive amount of effort (e.g. tedious manual processes, need for the involvement of experts and time workload).
机译:背景信息:要求优先级(RP)通常用于选择系统利益相关者所感知的最重要的系统要求。 RP在确保具有定义约束的质量体系方面发挥着至关重要的作用。然而,仔细看看现有的RP技术揭示了这些技术遭受了一些关键挑战,例如可扩展性,缺乏量化,参与利益相关者的不良优先级,超越专业知识的参与,缺乏自动化和过度消耗。这些关键挑战是目前研究的动机。目的:本研究旨在提出一种新的半动物可扩展优先级排序技术,称为“SRPTACKLE”以解决关键挑战。方法:Srptackle基于构造要求的组合提供半仿制过程优先级值配方函数使用多标准决策方法(即加权SUM模型),聚类算法(K-MEARY和K-MEARY ++)和二进制搜索树,以最大限度地减少专家参与的需求并提高效率。通过使用大型实际软件项目的基准数据集进行七个实验来评估Srptackle的有效性。结果:实验结果表明,Srptackle分别可以获得93.0%和94.65%的最小和最高精度百分比。这些值优于替代技术。调查结果还证明了SRPTACKLE能够优先考虑大规模要求的优先考虑,随着时间的消耗,以及与其他技术相比解决关键挑战的效力。结论:随着时间的效率,能够用许多要求,自动化和清晰度扩展。 SRPTACKLE的实施指南,项目经理可以以适当的方式对大规模要求进行RP,而不需要大量的努力(例如繁琐的手动流程,需要参与专家和时间工作量)。

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