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StakeQP: A semi-automated stakeholder quantification and prioritisation technique for requirement selection in software system projects

机译:StakeQP:用于软件系统项目需求选择的半自动化利益相关者量化和优先级排序技术

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摘要

Stakeholder quantification and prioritisation (SQP) is performed on the basis of the stakeholder's influence. The involvement of adequate stakeholders plays a crucial role in identifying and selecting the most essential requirements to produce a successful system. However, the current SQP techniques still face serious limitations, such as having insufficient low-level implementation details, being time-consuming, lacking an automation level, heavily relying on professional expertise and having no attribute measurement criteria (AMC) for stake-holder evaluation. These key issues serve as the motivation of the present study. Hence, this study is aimed at proposing a new semi-automated technique called StakeQP to address the reported key limitations. StakeQP introduces new low-level implementation details to perform SQP automatically on the basis of the newly proposed AMC using the multi-attribute decision-making method, namely, the technique of order preference similarity to the ideal solution (TOPSIS), to achieve an efficient StakeQP. The effectiveness of StakeQP is evaluated using a benchmark dataset of the actual software project. The findings show that StakeQP can produce more accurate results with less time consumption and is more effective in addressing the defined key limitations compared with other alternative techniques.
机译:利益相关者量化和优先排序(SQP)是在利益相关者的影响基础上执行的。适当的利益相关者的参与在确定和选择最基本的要求以建立一个成功的系统中起着关键作用。但是,当前的SQP技术仍然面临严重的局限性,例如,底层实施细节不足,耗时,缺乏自动化水平,严重依赖专业知识并且没有用于利益相关者评估的属性度量标准(AMC) 。这些关键问题是本研究的动机。因此,本研究旨在提出一种称为StakeQP的新半自动化技术,以解决所报告的关键限制。 StakeQP引入了新的低层实现细节,以使用多属性决策方法(即,与理想解决方案(TOPSIS)的顺序偏好相似性)的多属性决策方法,在新提议的AMC的基础上自动执行SQP。 StakeQP。使用实际软件项目的基准数据集评估StakeQP的有效性。研究结果表明,与其他替代技术相比,StakeQP可以以更少的时间消耗获得更准确的结果,并且在解决定义的关键限制方面更有效。

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