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

机译:StakeQP:软件系统项目中的半自动利益相关者量化和优先考虑技术

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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:利益攸关方的量化和优先级(SQP)是基于利益相关者的影响。适当利益相关者的参与在识别和选择生产成功系统的最重要要求方面发挥着至关重要的作用。然而,目前的SQP技术仍然面临严重的局限性,例如具有不足的低级实施细节,缺乏自动化级别,缺乏自动化级别,依赖专业专业知识并没有属性测量标准(AMC)进行股份持有人评估。这些关键问题是目前研究的动机。因此,本研究旨在提出一种称为StakeQP的新型半自动技术,以解决报告的关键限制。 Stakeqp介绍了使用多属性决策方法的新提出的AMC自动执行SQP的新的低级实施细节,即,与理想解决方案(TOPSIS)的订单偏好相似性,以实现有效Stakeqp。使用实际软件项目的基准数据集进行评估脂肪QP的有效性。调查结果表明,脂肪QP可以产生更准确的结果,时间较少,与其他替代技术相比,在寻址定义的密钥限制方面更有效。

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