Evaluating Approaches to Selecting Design Thinking Techniques : Quantitative and Qualitative Analysis

Maria A. C. Meireles, S. Souza, J. C. Duarte, T. Conte, J. Maldonado
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Abstract

Context: Requirements Engineering (RE) is essential to software quality. Studies have shown that software engineers often make mistakes, such as insufficient or misunderstood requirements. Therefore, it is necessary to support all the RE phases, especially eliciting requirements. In this context, Design Thinking (DT) is commonly used to deal with these problems. DT aims at bringing quality to software development to achieve users’ needs. It has a set of techniques and methods that can help software engineers properly elicit requirements to achieve this goal. However, in the literature, there are several techniques and selecting an appropriate one is not a trivial task. Some approaches support the selection of techniques, including DTA4RE and Selection Universe. Objective: This paper aims to analyze the performance of these two approaches to selecting DT techniques in terms of accuracy. Method: We conducted a controlled experiment to obtain the data. We have applied quantitative and qualitative analysis to the data. Results: Regarding the quantitative results, we found no significant difference in accuracy between the Universe Selection approaches and the DTA4RE. Regarding the qualitative results, we found that grouping the techniques into categories presented by the Selection Universe allowed us to reduce the search time for the techniques from DT since the approach allowed us to associate the system features with the techniques that can be used. Conclusion: We found that the two approaches satisfactorily supported the selection of DT techniques for various requirements elicitation activities. In the participants’ perception, the selection universe was helpful because the approach is intuitive. Also, the categorization of techniques made it easier to find the appropriate techniques for the proposed scenarios.
选择设计思维技术的评估方法:定量与定性分析
背景:需求工程(RE)对于软件质量是必不可少的。研究表明,软件工程师经常会犯错误,比如需求不足或误解。因此,有必要支持所有的RE阶段,特别是激发需求。在这种情况下,设计思维(DT)通常用于处理这些问题。DT旨在为软件开发带来质量,以满足用户的需求。它有一组技术和方法,可以帮助软件工程师正确地引出需求来实现这一目标。然而,在文献中,有几种技术,选择一种合适的技术并不是一项微不足道的任务。一些方法支持技术选择,包括DTA4RE和selection Universe。目的:本文旨在分析这两种方法在选择DT技术的准确性方面的表现。方法:采用对照实验方法获取数据。我们对数据进行了定量和定性分析。结果:在定量结果方面,我们发现宇宙选择方法与DTA4RE之间的准确性没有显著差异。关于定性结果,我们发现将技术分组到Selection Universe所呈现的类别中,使我们能够减少从DT中搜索技术的时间,因为该方法允许我们将系统特征与可以使用的技术联系起来。结论:我们发现这两种方法令人满意地支持了DT技术在各种需求激发活动中的选择。在参与者的感知中,选择范围是有帮助的,因为这种方法是直观的。此外,技术的分类使得为建议的场景找到适当的技术变得更加容易。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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