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Data-Driven Persona Retrospective Based on Persona Significance Index in B-to-B Software Development
International Journal of Software Engineering and Knowledge Engineering ( IF 0.6 ) Pub Date : 2021-02-07 , DOI: 10.1142/s0218194021500029
Yasuhiro Watanabe 1 , Hironori Washizaki 1 , Yoshiaki Fukazawa 1 , Kiyoshi Honda 2 , Masahiro Taga 3 , Akira Matsuzaki 3 , Takayoshi Suzuki 3
Affiliation  

Business-to-Business (B-to-B) software development companies develop services to satisfy their customers’ requirements. Developers should prioritize customer satisfaction because customers greatly influence agile software development. However, satisfying current customer’s requirements may not fulfill actual users or future customers’ requirements because customers’ requirements are not always derived from actual users. To reconcile these differences, developers should identify conflicts in their strategic plan. This plan should consider current commitments to end users and their intentions as well as employ a data-driven approach to adapt to rapid market changes. A persona models an end user representation in human-centered design. Although previous works have applied personas to software development and proposed data-driven software engineering frameworks with gap analysis between the effectiveness of commitments and expectations, the significance of developers’ commitment and quantitative decision-making are not considered. Developers often do not achieve their business goal due to conflicts. Hence, the target of commitments should be validated. To address these issues, we propose Data-Driven Persona Retrospective (DDR) to help developers plan future releases. DDR, which includes the Persona Significance Index (PerSI) to reflect developers’ commitments to end users’ personas, helps developers identify a gap between developers’ commitments to personas and expectations. In addition, DDR identifies release situations with conflicts based on PerSI. Specifically, we define four release cases, which include different situations and issues, and provide a method to determine the release case based on PerSI. Then we validate the release cases and their determinations through a case study involving a Japanese cloud application and discuss the effectiveness of DDR.

中文翻译:

基于角色显着性指数的数据驱动角色回顾在 B2B 软件开发中

企业对企业 (B-to-B) 软件开发公司开发服务以满足其客户的需求。开发人员应该优先考虑客户满意度,因为客户极大地影响敏捷软件开发。但是,满足当前客户的需求可能无法满足实际用户或未来客户的需求,因为客户的需求并不总是源自实际用户。为了调和这些差异,开发人员应在其战略计划中识别冲突。该计划应考虑当前对最终用户的承诺及其意图,并采用数据驱动的方法来适应快速的市场变化。角色在以人为本的设计中对最终用户表示进行建模。尽管之前的工作已经将角色模型应用于软件开发,并提出了数据驱动的软件工程框架,并在承诺和期望的有效性之间进行了差距分析,但并未考虑开发人员承诺和定量决策的重要性。由于冲突,开发人员经常无法实现其业务目标。因此,承诺的目标应该得到验证。为了解决这些问题,我们提出了数据驱动的角色回顾 (DDR) 来帮助开发人员规划未来的版本。DDR 包括用于反映开发人员对最终用户角色的承诺的角色显着性指数 (PerSI),可帮助开发人员确定开发人员对角色的承诺与期望之间的差距。此外,DDR 根据 PerSI 识别具有冲突的发布情况。具体来说,我们定义了四种发布案例,包括不同的情况和问题,并提供了一种基于 PerSI 确定发布案例的方法。然后我们通过一个涉及日本云应用程序的案例研究来验证发布案例及其确定,并讨论 DDR 的有效性。
更新日期:2021-02-07
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