[论文解读] Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey

Ahmed Fawzy, Amjed Tahir+2 more|arXiv (Cornell University)|Feb 1, 2024

Big Data and Business Intelligence被引用 5

一句话总结

本论文进行系统性文献综述，识别敏捷软件开发中的数据管理挑战并调查提出的解决方案，概述其对实践的影响。

ABSTRACT

Context: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. These include integrating data from diverse sources and ensuring data quality amidst continuous change and adaptation. Objective: The paper systematically explores data management challenges and potential solutions in agile projects, aiming to provide insights into data management challenges and solutions for both researchers and practitioners. Method: We employed a mixed-methods approach, including a systematic literature review (SLR) to understand the state-of-research followed by a survey with practitioners to reflect on the state-of-practice. The SLR reviewed 45 studies, identifying and categorizing data management aspects along with their associated challenges and solutions. The practitioner survey captured practical experiences and solutions from 32 industry practitioners who were significantly involved in data management to complement the findings from the SLR. Results: Our findings identified major data management challenges in practice, such as managing data integration processes, capturing diverse data, automating data collection, and meeting real-time analysis requirements. To address the challenges, solutions such as automation tools, decentralized data management practices, and ontology-based approaches have been identified. The solutions enhance data integration, improve data quality, and enable real-time decision-making by providing flexible frameworks tailored to agile project needs. Conclusion: The study pinpointed significant challenges and actionable solutions in data management for agile software development. Our findings provide practical implications for practitioners and researchers, emphasizing the development of effective data management practices and tools to address those challenges and improve project success.

研究动机与目标

• 识别在敏捷软件开发中讨论的数据管理方面。
• 描述数据管理各方面的挑战（如集成、收集、质量、分析）。
• 识别并对在敏捷环境中提出的数据管理挑战的解决方案进行分类。
• 评估数据管理挑战对团队与产品交付的影响。
• 就将健全的数据管理整合到敏捷框架中提供建议。

提出的方法

• 遵循 Kitchenham 和 Charters 的系统综述指南。
• 使用 Scopus 检索截至 2023 年 10 月前的英文研究。
• 应用纳入/排除标准筛选研究。
• 通过两阶段筛选和全文评阅共检索到 45 项高质量研究。
• 提取数据并按数据管理方面对研究进行分类。
• 提供含提取结果的可复现数据集。

Figure 1: The review process

实验结果

研究问题

• RQ1 RQ1：在敏捷软件开发背景下讨论了哪些数据管理方面？
• RQ2 RQ2：在这些方面面临的挑战是什么？
• RQ3 RQ3：为解决这些挑战提出了哪些解决方案？

主要发现

• 识别出十五个数据管理方面；数据集成、数据收集、数据质量和数据分析是讨论较多的方面。
• 主要挑战包括互操作性、语义异质性，以及整合异构数据源。
• 解决方案包括本体、数据网格方法、自动化 ETL 工具，以及以质量为导向的开发方法。
• 实现的解决方案包括基于云的数据加载管道、基于本体的集成，以及以架构为中心的方法。
• 存在数据收集与分析之间的重叠，以及数据集成与质量之间的重叠证据。

Figure 2: Total number of studies discussing challenges and solutions associated with fifteen aspects of data management