Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey

• URL: http://arxiv.org/abs/2402.00462v2
• Date: Fri, 12 Jul 2024 15:33:59 GMT
• Title: Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey
• Authors: Ahmed Fawzy, Amjed Tahir, Matthias Galster, Peng Liang,
• Abstract summary: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. Challenges include integrating data from diverse sources and ensuring data quality in light of continuous change and adaptation.
• Score: 4.45543024542181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. Challenges include integrating data from diverse sources and ensuring data quality in light of continuous change and adaptation. To this end, we aimed to systematically explore data management challenges and potential solutions in agile projects. We employed a mixed-methods approach, utilizing a systematic literature review (SLR) to understand the state-of-research followed by a survey with practitioners to reflect on the state-of-practice. In the SLR, we reviewed 45 studies in which we identified and categorized data management aspects and the associated challenges and solutions. In the practitioner survey, we captured practical experiences and solutions from 32 industry experts to complement the findings from the SLR. Our findings reveal major data management challenges reported in both the SLR and practitioner survey, such as managing data integration processes, capturing diverse data, automating data collection, and meeting real-time analysis requirements. Based on our findings, we present implications for practitioners and researchers, which include the necessity of developing clear data management policies, training on data management tools, and adopting new data management strategies that enhance agility, improve product quality, and facilitate better project outcomes.

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