Data management in agile software development : challenges and solutions : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Sciences in Software Engineering, Massey University, Palmerston North, New Zealand - Mohamed, Ahmed (Massey University, 2024)
Managing data in agile software development poses significant challenges for software projects and agile development teams. To thoroughly investigate these challenges and propose workable solutions, this thesis employs a mixed-methods approach, utilising 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 to identify key data management aspects in those studies (including data integration, data collection, data quality, and data analysis). The results of the SLR identified several data management challenges, such as the complexity of automating data collection in dynamic environments, the difficulty of harmonising semantically diverse data, and the continuous struggle to maintain data quality standards throughout iterative development cycles. To address these challenges, the SLR reported various solutions from the reviewed studies, including utilising ontology-based data integration methods to tackle semantic inconsistencies, implementing automated quality assurance frameworks to improve data reliability, and adopting decentralised data management strategies to align better with agile practices. The practitioner survey reported practical experiences from 32 agile practitioners across various industries, aiming to complement the findings from the SLR. The insights from the survey could enhance the practical application of our results and guide future research directions. The survey confirms the majority of SLR findings in terms of the data management challenges and solutions. How ever, the survey also offered additional practical insights, such as the need for better data management training, improved tools, and clearer communication in agile teams. Based on these findings, this thesis presents implications for agile process activities (e.g., sprint planning and daily stand-ups), highlighting that inaccurate or lacking data during requirements gathering can result in poorly defined project goals and affect the entire development process. Furthermore, some of the recommendations provided to help agile teams include the need for 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. This thesis encourages future researchers to explore new methods for data integration, real-time analytics, and data-driven decision-making in evolving agile practices.
Keywords: 460501 Data engineering and data science