Abstract
Requirement engineering is one of the software development life cycle phases; it has been recognized as an important phase for collecting and analyzing a system’s goals. However, despite its importance, requirement engineering has several limitations such as incomplete requirements, vague requirements, lack of prioritization, and less user involvement, all of which affect requirement quality. With the emergence of big data technology, the complexity of big data, which is defined by large data volume, high velocity, and large data variety, has gradually increased, affecting the quality of big data software requirements. This study proposes a framework with four sequential phases to improve requirement engineering quality through big data software development. By integrating the proposed framework’s phases in which user requirements are collected in a complete vision using traditional requirement elicitation techniques with agile methodology and mind mapping, the collected requirements are displayed via a graphical representation using mind maps to achieve high requirement accuracy with connectivity and modifiability, enabling the accurate prioritization of requirements implemented using agile SCRUM methodology. The proposed framework improves requirement quality in big data software development, which is represented by accuracy, completeness, connectivity, and modifiability to understand the value of the collected requirements and effectively affect the quality of the implementation phase.
Highlights
Software requirement engineering represents business needs and goals, including functional and nonfunctional dependency competencies that must be represented and achieved
A well-planned framework for the requirement elicitation process can mitigate the negative effect of big data software requirement limitations
This research is an integration of different parts, including requirement elicitation, big data, agile methodology, and mind mapping, and each part will be introduced
Summary
Software requirement engineering represents business needs and goals, including functional and nonfunctional dependency competencies that must be represented and achieved. It is critical to determine the type of data received because each data type has its customization, so considering these characteristics in the requirement election phase became more challenging [3]. How to systematically handle quality requirements involving big data characteristics to better understand the requirements of big data software projects is a challenge [4]. Mind mapping provides the best practice in requirement election representation on the basis of its graphical concept, i.e., mapping the main ideas together to obtain the best value, producing an accurate and clear requirement representation while considering big data characteristics and quality attributes, which greatly aids in obtaining well-prioritized requirements. This research is an integration of different parts, including requirement elicitation, big data, agile methodology, and mind mapping, and each part will be introduced . The two aspects are interdependent, as challenges from the customer aspect, such as a lack of understanding of users’ needs, customer collaboration, and a common language, lead to challenges in the system aspects, such as requirement changes and updates and a lack of accurate documentation, requirement quality, and requirement prioritization
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