Agile development methodologies have become key approaches in modern organizations, focusing on open communication between developers and customers to capture requirements at the earliest stages of the development cycle, often in the form of user stories. A critical aspect of agile methodologies is prioritizing these user stories, with customers playing a central and significant role in the decision-making process. Customer-oriented prioritization ensures that business value is maximized, aligning development effort with customer needs and expectations. This paper proposes an algorithm that streamlines user story prioritization by considering both customer-centric and developer-centric factors. By integrating insights from both perspectives, the algorithm optimizes the selection of user stories, ensuring that efforts are directed toward those with the greatest impact. The feasibility and effectiveness of the algorithm are evaluated through experiments, which demonstrate its superiority over methods that neglect to consider factors from both customer-centric and developer-centric viewpoints.

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An MCDA and PERT Based Novel Approach for Prioritization of User Stories in Agile Environment

  • Shreeram Hudda,
  • Rishabh Barnwal,
  • Tanupriya Chejara,
  • Abhishek Khurana

摘要

Agile development methodologies have become key approaches in modern organizations, focusing on open communication between developers and customers to capture requirements at the earliest stages of the development cycle, often in the form of user stories. A critical aspect of agile methodologies is prioritizing these user stories, with customers playing a central and significant role in the decision-making process. Customer-oriented prioritization ensures that business value is maximized, aligning development effort with customer needs and expectations. This paper proposes an algorithm that streamlines user story prioritization by considering both customer-centric and developer-centric factors. By integrating insights from both perspectives, the algorithm optimizes the selection of user stories, ensuring that efforts are directed toward those with the greatest impact. The feasibility and effectiveness of the algorithm are evaluated through experiments, which demonstrate its superiority over methods that neglect to consider factors from both customer-centric and developer-centric viewpoints.