Speaker
Description
The model proposed in this study aims to prevent the loss of key elements within the Scrum framework, commonly used in software development and management processes, and to facilitate their reuse. Software developers handle numerous tasks, and over time, these tasks are completed. New tasks arise, while existing tasks accumulate issues (bugs) or performance improvements. When this historical information is forgotten or an employee leaves, it becomes challenging to accurately assess the duration and complexity of new tasks.
To address this, this study proposes a large language model that learns task context and the Scrum process, leveraging past data on employees and tasks to accurately assign story points for new tasks and allocate them to the appropriate team members. By understanding both task context and process flow, the model aims to enhance the accuracy of task estimation and team member assignment, promoting effective and informed resource allocation within Scrum teams. For this work, the dataset was obtained from several open source projects and their software management tools.
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