Automated User Story Generation with Test Case Specification Using Large Language Model
This work addresses the time-consuming process of requirements analysis for software engineers, potentially reducing their workload and increasing productivity, though it appears incremental as it applies an existing LLM to a specific domain task.
The researchers tackled the problem of automating user story generation from requirements documents in software engineering by developing a tool called 'GeneUS' using GPT-4.0, which outputs user stories in JSON format for integration with project management tools.
Modern Software Engineering era is moving fast with the assistance of artificial intelligence (AI), especially Large Language Models (LLM). Researchers have already started automating many parts of the software development workflow. Requirements Engineering (RE) is a crucial phase that begins the software development cycle through multiple discussions on a proposed scope of work documented in different forms. RE phase ends with a list of user-stories for each unit task identified through discussions and usually these are created and tracked on a project management tool such as Jira, AzurDev etc. In this research we developed a tool "GeneUS" using GPT-4.0 to automatically create user stories from requirements document which is the outcome of the RE phase. The output is provided in JSON format leaving the possibilities open for downstream integration to the popular project management tools. Analyzing requirements documents takes significant effort and multiple meetings with stakeholders. We believe, automating this process will certainly reduce additional load off the software engineers, and increase the productivity since they will be able to utilize their time on other prioritized tasks.