Exploring the decomposition of epics using natural language processing

Abstract

Agile user requirements are typically givens as user stories written using natural language and they come in different forms. The most complex form of stories to work with are epics. If epics are poorly understood, they can contribute to threats regarding the sprints or projects becoming behind schedule. It can be attributed to the epic's complexity. The research aimed to explore and attempt the use of Stanza from the Stanford NLP group in the decomposition of epics by creating a text generative model. We have also utilised the chunking technique to formulate the tasks from the generated user stories by identifying the linguistic structure through the aid of a POS tagger. The obtained results illustrate that the stanza can be utilised in the requirements engineering domain such as Sprint backlog grooming. The benefits of this research work are enormous considering that sprint backlog grooming takes considerable time and is always in iterative mode. Agile teams will also benefit from this work by efficiently using sprint timeboxes with minimal sprint planning effort. This will enable agile teams to spend more time delivering the right solutions with reduced sprint planning time and effort.