Extracting Knowledge Graphs from User Stories using LangChain
This addresses the problem of aligning software functionalities with user expectations for software developers, though it appears incremental as it builds on existing frameworks like LangChain.
The paper tackles automated knowledge graph generation from user stories by developing a User Story Graph Transformer module using LangChain and LLMs, resulting in a fully automated extraction process with evaluation via an annotated dataset.
This thesis introduces a novel methodology for the automated generation of knowledge graphs from user stories by leveraging the advanced capabilities of Large Language Models. Utilizing the LangChain framework as a basis, the User Story Graph Transformer module was developed to extract nodes and relationships from user stories using an LLM to construct accurate knowledge graphs.This innovative technique was implemented in a script to fully automate the knowledge graph extraction process. Additionally, the evaluation was automated through a dedicated evaluation script, utilizing an annotated dataset for assessment. By enhancing the visualization and understanding of user requirements and domain concepts, this method fosters better alignment between software functionalities and user expectations, ultimately contributing to more effective and user-centric software development processes.