In this research project, we aim to provide traceability link recovery and consistency analyses between different kinds of software artifacts. Our recent approaches, such as LiSSA, leverage Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to enable more generic and effective traceability link recovery across various artifact types. These methods combine information retrieval with LLMs to find and suggest trace links, making them adaptable to different tasks like requirements-to-code, documentation-to-code, and more. You can find our different approaches, including LiSSA and others, on the approaches page or read more about them using the info button on the publications page.

Documenting the architecture of a software system is important, especially to capture reasoning and design decisions. However, documentation is often incomplete, outdated, or missing, leading to loss of crucial knowledge and increased risks. Our long-term vision is to persist information from various sources, such as whiteboard discussions, to avoid losing essential system knowledge. A key challenge is ensuring consistency between formal artifacts (e.g., models) and informal documentation. We address this by applying natural language understanding and knowledge bases to analyze consistency and create traceability links between models and textual artifacts.

ARDoCo is actively developed by researchers of the Modelling for Continuous Software Engineering (MCSE) group of KASTEL - Institute of Information Security and Dependability at the KIT.