Crii:OAC:towards automated ontology generation for software evolution utilizing large language models

Charleston Southern University received a $174,408 Project Grant from the National Science Foundation's (NSF) Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective August 1, 2025 through July 31, 2027. This Computer Research Infrastructure Initiative (CRII) award supports the development of automated ontology generation technologies for software evolution utilizing large language models (LLMs). The project addresses the challenge of extracting and representing knowledge from voluminous software development data through innovative application of LLMs to automatically generate ontologies from software engineering lifecycle artifacts.

The award will produce several key deliverables including custom metrics for assessing ontology usability in downstream applications such as change impact analysis, version comparison, and code generation. Additionally, the project will develop software tools supporting the ontology generation automation workflow, along with capabilities for visualizing and refining generated ontologies. The resulting ontology repositories and supporting tools are intended to improve understanding and enable greater automation in evolving software systems. No sub-awards are planned for this project. The university will serve as the sole performer of work at its North Charleston, South Carolina location.

Abstract

Software development processes produce large volumes of data that are often overlooked, difficult to visualize, and challenging to revisit or trace over time. These assets contain valuable knowledge that, if properly extracted, can support meaningful visualization and intelligent reasoning. Ontology is widely recognized as a practical approach to knowledge representation and enables efficient reasoning over structured information. A key challenge in ontology generation for software processes is that its automation cannot scale to handle the continuously accumulating and evolving data. This project aims to resolve this issue by innovatively applying large language models to automatically generate ontologies from data created in software engineering life cycles. By bridging knowledge representation and software process, this project produces ontologies that enable intelligent reasoning for software development tasks and broader stakeholder engagement with software systems. The project introduces custom metrics to assess the usability of the ontologies for downstream tasks such as change impact analysis, version comparison, and code generation. IT also develops tools for supporting the ontology generation automation workflow and visualizing and refining ontologies. The resulting ontology repositories and software tools contribute to improved understanding and automation in evolving software systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.