In recent years, the advancements in natural language processing and machine learning have revolutionized various industries and domains. This thesis aims to explore the potential of training a Large Language Model (LLM) on Git source code repositories to enable it to effectively respond to queries regarding the codebase, such as dependencies and functionality.
Thesis Type |
|
Student |
Ilija Kovacevic |
Status |
Finished |
Presentation room |
Seminar room I5 6202 |
Supervisor(s) |
Stefan Decker |
Advisor(s) |
Michal Slupczynski |
Contact |
slupczynski@dbis.rwth-aachen.de |
The main goal of this thesis is to develop and fine-tune a Large Language Model capable of comprehending and answering questions related to Git-based source code repositories.
The proposed LLM-based approach holds promise for enhancing documentation, facilitating modernization efforts of legacy systems, and fostering better stakeholder involvement in machine learning projects.
This research aims to contribute to the software engineering and machine learning fields in multiple ways. The developed LLM has the potential to serve as an intelligent tool for developers, project managers, and stakeholders in understanding and interacting with code repositories.
Co-Advisor: Ada Slupczynski (SWC RWTH)
- DevOps/MLOps
- Python
MILKI-PSY - Multimodales Immersives Lernen mit KI für Psychomotorische Fähigkeiten