This bachelor’s thesis investigates the efficacy of cutting-edge Large Language Models (LLMs) — GPT-4, Code Llama Instruct (7B parameters), and Gemini 1.0 — in detecting and correcting bugs in Java and Python code. Through a controlled experiment using standardized prompts and the QuixBugs dataset, each model's performance was analyzed and compared. The study highlights significant differences in the ability of these LLMs to correctly identify and fix programming bugs, showcasing a comparative advantage in handling Python over Java. Results suggest that while all these models are capable of identifying bugs, their effectiveness varies significantly between models. The insights gained from this research aim to aid software developers and AI researchers in selecting appropriate LLMs for integration into development workflows, enhancing the efficiency of bug management processes.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-130529 |
| Date | January 2024 |
| Creators | Gustafsson, Elias, Flystam, Iris |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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