ChatGPT has made large language models more accessible and made it possible to code using natural language prompts. This study conducted an experiment comparing prompt engineering techniques called task specification and investigated their impacton code generation in terms of correctness and variety. The hypotheses of this study focused on whether the baseline method had a statistically significant difference in code correctness compared to the other methods. Code is evaluated using a software requirement specification that measures functional and syntactical correctness. Additionally, code variance is measured to identify patterns in code generation. The results show that there is a statistically significant difference in some code correctness criteria between the baseline and the other task specification methods, and the code variance measurements indicate a variety in the generated solutions. Future work could include using another large language model; different programming tasks andprogramming languages; and other prompt engineering techniques.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-22866 |
| Date | January 2023 |
| Creators | Lundblad, Jonathan, Thörn, Edwin, Thörn, Linus |
| Publisher | Högskolan i Skövde, Institutionen för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| 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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