An in-depth study of software library upgrade dependency issues
With the rapid advancements in Artificial Intelligence and Large Language Models, the potential to leverage cutting-edge AI for addressing code-related issues is continually growing. This study explores the potential of utilizing AI and evaluate its effectiveness in resolving software dependen...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/181284 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | With the rapid advancements in Artificial Intelligence and Large Language Models, the
potential to leverage cutting-edge AI for addressing code-related issues is continually growing.
This study explores the potential of utilizing AI and evaluate its effectiveness in resolving
software dependency upgrade incompatibility issues. We developed an Automated Repair
Program SLUDI and evaluated the effectiveness of three different large language models
against a dataset of 30 upgrade incompatibility issues. Results show that the models achieved
an average of 38.9% identification rate and 41.1% correctness rate. The evaluation results show
that given only the exception information, source code of the method, context of the library
upgraded, the large language models are not very effective in resolving software dependency
upgrade incompatibility issues. Future research recommendations include extracting and
providing more crucial information to the AI, enabling them to gain a deeper understanding of
the project, which could improve their ability to identify and fix the incompatibility issue with
greater accuracy. Exploring automatic repair methods could also be implemented to enhance
efficiency and reduce the potential for human error. |
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