Towards Trustworthy AI Software Development Assistance
Pages 112 - 116
Abstract
It is expected that in the near future, AI software development assistants will play an important role in the software industry. However, current software development assistants tend to be unreliable, often producing incorrect, unsafe, or low-quality code. We seek to resolve these issues by introducing a holistic architecture for constructing, training, and using trustworthy AI software development assistants. In the center of the architecture, there is a foundational LLM trained on datasets representative of real-world coding scenarios and complex software architectures, and fine-tuned on code quality criteria beyond correctness. The LLM will make use of graph-based code representations for advanced semantic comprehension. We envision a knowledge graph integrated into the system to provide up-to-date background knowledge and to enable the assistant to provide appropriate explanations. Finally, a modular framework for constrained decoding will ensure that certain guarantees (e.g., for correctness and security) hold for the generated code.
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Published In
April 2024
127 pages
ISBN:9798400705007
DOI:10.1145/3639476
- Co-chairs:
- Ana Paiva,
- Rui Abreu,
- Robert Hierons,
- Henrique Madeira,
- Program Co-chairs:
- Abhik Roychoudhury,
- Margaret Storey
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 24 May 2024
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