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.

介绍了构建、训练和使用可靠的人工智能软件开发助理的整体架构，其中包括基于真实编码场景和复杂软件架构进行训练的 LLM，利用基于图形的代码表示进行高级语义理解，整合知识图谱以提供最新背景知识，并通过模块化的解码框架确保生成代码的正确性和安全性。