Advanced Coding Assistant: Knowledge Graphs and ASTs

As AI systems evolve, their ability to understand and generate code is becoming increasingly sophisticated. However, current AI coding assistants often lack the deep contextual understanding necessary to provide truly intelligent and efficient coding support. This is where knowledge graphs and abstract syntax trees (ASTs) come into play.

Knowledge graphs, which represent entities and their relationships in a structured format, have the potential to provide AI with a more comprehensive understanding of a codebase (Knowledge Graphs: Opportunities and Challenges; Application of Knowledge Graph Technology with Integrated Feature Data in Spacecraft Anomaly Detection). By encoding not just the code itself, but also the interconnections between different components, knowledge graphs enable AI to reason about code at a higher level of abstraction (Turn tree into graph: Automatic code review via simplified AST driven graph convolutional network).

ASTs, on the other hand, provide a structured representation of code that captures its syntactic structure(Program Slicing on Code Property Graphs; Source Code Vulnerability Detection: Combining Code Language Models and Code Property Graphs). By combining ASTs with additional semantic information like control flow and data dependencies, we can create a unified representation that encapsulates both the static structure and dynamic behavior of code (Learning Graph-based Code Representations for Source-level Functional Similarity Detection)

AST Knowledge Graph

The integration of knowledge graphs and ASTs opens up exciting possibilities for AI coding assistants. Imagine an AI that not only understands the syntax of your code but also grasps the underlying design patterns, architectural decisions, and domain-specific concepts. Such an AI could provide intelligent suggestions, catch potential bugs, and even propose optimizations based on a holistic understanding of your codebase.

This is precisely what we at Deutsche Telekom in our small research team aim to achieve (big thanks to our main researcher Zimin Chen) with internal research prototype, the Advanced Coding Assistant. By using knowledge graphs and ASTs, this chatbot will be able to provide developers with a high level of coding support (Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering).

Benefits of Integrating Knowledge Graphs and ASTs

The integration of knowledge graphs and ASTs brings several significant benefits to the Advanced Coding Assistant:

1. Enhanced Contextual Understanding: By combining syntactic and semantic information, the chatbot gains a comprehensive understanding of the codebase. This allows it to provide more accurate suggestions and identify potential issues that might be missed with a purely syntactic or semantic analysis (Unified Abstract Syntax Tree Representation Learning for Cross-Language Program Classification).
2. Improved Code Quality: With its deep understanding of code structures and relationships, the chatbot can suggest best practices, design patterns, and optimizations. This leads to cleaner, more maintainable, and efficient code.
3. Bug Detection and Prevention: The chatbot’s ability to reason about code at a high level enables it to catch subtle bugs and logical errors. This reduces the risk of costly errors and improves the overall reliability of the software (Source Code Vulnerability Detection: Combining Code Language Models and Code Property Graphs).
4. Personalized Assistance: By leveraging LLMs and knowledge graphs, the chatbot can provide personalized coding support tailored to the specific needs and preferences of individual developers or teams (Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering).

Of course, building such a system is not simple. It requires advanced techniques in natural language processing, graph representation learning, and program analysis (An Intro to the Code Property Graph: Learn How to Leverage Graph-Oriented Databases for Source Code Analysis).

Technologies

To achieve the level of sophistication we want to bring to our developers with Advanced Coding Assistant, we decided to focus on several modern technologies that enable us to move forward:

• LangChain4J — this framework enables us freely explore different LLM integrations
• Neo4J — a state of the art Graph DB
• TreeSitter — parser generator tool. It can build a concrete syntax tree for a source file

Graphs for code represenation are crucial (Knowledge Graphs & LLMs: Multi-Hop Question Answering). They allow us to model complex relationships and dependencies within the code. These representations are particularly effective when combined with large language models (LLMs), which can leverage the rich contextual information encoded in the graphs to provide more accurate and contextually aware responses.

We are excited to test our Advanced Coding Assistant prototype and witness firsthand how the integration of knowledge graphs and ASTs can change coding support and tools.