Problems Faced
Building CodeSage — a system that integrates a Tree-Walk Interpreter, AST-based summarization, and NLP-driven code explanation — was a challenging process.
Throughout the project, several major and minor issues were encountered, both on the NLP (Natural Language Processing) side and the compiler/interpreter implementation side.
1. Lack of Suitable Dataset for NLP Training
One of the primary challenges in this project was the unavailability of a proper dataset to train the NLP engine.
- There was no existing dataset that matched the structure of the interpreter’s output or the format of code summaries required.
- Public datasets (like CodeSearchNet or CodeT5 datasets) were either too complex, making them unsuitable for training a summarization model aligned with CodeSage’s interpreter logic.
Solution Attempt
To overcome this, a custom template-based dataset was created: - Templates were manually designed for various code constructs (loops, functions, assignments, etc.). - Each template included example code and a structured explanation. - The dataset was later filtered and aligned with the interpreter’s grammar and AST patterns.
However, despite this effort, the model did not yield accurate or meaningful results due to: - Insufficient training data - Lack of variety in examples - Model overfitting to repetitive patterns
Result
Due to the low accuracy and unstable summarization quality, the project shifted focus from a pure NLP-based summarization to a Tree-based summarization approach.
This allowed deterministic and consistent explanations derived directly from the Abstract Syntax Tree (AST), ensuring interpretability and stability.
2. Complexity in Building Recursive-Descent Parser
Designing a recursive-descent parser manually required handling: - Tokenization edge cases - Parsing ambiguity (especially nested expressions) - Error recovery (panic mode) - Grammar synchronization between parser and interpreter
Even small syntax mistakes often caused cascading parse failures, making debugging time-consuming.
3. Tree-Walk Interpreter Challenges
Implementing a working Tree-Walk Interpreter involved:
- Managing nested scopes and variable environments
- Handling return, break, and continue flow control correctly
- Avoiding infinite loops or stack overflows in recursive evaluation
- Debugging runtime errors that stemmed from incorrect AST traversal
The scope resolution (Resolver) also introduced complexity, since incorrect distance tracking caused runtime variable access errors.
🔍 4. Error Handling and Debugging
Runtime errors (like undefined variables or division by zero) were difficult to trace back to exact source lines.
Adding meaningful error messages and implementing panic mode recovery in the parser required careful thought and testing.
Final Reflection
The biggest learning from these challenges was understanding how theory from compiler design meets the practical constraints of AI and UI development.
While the NLP summarization goal was not fully achieved, the shift to a Tree-based system made CodeSage more reliable, interpretable, and educational — staying true to its core purpose:
“To explain code, not just execute it.”