Objective

Implement statistical approximation techniques to simulate combinatoric code analysis without state explosion.

Features

1. Code2Vec Embeddings: Implement an encoder to map AST subtrees to vector space.
   • Use trueno for tensor acceleration.
   • Goal: Cluster similar code structures (e.g., "Homogeneous List") to map centroids rather than instances.
2. Graph Neural Networks (GNN): Integrate GNN capabilities to process code graphs (AST + Data Flow).
   • Propagate type and lifetime constraints across the graph.
   • Predict node attributes (types) based on neighbors.

Implementation

• Add aprender::code module.
• Implement AST-to-Vector encoder.
• Implement Message Passing Neural Network (MPNN) layers using trueno.

References

• Alon et al. (2019), 'code2vec: Learning distributed representations of code'.
• Allamanis et al. (2018), 'A survey of machine learning for big code'.