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Semantic tree

As explained in Software Testing with Generative AI

A semantic tree is a hierarchical representation of source-code constructs and their structural relationships.

A semantic tree represents source-code constructs and their relationships so data-preparation tooling can preserve useful context without reducing a codebase to isolated lines. section 12.2.1

A file-processing pipeline can use JavaParser to ingest each file, build the tree, and query class declarations, variables, methods, and containing code for a fine-tuning dataset. section 12.2.1

Extracting contextual code data

Rather than storing only a method line, a dataset can retain the surrounding class declaration, declared variables, method, and containing code. This preserves context while avoiding excessive granularity. section 12.2.1

How Software Testing with Generative AI covers it

How deep it goes
Explains construction and querying through JavaParser but does not detail the tree’s internal node model.
Examples
Uses class declarations, variables, methods, and containing code as extracted dataset entries.
What it assumes
Assumes source files that can be parsed and a need to organize code into training or fine-tuning data.
What you can do after
Apply it via JavaParser to extract structurally contextual code data for rapid dataset experimentation.
How it connects
Connects source-code parsing with JavaParser, dataset preparation, training data, and fine-tuning.

This explainer was generated by Manning's knowledge graph from the text of Software Testing with Generative AI. The section links point to the parts of the book it draws on.