Abstract syntax tree

Source code begins as characters, while an abstract syntax tree organizes expressions, statements, and declarations into nodes. A binary-operation node can contain an operator and two inputs; a call node contains a callable and arguments. Tools can traverse these components structurally instead of repeatedly reconstructing their relationships from raw characters.

References: [1]

Operators on one line need not execute in written order. Parsing records the grouping established by precedence and parentheses, for example placing multiplication beneath addition. LLVM’s tutorial combines recursive-descent parsing with operator-precedence parsing so later stages receive an explicit expression structure rather than an ambiguous sequence of tokens.

References: [2]

An abstract syntax tree commonly omits some punctuation and formatting, allowing different spellings to share a similar structure. Retaining comments, whitespace, or original parentheses depends on the representation. A tool that must reproduce a file faithfully generally needs additional information beyond the semantic structure recorded by a basic abstract tree.

References: [1]

Trees support type checking, static analysis, and code generation, and tools can transform nodes before producing new code. Python’s ast module provides parsing, traversal, and transformation facilities. Compilation still requires valid nodes and location information; resolving names and predicting effects require analyses beyond the tree’s account of syntactic organization.

Producing a tree does not complete all validity checks. Python ast.parse can construct a return-statement node for return 42, yet compiling it as a standalone program raises SyntaxError because it must occur inside a function. Compilation performs scope checks that parsing omits. Forming a node, placing it in a valid context, and obtaining the intended execution result are therefore separate questions.

References: [2] [3]