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README.md
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---
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language:
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- en
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tags:
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- software-engineering
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- code-analysis
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- refactoring
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- AST
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- lexical-features
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- Python
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- static-analysis
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task_categories:
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- code-understanding
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- code-search
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- software-quality
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pretty_name: "Python Function-Level AST and Lexical Features Dataset"
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dataset_name: "python_function_ast_lexical_features"
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size_categories:
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- 1M<n<100M
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---
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# π Python Function-Level AST and Lexical Features Dataset
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## π Dataset Summary
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This dataset integrates **function-level static analysis data** from Python repositories.
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It merges three data sources:
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1. **Lizard Cyclomatic Analysis**: provides complexity, NLOC, and basic function metadata.
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2. **AST Extracted Features**: includes detailed abstract syntax tree information such as token counts, parameters, variable extraction, and function bodies.
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3. **Lexical Features**: captures lexical and structural features of each function (e.g., class structure, modifiers, incoming/outgoing calls, and statement types).
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The resulting **Dataset** represents each Python function as a unified row combining complexity metrics, lexical information, and AST structure β enabling advanced research in:
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- Code comprehension
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- Automated refactoring
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- Software quality analysis
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- Function-level code search
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- Maintainability prediction
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- ML model training for software engineering tasks
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---
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## π Dataset Structure
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Each row corresponds to **one Python function** identified across open-source repositories.
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### Columns
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| Column name | Description |
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|------------------------------|---------------------------------------------------------------------------------------------------|
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| `project_name` | Repository name |
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| `class_name` | Class name containing the function (if applicable) |
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| `class_modifiers` | Access modifiers of the class |
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| `class_implements` | Interfaces implemented |
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| `class_extends` | Class inheritance |
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| `function_name` | Function name |
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| `function_body` | Raw function body code |
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| `cyclomatic_complexity` | Cyclomatic complexity measure |
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| `NLOC` | Number of lines of code |
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| `num_parameter` | Number of function parameters |
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| `num_token` | Number of tokens |
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| `num_variable` | Number of variables detected in function |
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| `start_line` / `end_line` | Start and end line of the function in the file |
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| `function_index` | Function index in AST parsing |
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| `function_params` | Parameter names |
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| `function_variable` | Variable names extracted |
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| `function_return_type` | Return type (if inferred) |
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| `function_body_line_type`| Mapping of statement types inside the function (e.g., Assign, If, Return) |
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| `function_num_functions` | Number of functions declared inside |
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| `function_num_lines` | Number of lines of function (lexical) |
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| `outgoing_function_count` / `outgoing_function_names` | Number and names of functions called inside this function |
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| `incoming_function_count` / `incoming_function_names` | Number and names of functions calling this function |
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---
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## βοΈ Data Sources
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- Static analysis performed on public Python repositories cloned from [GitHub](https://github.com) that apply the following characteristics (python language projects, commits > 500, and contributors > 10).
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- Function-level analysis uses:
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- [`lizard`](https://github.com/terryyin/lizard) for cyclomatic complexity.
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- Python AST parsing for structural features.
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- Custom lexical parser for lexical metadata and call graphs.
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---
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## π§ͺ Dataset Creation
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### Collection
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1. Clone open-source Python repositories.
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2. Run Lizard static analysis to generate base metrics.
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3. Parse source files to extract AST and lexical features.
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4. Merge the three sources
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