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# CodeGen Documentation
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CodeGen is the process for auto-generating language-specific, strongly-typed ASTs to be used in [Semantic](https://github.com/github/semantic-code/blob/d9f91a05dc30a61b9ff8c536d75661d417f3c506/design-docs/precise-code-navigation.md).
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### Prerequisites
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To get started, first make sure your language has:
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1. An existing [tree-sitter](http://tree-sitter.github.io/tree-sitter/) parser;
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2. An existing Cabal package in [tree-sitter](http://tree-sitter.github.io/tree-sitter/) for said language. This will provide an interface into tree-sitter's C source. [Here](https://github.com/tree-sitter/haskell-tree-sitter/tree/master/tree-sitter-python) is an example of a library for Python, a supported language that the remaining documentation will refer to.
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### CodeGen Pipeline
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During parser generation, tree-sitter produces a JSON file that captures the structure of a language's grammar. Based on this, we're able to derive datatypes representing surface languages, and then use those datatypes to generically build ASTs. This automates the engineering effort [historically required for adding a new language](https://github.com/github/semantic/blob/master/docs/adding-new-languages.md).
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The following steps provide a high-level outline of the process:
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1. [**Deserialize.**](https://github.com/github/semantic/blob/master/semantic-ast/src/AST/Deserialize.hs) First, we deserialize the `node-types.json` file for a given language into the desired shape of datatypes via parsing capabilities afforded by the [Aeson](http://hackage.haskell.org/package/aeson) library. There are four distinct types represented in the node-types.json file takes on: sums, products, named leaves and anonymous leaves.
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2. [**Generate Syntax.**](https://github.com/github/semantic/blob/master/semantic-ast/src/AST/GenerateSyntax.hs) We then use Template Haskell to auto-generate language-specific, strongly-typed datatypes that represent various language constructs. This API exports the top-level function `astDeclarationsForLanguage` to auto-generate datatypes at compile-time, which is is invoked by a given language [AST](https://github.com/github/semantic/blob/master/semantic-python/src/Language/Python/AST.hs) module.
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3. [**Unmarshal.**](https://github.com/github/semantic/blob/master/semantic-ast/src/AST/Unmarshal.hs) Unmarshaling is the process of iterating over tree-sitter’s parse trees using its tree cursor API, and producing Haskell ASTs for the relevant nodes. We parse source code from tree-sitter and unmarshal the data we get to build these ASTs generically. This file exports the top-level function `parseByteString`, which takes source code and a language as arguments, and produces an AST.
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Here is an example that describes the relationship between a Python identifier represented in the tree-sitter generated JSON file, and a datatype generated by Template Haskell based on the provided JSON:
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| Type | JSON | TH-generated code |
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|----------|--------------|------------|
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|Named leaf|<code>{<br>"type": "identifier",<br>"named": true<br>}|<code>data TreeSitter.Python.AST.Identifier a<br>= TreeSitter.Python.AST.Identifier {TreeSitter.Python.AST.ann :: a,<br>TreeSitter.Python.AST.bytes :: text-1.2.3.1:Data.Text.Internal.Text} -- Defined at TreeSitter/Python/AST.hs:10:1<br>instance Show a => Show (TreeSitter.Python.AST.Identifier a) -- Defined at TreeSitter/Python/AST.hs:10:1<br>instance Ord a => Ord (TreeSitter.Python.AST.Identifier a) -- Defined at TreeSitter/Python/AST.hs:10:1<br>instance Eq a => Eq (TreeSitter.Python.AST.Identifier a) -- Defined at TreeSitter/Python/AST.hs:10:1<br>instance Traversable TreeSitter.Python.AST.Identifier -- Defined at TreeSitter/Python/AST.hs:10:1<br>instance Functor TreeSitter.Python.AST.Identifier -- Defined at TreeSitter/Python/AST.hs:10:1<br>instance Foldable TreeSitter.Python.AST.Identifier -- Defined at TreeSitter/Python/AST.hs:10:1<br>instance Unmarshal TreeSitter.Python.AST.Identifier -- Defined at TreeSitter/Python/AST.hs:10:1<br>instance SymbolMatching TreeSitter.Python.AST.Identifier -- Defined at TreeSitter/Python/AST.hs:10:1|
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The remaining document provides more details on generating ASTs, inspecting datatypes, tests, and information on decisions pertaining to relevant APIs.
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___
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### Table of Contents
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- [CodeGen Documentation](#codegen-documentation)
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- [Prerequisites](#prerequisites)
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- [CodeGen Pipeline](#codegen-pipeline)
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- [Table of Contents](#table-of-contents)
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- [Generating ASTs](#generating-asts)
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- [Inspecting auto-generated datatypes](#inspecting-auto-generated-datatypes)
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- [Tests](#tests)
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- [Additional notes](#additional-notes)
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___
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### Generating ASTs
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To parse source code and produce ASTs locally:
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1. Load the REPL for a given language package:
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```
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cabal new-repl lib:semantic-python
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```
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2. Set language extensions, `OverloadedStrings` and `TypeApplications`, and import relevant modules, `AST.Unmarshal`, `Source.Range` and `Source.Span`:
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```
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:seti -XOverloadedStrings
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:seti -XTypeApplications
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import Source.Span
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import Source.Range
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import AST.Unmarshal
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```
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3. You can now call `parseByteString`, passing in the desired language you wish to parse (in this case Python is given by the argument `Language.Python.Grammar.tree_sitter_python`), and the source code (in this case an integer `1`). Since the function is constrained by `(Unmarshal t, UnmarshalAnn a)`, you can use type applications to provide a top-level node `t`, an entry point into the tree, in addition to a polymorphic annotation `a` used to represent range and span. In this case, that top-level root node is `Module`, and the annotation is given by `Span` and `Range` as defined in the [semantic-source](https://github.com/github/semantic/tree/master/semantic-source/src/Source) package:
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```
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TS.parseByteString @Language.Python.AST.Module @(Source.Span.Span, Source.Range.Range) Language.Python.Grammar.tree_sitter_python "1"
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```
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This generates the following AST:
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```
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Right (Module {ann = (Span {start = Pos {line = 0, column = 0}, end = Pos {line = 0, column = 1}},Range {start = 0, end = 1}), extraChildren = [R1 (SimpleStatement {getSimpleStatement = L1 (R1 (R1 (L1 (ExpressionStatement {ann = (Span {start = Pos {line = 0, column = 0}, end = Pos {line = 0, column = 1}},Range {start = 0, end = 1}), extraChildren = L1 (L1 (Expression {getExpression = L1 (L1 (L1 (PrimaryExpression {getPrimaryExpression = R1 (L1 (L1 (L1 (Integer {ann = (Span {start = Pos {line = 0, column = 0}, end = Pos {line = 0, column = 1}},Range {start = 0, end = 1}), text = "1"}))))})))})) :| []}))))})]})
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```
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### Inspecting auto-generated datatypes
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Datatypes are derived from a language and its `node-types.json` file using the `GenerateSyntax` API. These datatypes can be viewed in the REPL just as they would for any other datatype, using `:i` after loading the language-specific `AST.hs` module for a given language.
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```
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:l semantic-python/src/Language/Python/AST.hs
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Ok, six modules loaded.
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*Language.Python.AST Source.Span Source.Range> :i Module
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```
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This shows us the auto-generated `Module` datatype:
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```Haskell
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data Module a
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= Module {Language.Python.AST.ann :: a,
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Language.Python.AST.extraChildren :: [(GHC.Generics.:+:)
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CompoundStatement SimpleStatement a]}
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-- Defined at /Users/aymannadeem/github/semantic/semantic-python/src/Language/Python/AST.hs:23:1
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instance Show a => Show (Module a)
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-- Defined at /Users/aymannadeem/github/semantic/semantic-python/src/Language/Python/AST.hs:23:1
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instance Ord a => Ord (Module a)
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-- Defined at /Users/aymannadeem/github/semantic/semantic-python/src/Language/Python/AST.hs:23:1
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instance Eq a => Eq (Module a)
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-- Defined at /Users/aymannadeem/github/semantic/semantic-python/src/Language/Python/AST.hs:23:1
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instance Traversable Module
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-- Defined at /Users/aymannadeem/github/semantic/semantic-python/src/Language/Python/AST.hs:23:1
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instance Functor Module
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-- Defined at /Users/aymannadeem/github/semantic/semantic-python/src/Language/Python/AST.hs:23:1
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instance Foldable Module
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-- Defined at /Users/aymannadeem/github/semantic/semantic-python/src/Language/Python/AST.hs:23:1
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```
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### Tests
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As of right now, Hedgehog tests are minimal and only in place for the Python library.
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To run tests:
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`cabal v2-test semantic-python`
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### Additional notes
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- [GenerateSyntax](https://github.com/tree-sitter/haskell-tree-sitter/blob/master/tree-sitter/src/TreeSitter/GenerateSyntax.hs) provides a way to pre-define certain datatypes for which Template Haskell is not used. Any datatypes among the node types which have already been defined in the module where the splice is run will be skipped, allowing customization of the representation of parts of the tree. While this gives us flexibility, we encourage that this is used sparingly, as it imposes extra maintenance burden, particularly when the grammar is changed. This may be used to e.g. parse literals into Haskell equivalents (e.g. parsing the textual contents of integer literals into `Integer`s), and may require defining `TS.UnmarshalAnn` or `TS.SymbolMatching` instances for (parts of) the custom datatypes, depending on where and how the datatype occurs in the generated tree, in addition to the usual `Foldable`, `Functor`, etc. instances provided for generated datatypes.
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- Annotations are captured by a polymorphic parameter `a`
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- [Unmarshal](https://github.com/tree-sitter/haskell-tree-sitter/blob/master/tree-sitter/src/TreeSitter/Unmarshal.hs) defines both generic and non-generic classes. This is because generic behaviors are different than what we get non-generically, and in the case of ` Maybe`, `[]`—we actually preference doing things non-generically. Since `[]` is a sum, the generic behavior for `:+:` would be invoked and expect that we’d have repetitions represented in the parse tree as right-nested singly-linked lists (ex., `(a (b (c (d…))))`) rather than as just consecutive sibling nodes (ex., `(a b c ...d)`, which is what our trees have). We want to match the latter.
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