Moonbit Extract Spec Test

MoonBit Extract Spec & Test

Overview

Reverse-engineer a formal API contract (<pkg>_spec.mbt) and comprehensive test suites from an existing MoonBit implementation. This enables spec-driven testing for already-written code.

When to Use

• User asks to "extract spec from existing implementation"
• User wants to "generate spec-driven tests for a package"
• User requests "create formal API specification from code"
• Converting legacy code to spec-driven development workflow
• Need to document and test existing public API surface

Workflow

1. Analyze the Existing Implementation

Identify public API surface:

• List all pub and pub(all) types, functions, traits
• Note error types (suberrors) and their variants
• Document method signatures and trait implementations
• Identify key entry points and core functionality

Examine test patterns (if tests exist):

• Look for existing test files (*_test.mbt, *_wbtest.mbt)
• Identify test coverage gaps
• Note common test scenarios

2. Generate the Spec File (<pkg>_spec.mbt)

Create formal API contract:

• Use the declare keyword for all public functions/types
• Preserve exact type signatures from implementation
• Include error types with derive(Show, Eq, ToJson) for testability
• Add documentation comments from original code
• Declarations with declare do not have a body
• Use pub(all) for types that need construction in tests
• Keep pub for opaque types

Example spec structure:

///|
/// Error type for parsing operations
pub(all) suberror ParseError {
  InvalidFormat(String)
  UnexpectedEof
} derive(Show, Eq, ToJson)

///|
/// Main data type
declare pub(all) type Toml

///|
/// Convert to test JSON format
declare pub fn Toml::to_test_json(self : Toml) -> Json

///|
/// Parse TOML from string
declare pub fn parse(input : StringView) -> Result[Toml, ParseError]

3. Extract and Organize Test Cases

Categorize tests by validity (happy path vs error path):

Valid tests (<pkg>_valid_test.mbt):

• Happy path: valid inputs that exercise core features
• Edge cases: boundary sizes, empty inputs, maximum nesting
• Compatibility: behaviors that match real-world data or dominant implementations
• Regression: bugs fixed in prior work or known pitfalls

Invalid tests (<pkg>_invalid_test.mbt):

• Error path: invalid inputs and required error behavior
• Ambiguities: inputs where the spec allows multiple interpretations (tests pin down chosen interpretation)
• Malformed inputs: syntax errors, type mismatches, constraint violations

4. Write Spec-Driven Tests

For valid input tests:

///|
test "valid/category/test-name" {
  let toml_text =
    #|key = "value"
    #|
  let toml = match @pkg.parse(toml_text) {
    Ok(toml) => toml
    Err(error) => fail("Failed to parse: " + error.to_string())
  }
  json_inspect(toml.to_test_json(), content={
    "key": { "type": "string", "value": "value" }
  })
}

For invalid input tests:

///|
test "invalid/category/test-name" {
  let toml_text =
    #|invalid syntax here
    #|
  match @pkg.parse(toml_text) {
    Ok(toml) => {
      let json = toml.to_test_json()
      fail("Expected parse to fail, got \{json.stringify(indent=2)}")
    }
    Err(_) => ()
  }
}

5. Validate

Type-check the spec:

moon check

Run the tests:

moon test          # Run all tests
moon test -u       # Update snapshot tests

Verify coverage:

• Ensure all public API functions have tests
• Check for untested error paths
• Confirm edge cases are covered

Key Principles

Spec File Rules

1. Declaration-only stubs: All public API uses declare keyword (no body needed)
2. Exact signatures: Match the implementation's type signatures precisely
3. Error handling: Include all error types with proper derives
4. Documentation: Preserve or add doc comments for clarity
5. Test helpers: Include conversion functions like to_test_json() for inspection

Test Organization

1. Black-box testing: Tests use only public API (call via @pkg.function)
2. Snapshot testing: Prefer json_inspect() for complex values
3. Clear naming: Use descriptive test names like "valid/arrays/nested" or "invalid/keys/empty"
4. Categorization: Organize by validity (<pkg>_valid_test.mbt for happy path, <pkg>_invalid_test.mbt for error path)
5. Block separation: Use ///| to delimit test blocks

Test Coverage Goals

• Positive cases: Valid inputs producing expected outputs
• Negative cases: Invalid inputs producing appropriate errors
• Edge cases: Boundaries, empty inputs, special values
• Integration: Features working together
• Aim for 80-90% API coverage

Example Extraction

From Implementation

pub type Config

pub fn Config::parse(text : String) -> Result[Config, String] {
  // implementation
}

pub fn Config::get_value(self : Config, key : String) -> String? {
  // implementation
}

To Spec

///|
declare pub type Config

///|
declare pub fn Config::parse(text : String) -> Result[Config, String]

///|
declare pub fn Config::get_value(self : Config, key : String) -> String?

To Tests

///|
test "parse valid config" {
  let result = try? Config::parse("key=value")
  match result {
    Ok(cfg) => {
      inspect(cfg.get_value("key"), content="Some(\"value\")")
    }
    Err(e) => fail("Parse failed: \{e}")
  }
}

///|
test "parse invalid config returns error" {
  let result = try? Config::parse("invalid")
  match result {
    Ok(_) => fail("Should have failed to parse")
    Err(_) => ()
  }
}

Tips

• Start simple: Begin with core functionality, add complexity incrementally
• Use existing tests: If tests exist, migrate and enhance them
• Iterate: Run moon check frequently to catch type errors early
• Update snapshots: Use moon test -u when output format changes
• Document reasoning: Add comments explaining non-obvious test cases

Common Patterns

Testing with Result types

let result : Result[T, Error] = try? function_call(...)
match result {
  Ok(value) => inspect(value, content="...")
  Err(e) => fail("Unexpected error: \{e}")
}

Testing expected failures

match try? function_call(...) {
  Ok(v) => fail("Expected error, got \{v}")
  Err(_) => ()  // Success - error was expected
}

Using test JSON format

json_inspect(value.to_test_json(), content={
  "field": { "type": "integer", "value": "42" }
})