Epic Test Coverage Boost: Enhance Your Code

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Epic Test Coverage Boost: Enhance Your Code

Hey everyone! 👋 This epic is all about boosting test coverage across all our modules. We're aiming to make our code more robust and reliable. Let's dive into the current state, the improvements we're targeting, and how we're going to get there. This is going to be a fun journey, and together, we can make our code even better! 💪

Current Coverage Assessment: Where We Stand

Before we jump into the details, let's see where we're at with our test coverage. This gives us a baseline to measure our progress and prioritize our efforts. As you can see, the levels vary across different components. Remember, the goal is always to improve, not just meet a number. It's about writing quality tests that help catch bugs and ensure our code works as expected. So, let's take a look:

  • Lexer: ~40% - Right now, we mostly cover the happy path. We need to add tests for error scenarios.
  • Parser: ~50% - We have good tests for expressions, but the tests for statements could be better.
  • Semantic: ~60% - Scope tests are pretty solid, but the type tests need some work.

Issues: The Areas We're Targeting

Now, let's look at the specific areas where we need to improve. We've identified several key issues that we'll address to increase our test coverage and, ultimately, the quality of our code. The following are grouped by component. Each section describes the current state, what we are missing, and how we can approach the improvement.

1. Add Lexer Error Case Tests

Component: Lexer

Current: 0 error case tests. Currently, we're not testing how our lexer handles errors. This means we might miss potential issues when the lexer encounters invalid input. It is crucial to check our lexer’s ability to catch and report errors effectively. That is the point of adding error tests. This is like making sure our code has a safety net to catch problems.

Missing test cases:

  • Tab indentation error
  • Invalid dedentation
  • Indentation not multiple of 2
  • Integer overflow
  • Float overflow/underflow
  • Unterminated strings
  • Invalid escape sequences
  • Empty file
  • File with only comments
  • File with only whitespace
  • Unicode identifiers (if supported)
  • Very deeply nested indentation (50+ levels)

2. Add Parser Error Recovery Tests

Component: Parser

Current: Only 2 error tests. Our parser needs to be able to handle errors gracefully. This means it should detect errors, report them, and attempt to recover to continue parsing valid parts of the code. We want to test how it responds to various errors and ensures that it recovers correctly. This will help us prevent cascading errors and improve the overall user experience.

Missing:

  • Multiple errors in one file
  • Recovery from missing colons
  • Recovery from missing indentation
  • Invalid assignment targets
  • Incomplete statements
  • Mismatched brackets/parentheses
  • Invalid operator usage

3. Add Parser Edge Case Tests

Component: Parser

We need tests that focus on the less common or boundary conditions. Edge cases are where the parser might behave unexpectedly, so testing these is critical. These test cases help ensure that our parser correctly handles various edge cases, leading to more robust and reliable parsing.

Missing:

  • Empty list: []
  • Empty dict: {}
  • Trailing commas: [1, 2, 3,]
  • Long expressions (20+ nested operations)
  • All operators combined in complex expressions
  • Lambda in various positions
  • Method chaining: obj.a().b().c()
  • Nested indexing: matrix[i][j]
  • Deeply nested structures

4. Add Semantic Complex Control Flow Tests

Component: Semantic

Semantic analysis checks that the code makes sense, e.g., types are correct, and variables are declared. Testing complex control flows such as multiple if/else blocks, nested loops, and function calls ensures our semantic analysis handles these scenarios correctly. This will enhance the overall stability and reliability of our code.

Missing:

  • Multiple elif blocks (currently only 1 tested)
  • Nested if/while/for combinations
  • Break/continue at various nesting levels
  • Return in nested blocks
  • Function calling function calling function (deep call stack)
  • Mutual recursion

5. Add Type Unification Tests

Component: Semantic

Type Unification is how our code figures out the types of variables and expressions. We need to add tests for scenarios where types might change or be inferred in complicated ways. This includes making sure our code handles different data types, type conversions, and complex expressions involving various types. It's about ensuring our code can handle mixed types correctly and doesn't crash in unexpected ways.

Missing:

  • Variables changing types across if/else branches
  • Multiple return types unification
  • Complex numeric type mixing (int/float)
  • Type inference through function calls
  • Generic type constraints (future)

6. Add Class Feature Tests

Component: Semantic

Right now, our class feature tests are pretty limited. We only test the methods. We need more tests to ensure that classes work correctly. Classes are an essential part of object-oriented programming. Comprehensive class feature tests are crucial for verifying that methods, attributes, and inheritance function as expected.

Current: Only tests that methods have self

Missing:

  • Calling class methods
  • __init__ method validation
  • Attribute access on instances
  • Instance creation
  • Method resolution
  • Class inheritance (when implemented)

7. Add Lambda Edge Case Tests

Component: Semantic

Lambdas are anonymous functions. We should also add tests for edge cases. Edge case tests will verify that our lambda functions behave correctly in various scenarios, including nested lambdas and lambdas with default arguments.

Missing:

  • Lambda with no parameters: lambda: 42
  • Lambda capturing outer variables
  • Lambda in function call: map(lambda x: x*2, [1,2,3])
  • Nested lambdas
  • Lambda returning lambda
  • Lambda with default arguments (if supported)

8. Add Property-Based Tests

Component: All

Property-based tests automatically generate a wide range of inputs and check if your code behaves as expected for all of them. This helps find edge cases you might not have thought of. It's like having a robot that keeps trying different inputs until it finds something that breaks your code. This method is used to verify the program against different inputs, increasing the confidence of the code's accuracy.

Recommendation: Add proptest or similar

Goals:

  • Generate random token sequences for lexer fuzzing
  • Generate random AST trees for semantic analysis
  • Generate random expression combinations for type checking
  • Discover edge cases automatically

Example:

proptest! {
    #[test]
    fn lexer_doesnt_crash(s in "\PC*") {
        let _ = lex(&s);  // Should never panic
    }
}

9. Add Performance/Stress Tests

Component: All

Performance/stress tests are designed to check how our code performs under heavy loads or extreme conditions. These tests help ensure that our code remains performant and doesn't have any unexpected bottlenecks. The performance test is very important. Performance tests help us to discover potential bottlenecks and ensure that our code is efficient.

Missing:

  • Large file (10,000+ lines)
  • Deep nesting (100+ levels)
  • Long expressions (1000+ tokens)
  • Many variables (1000+)
  • Large lists/dicts (10,000+ elements)
  • Benchmark regression tests

10. Add Integration Tests

Component: Pipeline (Lexer → Parser → Semantic)

Integration tests will verify that the lexer, parser, and semantic analysis components work together correctly. This will help ensure the entire pipeline functions smoothly. It's like testing the entire assembly line to catch any issues early on.

Missing:

Tests that exercise the full frontend pipeline without going to execution:

  1. Take raw source string
  2. Lex it
  3. Parse it
  4. Analyze it
  5. Verify all three stages work together correctly

Current: E2E tests go all the way to execution, but no middle ground testing

Test Infrastructure Improvements

Apart from adding new tests, we also need to improve our test infrastructure. These improvements will make it easier to write, run, and maintain our tests. It's about creating a more streamlined and efficient testing process, and they play a vital role in our testing strategy.

11. Add Test Organization

We need to improve how we organize our tests. Well-organized tests are easier to understand, maintain, and expand. It will make it easier to manage and find our tests.

  • Organize tests by module/component
  • Use descriptive test names
  • Add test documentation
  • Group related tests with mod blocks

12. Add CI/CD Test Reporting

CI/CD (Continuous Integration/Continuous Deployment) helps us automate our testing process. This makes it easier to run tests and get quick feedback on our code. CI/CD integration will help us run our tests automatically every time we make changes to the code. This will help us catch problems earlier.

  • Code coverage reporting (tarpaulin)
  • Test result visualization
  • Performance regression detection
  • Automated test running on PR

13. Add Snapshot Testing

Snapshot testing is used to verify the output of your code against a saved snapshot. This is useful for comparing error messages, ensuring that your output is consistent, and identifying unexpected changes. These tests are helpful for ensuring that our error messages are consistent and don't change unexpectedly.

For error messages and diagnostics:

#[test]
fn test_type_error_message() {
    let result = analyze("x = 1 + \"hello\"");
    insta::assert_snapshot!(result.unwrap_err().to_string());
}

Acceptance Criteria: What Success Looks Like

We need to define clear acceptance criteria to measure the success of this epic. This will help ensure that we meet our goals and improve the overall quality of our code. The following criteria will help us determine if we've successfully improved test coverage.

  • Lexer error cases: 100% coverage
  • Parser edge cases: 20+ new tests
  • Semantic complex flows: 15+ new tests
  • Property-based tests integrated
  • Performance/stress tests added
  • Integration tests for frontend pipeline
  • Overall code coverage: >80%
  • CI/CD test reporting set up