Some tests for user-defined meta rules that accept some PEGs as arguments.

[TheVeryDarkness]

This adds some tests about meta rules. Currently only meta rules that accept PEGs can be defined by users, and users can't define a rule that accepts a slice (like pest::stack::peek).

An example that is tested:

separated(i, sep) = i - (sep - i)*
cell()            = ('a'..'z' | 'A'..'Z' | '0'..'9' | ".")+
main              = separated(separated(cell, " "* - "," - " "*), "\n")

This solves a part of #8.

And this PR includes some updates about CI, as previous PR doesn't actually enable benchmarking correctly.

Summary by CodeRabbit

• Chores

  • Updated GitHub Actions workflow to run benchmarks on all branches.
  • Renamed and standardized step names in the CI workflow for consistency.
  • Added comments for future improvements and reminders in the CI workflow.
  • Introduced additional configuration options in Codecov for layout, diffs, and profiling.

• Documentation

  • Enhanced documentation for derive macros, explaining meta rules and rule arguments in struct mapping.

• Tests

  • Added new tests for custom parsing logic using pest3 to convert input strings into JSON arrays.

[coderabbitai]

Walkthrough

In this update, several GitHub workflow files and source code are modified to improve continuous integration (CI) and code benchmarking processes, enhance code documentation, and introduce new testing functionality. Benchmarking is now run on all branches, workflow steps are refined for consistency, code coverage visualization is enhanced, explanations in derive macros are expanded, and a custom parser test using pest3 is added.

Changes

Files	Change Summary
.github/workflows/benchmark.yml	Branch filtering logic altered to run benchmarks on all branches.
.github/workflows/ci.yml	Job and step names standardized; comments added for consistency and future improvements; reminders for Codecov token usage.
codedov.yml	Added configuration for layout, visualization settings, diffs display, flags, and beta profiling options.
derive/src/lib.rs	Enhanced documentation and examples for meta rules, rule arguments, and their application in struct mapping in Rust.
derive/tests/meta_rules.rs	Introduced a custom parser using pest3, with parsing rules to convert input strings into JSON arrays, including test functions to validate the logic.

Sequence Diagram(s)

Skipping this section as the changes are varied and do not include a new feature or significant modifications to control flow in a unified manner.

Poem

In the code where changes flow,
Benchmarks run from branch to branch,
CI steps in a seamless dance,
Explanations clear to enhance,
Parsers parse, and tests advance,
Code coverage gets a glance,
A rabbit's joy in every chance.

🐇✨

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[coderabbitai]

Actionable comments posted: 0

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between b597196 and d9f0c02.

Files selected for processing (5)

• .github/workflows/benchmark.yml (1 hunks)
• .github/workflows/ci.yml (2 hunks)
• codedov.yml (1 hunks)
• derive/src/lib.rs (1 hunks)
• derive/tests/meta_rules.rs (1 hunks)

Files skipped from review due to trivial changes (4)

• .github/workflows/benchmark.yml
• .github/workflows/ci.yml
• codedov.yml
• derive/src/lib.rs

Additional comments not posted (3)

derive/tests/meta_rules.rs (3)

7-13: Define a custom parser with user-defined meta rules.

The parser definition using pest3 and pest3_derive with inline grammar looks correct and effectively utilizes PEGs to define complex parsing rules. This setup follows best practices for defining concise and readable grammars.

---

15-27: Implement a function to convert input strings into JSON arrays.

The function to_json correctly implements the parsing logic using the pest3 library. The use of iterators and chaining methods enhances the readability and efficiency of the code. Good error handling with Result return type.

---

29-42: Extensive testing of the custom parser functionality.

The test cases are well-constructed and cover a variety of scenarios, including success cases and expected failures. This thorough testing ensures the robustness of the parser functionality.