Add matchesPattern support

[Arithmomaniac]

Issues

This pull request fixes #1901.

Description

Adds support for the matchesPattern function introduced in OData v4. In addition to being added as a supported function to the raw parser, it is mapped in .NET to Regex.IsMatch(input, pattern, RegexOptions.ECMAScript).

The basic methodology was to look for any C# file in the repository that handled all of the string predicates (startswith, endswith, etc.) and added matchesPattern to that set. There was at least one legacy provider with no support for e.g. contains; that provider was unmodified.

As part of this work, the case-insensitivity logic had to be changed to account for matchesPattern being the first OData function whose canonical name is mixed-case.

Checklist

• Test cases added
• Build and test with one-click build and test script passed
  • Almost of the tests passed locally
  • Some unit tests failed intermittently from the build script but worked in VS (different tests each time)
  • Astoria complained about Unable to cast COM object of type 'AstoriaUnitTests.Tests.JScriptEngine'...)

[mikepizzo]

[pull-request-quantifier-deprecated]

This PR has 100 quantified lines of changes. In general, a change size of upto 200 lines is ideal for the best PR experience!

---

Quantification details

Label      : Small
Size       : +90 -10
Percentile : 40%

Total files changed: 15

Change summary by file extension:
.cs : +90 -10

Change counts above are quantified counts, based on the PullRequestQuantifier customizations.

Why proper sizing of changes matters

Optimal pull request sizes drive a better predictable PR flow as they strike a
balance between between PR complexity and PR review overhead. PRs within the
optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
    iterations.
  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
  • Solving big problems by dividing them in well contained, smaller problems.
  • Exercising separation of concerns within the code changes.

What can I do to optimize my changes

• Use the PullRequestQuantifier to quantify your PR accurately
  • Create a context profile for your repo using the context generator
  • Exclude files that are not necessary to be reviewed or do not increase the review complexity. Example: Autogenerated code, docs, project IDE setting files, binaries, etc. Check out the Excluded section from your prquantifier.yaml context profile.
  • Understand your typical change complexity, drive towards the desired complexity by adjusting the label mapping in your prquantifier.yaml context profile.
  • Only use the labels that matter to you, see context specification to customize your prquantifier.yaml context profile.
• Change your engineering behaviors
  • For PRs that fall outside of the desired spectrum, review the details and check if:
    • Your PR could be split in smaller, self-contained PRs instead
    • Your PR only solves one particular issue. (For example, don't refactor and code new features in the same PR).

How to interpret the change counts in git diff output

• One line was added: +1 -0
• One line was deleted: +0 -1
• One line was modified: +1 -1 (git diff doesn't know about modified, it will
  interpret that line like one addition plus one deletion)
• Change percentiles: Change characteristics (addition, deletion, modification)
  of this PR in relation to all other PRs within the repository.

---

Was this comment helpful? 👍  :ok_hand:  :thumbsdown: (Email)
Customize PullRequestQuantifier for this repository.