NineChronicles.Headless icon indicating copy to clipboard operation
NineChronicles.Headless copied to clipboard

Arena GQL Simulator - Win % calculator

Open FioX0 opened this issue 2 years ago • 1 comments

Please note that this might be a bit of a resource intensive query. It performs quite well on my Dev PC, but on my VPS I do see a bit of a slowdown but the VPS isn't really strong to begin with.

I have optimized this as much as I could. Might not be suitable for production, but letting planetarium to make that decision.

This allows you to provide 2 AvatarAddresses and it will simulate 1000 fights and return a decimal which would be the % of avatar1 winning against Avatar2.

query{ stateQuery{ arenaPercentageCalculator(avatarAddress:"0x3b7a47daaece48807fc00a310b05bd9f5d26736e", enemyAvatarAddress:"0xab44635462880666daa7f2be5a21c71c1590ff2b") } }

{ "data": { "stateQuery": { "arenaPercentageCalculator": 3 } }, "extensions": {} }

FioX0 avatar Jun 16 '23 09:06 FioX0

This PR has 223 quantified lines of changes. In general, a change size of upto 200 lines is ideal for the best PR experience!


Quantification details

Label      : Large
Size       : +222 -1
Percentile : 62.3%

Total files changed: 9

Change summary by file extension:
.cs : +222 -1

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? :thumbsup:  :ok_hand:  :thumbsdown: (Email) Customize PullRequestQuantifier for this repository.