example for hardfork DailyReward

[ipdae]

No description provided.

[pull-request-quantifier-deprecated]

This PR has 86 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       : +73 -13
Percentile : 34.4%

Total files changed: 5

Change summary by file extension:
.cs : +73 -13

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.

---

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Customize PullRequestQuantifier for this repository.

[sonohoshi]

어차피 이 IValue의 Address를 AvatarAddress로 가져올거면 굳이 AgentAddress를 들고있을 이유가 있을까요?

[riemannulus]

나중에 statestore 에서 직접 값 읽어보고 할 때 편하긴 할 겁니다.

[sonohoshi]

상태만 덜렁 들고있을때 이게 어떤 아바타의 DailyRewardInfo인지 헷갈릴 것 같다 싶긴했는데, 음... 네 상관없을 것 같네요

[ipdae]

제약조건(아바타어드레스가 에이전트어드레스종속적인지)를 확인할 방법이 AgentState나 AvatarState를 직접 확인하는 방법밖에 없었어서 DailyRewardInfo에 넣어놨는데, 생각해보니 별도 어카운트에 넣어두거나(다른 액션들도 확인할 수 있으니) 액션안에서는 Derive를 해보는게 낫겠다 싶네요.

[pull-request-quantifier-deprecated]

This PR has 86 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       : +73 -13
Percentile : 34.4%

Total files changed: 5

Change summary by file extension:
.cs : +73 -13

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.