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The refactoring is now published as Slice on sammancoaching.org: https://sammancoaching.org/refactorings/slice.html - git mv ideas/split-loop-with-lambda -> ideas/slice-with-lambda - rename learning-hour file and update frontmatter name - update paths in tools/rehearse scripts and report title - update prose: 'Split Loop with Lambda' -> 'Slice with Lambda' in learning hour, exercise READMEs, storyboard, solution comments - link the published Slice refactoring from the pattern draft
28 lines
1.4 KiB
Markdown
28 lines
1.4 KiB
Markdown
# Reference solution — end state after step 3
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Derived from [IceCreamForecasting-Refactoring-Kata](https://github.com/emilybache/IceCreamForecasting-Refactoring-Kata)
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(same license as the kata applies to the copied `IceCream.java`).
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Run with `mvn test` — the approval test passes deterministically without
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touching the weather service.
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## What changed vs the exercise start
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1. Weather loop extracted to `collectExpectedWeather(LongFunction<Boolean>)`
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— a static method whose only "unwanted dependency" is the lambda parameter.
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2. Public no-arg `longRangeForecast()` delegates to a new overload that takes
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the weather function, keeping existing callers working.
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3. Test passes a pure lambda (`daysForward -> true`) — the original test
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called the no-arg method and was flaky because of `Scorer`'s `Random`.
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## Variations to try (for the debrief or a second round)
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- `daysForward -> false` (no sunny holidays): scores drop to `+10` only.
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- `daysForward -> daysForward == 5` (exactly one sunny holiday, and it is not
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`expectedWeather.get(1)`, so no `+2`): all flavours `10`.
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- Delete the `scorer.updateSelection()` call inside the production lambda:
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the approved file does not change — the forecast never reads `Scorer.flavour`.
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- Variation end state: skip the overload; make `collectExpectedWeather`
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package-private and test it directly with pure lambdas. Trade-off:
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tests the pure slice but not the composition (scoring loop).
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