Add split-loop-with-lambda learning hour: draft, pre-factored exercise, solution

- Samman learning hour: 5/5/10/25/5 outline; pairs stop at extracted method taking the lambda as a parameter - exercise/java: pre-factored variant of the kata (Q1/Q4 dead branch stripped, dates as constant), approval test red/flaky by design - exercise/solution: collectExpectedWeather(LongFunction<Boolean>) + no-arg overload; deterministic approval test (all-sunny lambda) - rehearse-with-agent.md: rehearsal setup with qwen + java-refactoring-mcp
2026-09-27 13:30:00 +00:00
parent 1a063860c9
commit a27e32688a
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+target/
+*.received.txt
+.idea/
+*.iml
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+# Reference solution — end state after step 3
+
+Derived from [IceCreamForecasting-Refactoring-Kata](https://github.com/emilybache/IceCreamForecasting-Refactoring-Kata)
+(same license as the kata applies to the copied `IceCream.java`).
+
+Run with `mvn test` — the approval test passes deterministically without
+touching the weather service.
+
+## What changed vs the exercise start
+
+1. Weather loop extracted to `collectExpectedWeather(LongFunction<Boolean>)`
+   — a static method whose only "unwanted dependency" is the lambda parameter.
+2. Public no-arg `longRangeForecast()` delegates to a new overload that takes
+   the weather function, keeping existing callers working.
+3. Test passes a pure lambda (`daysForward -> true`) — the original test
+   called the no-arg method and was flaky because of `Scorer`'s `Random`.
+
+## Variations to try (for the debrief or a second round)
+
+- `daysForward -> false` (no sunny holidays): scores drop to `+10` only.
+- `daysForward -> daysForward == 5` (exactly one sunny holiday, and it is not
+  `expectedWeather.get(1)`, so no `+2`): all flavours `10`.
+- Delete the `scorer.updateSelection()` call inside the production lambda:
+  the approved file does not change — the forecast never reads `Scorer.flavour`.
+- Variation end state: skip the overload; make `collectExpectedWeather`
+  package-private and test it directly with pure lambdas. Trade-off:
+  tests the pure slice but not the composition (scoring loop).
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+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>codingdojo</groupId>
+    <artifactId>icecream-split-loop-lambda-exercise</artifactId>
+    <version>1.0.0</version>
+
+    <properties>
+        <maven.compiler.source>17</maven.compiler.source>
+        <maven.compiler.target>17</maven.compiler.target>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+    </properties>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter</artifactId>
+            <version>5.10.2</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>com.approvaltests</groupId>
+            <artifactId>approvaltests</artifactId>
+            <version>18.6.0</version>
+            <scope>test</scope>
+        </dependency>
+    </dependencies>
+</project>
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+package codingdojo;
+
+public enum IceCream {
+    Strawberry,
+    Chocolate,
+    Vanilla;
+}
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+package codingdojo;
+
+import java.time.Instant;
+import java.time.temporal.ChronoUnit;
+import java.util.*;
+import java.util.function.LongFunction;
+
+/**
+ * Reference end state of the learning hour exercise (step 3 of the
+ * Split Loop with Lambda mechanics — stop here; step 4, moving the method
+ * to another class, is future work).
+ *
+ * Derived from https://github.com/emilybache/IceCreamForecasting-Refactoring-Kata
+ */
+public class LongRangeForecasts {
+
+    private static final Instant ANCHOR_DATE = Instant.parse("2023-04-26T14:00:00.000-07:00");
+    private static final List<Instant> DATES = List.of(
+            Instant.parse("2023-05-01T14:00:00.000-07:00"),
+            Instant.parse("2023-05-18T14:00:00.000-07:00"),
+            Instant.parse("2023-06-05T14:00:00.000-07:00"),
+            Instant.parse("2023-06-23T14:00:00.000-07:00")
+    );
+
+    private final Scorer scorer = new Scorer();
+
+    public Map<IceCream, Integer> longRangeForecast() {
+        return longRangeForecast(daysForward -> {
+            scorer.updateSelection();
+            return scorer.lookupWeather(daysForward);
+        });
+    }
+
+    /**
+     * The forecast, with the weather source injected as a function.
+     * Testable: pass any pure function instead of calling the weather service.
+     */
+    public Map<IceCream, Integer> longRangeForecast(
+            LongFunction<Boolean> dayOffsetToWeather) {
+        var expectedWeather = collectExpectedWeather(dayOffsetToWeather);
+
+        var result = new HashMap<IceCream, Integer>();
+        for (IceCream flavour : IceCream.values()) {
+            result.put(flavour, 0);
+        }
+        for (IceCream flavour : result.keySet()) {
+            result.put(flavour, result.get(flavour) + 10);
+            var sunnyHolidays = expectedWeather.stream().filter(s -> s).count();
+            if (sunnyHolidays > 2 && flavour == IceCream.Vanilla) {
+                result.put(flavour, result.get(flavour) + 5);
+            }
+            if (sunnyHolidays > 1 && expectedWeather.get(1)) {
+                result.put(flavour, result.get(flavour) + 2);
+            }
+        }
+        return result;
+    }
+
+    /**
+     * Extracted method. Note: it uses no fields — all its inputs are
+     * parameters. The only leftover dependency is the lambda, which can
+     * be replaced by any pure function in tests.
+     */
+    private static List<Boolean> collectExpectedWeather(
+            LongFunction<Boolean> dayOffsetToWeather) {
+        var expectedWeather = new ArrayList<Boolean>();
+        for (Instant interestingDate : DATES) {
+            long daysForward = ChronoUnit.DAYS.between(ANCHOR_DATE, interestingDate);
+            expectedWeather.add(dayOffsetToWeather.apply(daysForward));
+        }
+        return expectedWeather;
+    }
+}
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+package codingdojo;
+
+import java.util.Random;
+
+/**
+ * Trimmed version of the kata's Scorer: only the parts LongRangeForecasts uses.
+ * lookupWeather stands in for an API call to a weather service — in the real
+ * system this is the "unwanted dependency".
+ */
+public class Scorer {
+
+    public static IceCream flavour = null;
+
+    public int getScore() {
+        boolean sunnyToday = lookupWeather();
+        if (flavour == null) {
+            return -1;
+        }
+        switch (flavour) {
+            case Strawberry:
+                return sunnyToday ? 10 : 5;
+            case Chocolate:
+                return 6;
+            case Vanilla:
+                return sunnyToday ? 7 : 5;
+            default:
+                return -1;
+        }
+    }
+
+    public boolean lookupWeather() {
+        return new Random().nextBoolean();
+    }
+
+    public boolean lookupWeather(long daysForward) {
+        // placeholder implementation - real version would make API call to weather service
+        return new Random().nextBoolean();
+    }
+
+    public void updateSelection() {
+        // placeholder implementation - real version would use machine learning to predict sales
+        var score = getScore();
+        if (score > 5) {
+            var randomizer = new Random();
+            IceCream[] allFlavours = IceCream.values();
+            flavour = allFlavours[randomizer.nextInt(allFlavours.length)];
+        }
+    }
+}
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+package codingdojo;
+
+import org.approvaltests.Approvals;
+import org.junit.jupiter.api.Test;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Map;
+import java.util.function.LongFunction;
+
+public class LongRangeForecastsTest {
+
+    @Test
+    void longRangeForecast() {
+        // pure weather implementation: every holiday is sunny
+        LongFunction<Boolean> alwaysSunny = daysForward -> true;
+        var actual = new LongRangeForecasts().longRangeForecast(alwaysSunny);
+        Approvals.verify(printIceCreamForecast(actual));
+    }
+
+    private static String printIceCreamForecast(Map<IceCream, Integer> actual) {
+        var printedResult = new StringBuilder();
+        printedResult.append("{");
+        var sortedKeys = new ArrayList<>(actual.keySet());
+        Collections.sort(sortedKeys);
+        for (IceCream key : sortedKeys) {
+            printedResult.append(key);
+            printedResult.append(":");
+            printedResult.append(actual.get(key));
+            printedResult.append(", ");
+        }
+        printedResult.append("}");
+        return printedResult.toString();
+    }
+}
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+{Strawberry:12, Chocolate:12, Vanilla:17, }
				`@@ -0,0 +1 @@`
				`{Strawberry:12, Chocolate:12, Vanilla:17, }`