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
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# Exercise folder — Split Loop with Lambda (learning hour)
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Contents:
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- `java/` — pre-factored exercise repo (prefactored-longrange; runs with `mvn test`)
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- `solution/` — reference end state after step 3 (extracted method taking the lambda)
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- `rehearse-with-agent.md` — how Willem rehearses this with qwen-code + java-refactoring-mcp
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## Reference solution
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See `solution/`. End state of the production code (simplified):
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```java
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public Map<IceCream, Integer> longRangeForecast() {
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return longRangeForecast(daysForward -> {
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scorer.updateSelection();
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return scorer.lookupWeather(daysForward);
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});
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}
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public Map<IceCream, Integer> longRangeForecast(
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LongFunction<Boolean> dayOffsetToWeather) {
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var expectedWeather = collectExpectedWeather(dayOffsetToWeather);
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// ... reset loop + scoring loop, unchanged ...
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}
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private static List<Boolean> collectExpectedWeather(
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LongFunction<Boolean> dayOffsetToWeather) {
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var expectedWeather = new ArrayList<Boolean>();
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for (Instant interestingDate : DATES) {
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long daysForward = ChronoUnit.DAYS.between(ANCHOR_DATE, interestingDate);
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expectedWeather.add(dayOffsetToWeather.apply(daysForward));
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}
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return expectedWeather;
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}
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```
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Notes on the end state:
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- Key move: `scorer` is used **only inside the lambda**; the extracted method
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`collectExpectedWeather` has no dependency on `Scorer` at all — all its
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inputs are parameters.
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- The public no-arg overload keeps callers unchanged; it injects the
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"real" weather lambda. The test calls the overload with a pure
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`daysForward -> true` instead.
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- The test is pure and glanceable: `alwaysSunny` weather gives
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`{Strawberry:12, Chocolate:12, Vanilla:17, }` (approved file included).
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- `scorer.updateSelection()` turned out to be deletable (it only mutates
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static `Scorer.flavour` state that this method never reads). Leaving it
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in the lambda is fine for step 3; deleting it is a debrief aha.
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target/
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*.received.txt
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*.approved.txt
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.idea/
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*.iml
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# IceCreamForecasting — Split Loop with Lambda (exercise)
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Pre-factored variant of [IceCreamForecasting-Refactoring-Kata](https://github.com/emilybache/IceCreamForecasting-Refactoring-Kata):
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the Q1/Q4 dead branch is stripped and the hardcoded dates are a constant now.
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Everything else — including the noise in the scoring loop — is untouched.
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## Task
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`longRangeForecast()` is hard to test: it calls `scorer`, and `Scorer`
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stands for an external weather service you must not call from a unit test.
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The rest of the method's inputs are only `DATES` and `ANCHOR_DATE`.
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Get it under test:
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1. First get any test running that calls `longRangeForecast()` —
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it will fail or be flaky, because of the dependency on `Scorer`.
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2. Then use the **Split Loop with Lambda** mechanics (6 steps, on the handout)
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so that the weather collection becomes an extracted method that takes the
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weather-reading as a **lambda parameter**.
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3. Stop when you have an extracted method that takes the lambda as a parameter,
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and a test that passes **without** touching `Scorer`.
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## Constraint
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Don't change `Scorer` and don't change the *behaviour* of
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`longRangeForecast()`. Compile early, compile often.
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## Debrief questions
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- At which step did plain **Split Loop** stop working, and why?
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(Hint: what type did your introduced variable end up being?)
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- There are **two** calls to `scorer` in the weather loop.
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What did you do with each one?
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- Could you delete either call without changing the forecast?
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Try it. Why did that work?
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- Where would you take this next — more extraction, or moving the method
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to another class where `Scorer` is unknown?
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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>codingdojo</groupId>
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<artifactId>icecream-split-loop-lambda-exercise</artifactId>
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<version>1.0.0</version>
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<properties>
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<maven.compiler.source>17</maven.compiler.source>
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<maven.compiler.target>17</maven.compiler.target>
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<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
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</properties>
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<dependencies>
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<dependency>
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<groupId>org.junit.jupiter</groupId>
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<artifactId>junit-jupiter</artifactId>
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<version>5.10.2</version>
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<scope>test</scope>
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</dependency>
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<dependency>
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<groupId>com.approvaltests</groupId>
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<artifactId>approvaltests</artifactId>
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<version>18.6.0</version>
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<scope>test</scope>
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</dependency>
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</dependencies>
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</project>
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package codingdojo;
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public enum IceCream {
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Strawberry,
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Chocolate,
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Vanilla;
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}
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+59
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package codingdojo;
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import java.time.Instant;
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import java.time.temporal.ChronoUnit;
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import java.util.*;
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/**
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* Pre-factored variant of LongRangeForecasts from
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* https://github.com/emilybache/IceCreamForecasting-Refactoring-Kata
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*
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* Differences from the kata original (deliberate, for the learning hour):
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* - The Q1/Q4 branch was removed. It was dead code anyway: the unconditional
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* loop right after it overwrites every value it sets. (Splitting loops
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* sometimes reveals things like this.)
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* - The hardcoded list of interesting dates is now a constant, DATES, and the
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* anchor instant is ANCHOR_DATE. (Turning the dates into a parameter is a
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* *later* refactoring — not part of this exercise.)
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*
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* Your task: see README.md
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*/
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public class LongRangeForecasts {
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private static final Instant ANCHOR_DATE = Instant.parse("2023-04-26T14:00:00.000-07:00");
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private static final List<Instant> DATES = List.of(
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Instant.parse("2023-05-01T14:00:00.000-07:00"),
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Instant.parse("2023-05-18T14:00:00.000-07:00"),
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Instant.parse("2023-06-05T14:00:00.000-07:00"),
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Instant.parse("2023-06-23T14:00:00.000-07:00")
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);
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private final Scorer scorer = new Scorer();
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public Map<IceCream, Integer> longRangeForecast() {
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var result = new HashMap<IceCream, Integer>();
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for (IceCream flavour : IceCream.values()) {
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result.put(flavour, 0);
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}
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List<Boolean> expectedWeather = new ArrayList<>();
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for (Instant interestingDate : DATES) {
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scorer.updateSelection();
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long daysForward = ChronoUnit.DAYS.between(ANCHOR_DATE, interestingDate);
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Boolean weather = scorer.lookupWeather(daysForward);
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expectedWeather.add(weather);
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}
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for (IceCream flavour : result.keySet()) {
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result.put(flavour, result.get(flavour) + 10);
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var sunnyHolidays = expectedWeather.stream().filter(s -> s).count();
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if (sunnyHolidays > 2 && flavour == IceCream.Vanilla) {
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result.put(flavour, result.get(flavour) + 5);
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}
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if (sunnyHolidays > 1 && expectedWeather.get(1)) {
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result.put(flavour, result.get(flavour) + 2);
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}
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}
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return result;
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}
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}
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package codingdojo;
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import java.util.Random;
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/**
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* Trimmed version of the kata's Scorer: only the parts LongRangeForecasts uses.
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* lookupWeather stands in for an API call to a weather service — in the real
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* system this is the "unwanted dependency".
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*/
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public class Scorer {
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public static IceCream flavour = null;
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public int getScore() {
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boolean sunnyToday = lookupWeather();
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if (flavour == null) {
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return -1;
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}
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switch (flavour) {
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case Strawberry:
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return sunnyToday ? 10 : 5;
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case Chocolate:
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return 6;
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case Vanilla:
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return sunnyToday ? 7 : 5;
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default:
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return -1;
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}
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}
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public boolean lookupWeather() {
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return new Random().nextBoolean();
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}
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public boolean lookupWeather(long daysForward) {
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// placeholder implementation - real version would make API call to weather service
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return new Random().nextBoolean();
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}
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public void updateSelection() {
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// placeholder implementation - real version would use machine learning to predict sales
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var score = getScore();
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if (score > 5) {
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var randomizer = new Random();
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IceCream[] allFlavours = IceCream.values();
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flavour = allFlavours[randomizer.nextInt(allFlavours.length)];
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}
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}
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}
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package codingdojo;
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import org.approvaltests.Approvals;
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import org.junit.jupiter.api.Test;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.Map;
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public class LongRangeForecastsTest {
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@Test
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void longRangeForecast() {
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var actual = new LongRangeForecasts().longRangeForecast();
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Approvals.verify(printIceCreamForecast(actual));
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}
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private static String printIceCreamForecast(Map<IceCream, Integer> actual) {
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var printedResult = new StringBuilder();
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printedResult.append("{");
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var sortedKeys = new ArrayList<>(actual.keySet());
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Collections.sort(sortedKeys);
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for (IceCream key : sortedKeys) {
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printedResult.append(key);
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printedResult.append(":");
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printedResult.append(actual.get(key));
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printedResult.append(", ");
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}
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printedResult.append("}");
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return printedResult.toString();
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}
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}
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# Rehearsing the exercise with a coding agent + java-refactoring-mcp
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How Willem can rehearse the exercise (and test whether an agent can do it)
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using [java-refactoring-mcp](https://github.com/pfichtner/java-refactoring-mcp)'s
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CLI as the semantic refactoring backend. One-time setup, then a repeatable
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session script.
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## One-time setup
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```bash
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# 1. Build the tool (needs Java 21 + Maven 3.8+)
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git clone https://github.com/pfichtner/java-refactoring-mcp
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cd java-refactoring-mcp && mvn package -DskipTests
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# → refactor-mcp/target/refactor-mcp-*-fat.jar
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# 2. Install the CLI wrapper (see that repo's README, "CLI (optional)"):
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# gives you the `java-refactor` command.
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# 3. Sanctioned dirs for the MCP server (it refuses paths outside this list)
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# — check the repo docs for the exact env var / flag.
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# 4. Copy the exercise (NOT the solution folder) to a scratch workspace
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cp -r exercise/java ~/scratch/prefactored-longrange
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```
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## Session script
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Run the agent in `~/scratch/prefactored-longrange` with a prompt like:
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> You may only change code by invoking `java-refactor` CLI commands
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> (`java-refactor --help` lists them). Never edit `.java` files with text
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> edits, except lines marked by the CLI as needing manual work.
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> After each refactoring, run `mvn -q test` (it may fail — that is expected;
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> the dependency on `Scorer` makes the method untestable — but it must
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> still *compile*).
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>
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> Goal: in `LongRangeForecasts.longRangeForecast()`, the loop over `DATES`
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> collects weather by calling `scorer` (an unwanted external dependency).
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>
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> 1. Introduce a lambda for the scorer-dependent code and move its
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> definition to the top of the method, leaving the invocation where it is.
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> (No tool supports this — do it as a minimal manual edit.)
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> 2. Extract the weather-collection loop into a new method
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> `collectExpectedWeather` that takes the lambda as a parameter
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> (suggested type `LongFunction<Boolean>`).
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> 3. Stop. Report the final diff.
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Then compare the agent's end state against `exercise/solution/` (or diff
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against it directly) and run `mvn test` with the solution's test + approved
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file dropped in: it must pass deterministically.
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## What this rehearsal tells you
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- The mechanics-with-names prompt (introduce lambda / slide statement /
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extract method + introduce parameter) is unambiguous enough that an agent
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with weak tool-calling (qwen 35B) can follow it — if it can, a human
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pair can too.
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- Which steps the agent drifts on (usually step 1, where it tends to skip
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the lambda and just extract a method that closes over `scorer` — the
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exact mistake participants make). Collect these failure modes: they are
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gold for the concept slide and the debrief.
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- Time estimate for the exercise: run it twice, once tool-only, once with
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the manual lambda edit. The gap between the two is the "no tool support"
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friction participants will feel.
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## Tool coverage cheat sheet
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| Mechanic | Tool support |
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|---|---|
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| introduce lambda | none — manual edit |
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| slide statement | none — manual edit |
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| extract method | `java-refactor extract` |
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| add lambda parameter | `java-refactor introduce-param` / `change-method-signature` |
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| move method to another class (step 4, not in scope) | `java-refactor move-method` (call sites not rewritten) |
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@@ -0,0 +1,4 @@
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target/
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*.received.txt
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.idea/
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*.iml
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@@ -0,0 +1,27 @@
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# 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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@@ -0,0 +1,31 @@
|
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<?xml version="1.0" encoding="UTF-8"?>
|
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<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>
|
||||
|
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<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>
|
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</project>
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@@ -0,0 +1,7 @@
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package codingdojo;
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|
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public enum IceCream {
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Strawberry,
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Chocolate,
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Vanilla;
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||||
}
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+73
@@ -0,0 +1,73 @@
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package codingdojo;
|
||||
|
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import java.time.Instant;
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import java.time.temporal.ChronoUnit;
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||||
import java.util.*;
|
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import java.util.function.LongFunction;
|
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|
||||
/**
|
||||
* 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
|
||||
*/
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||||
public class LongRangeForecasts {
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|
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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;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,49 @@
|
||||
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)];
|
||||
}
|
||||
}
|
||||
}
|
||||
+35
@@ -0,0 +1,35 @@
|
||||
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();
|
||||
}
|
||||
}
|
||||
+1
@@ -0,0 +1 @@
|
||||
{Strawberry:12, Chocolate:12, Vanilla:17, }
|
||||
Reference in New Issue
Block a user