Provides parameterized testing patterns with JUnit 5, generates data-driven unit tests using @ParameterizedTest, @ValueSource, @CsvSource, @MethodSource. Creates tests that run the same logic with multiple input values. Use when writing data-driven Java tests, multiple test cases from single method, or boundary value analysis.
npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill unit-test-parameterized
Provides patterns for parameterized unit tests in Java using JUnit 5. Covers @ValueSource, @CsvSource, @MethodSource, @EnumSource, @ArgumentsSource, and custom display names. Reduces test duplication by running the same test logic with multiple input values.
junit-jupiter-params is on test classpath (included in junit-jupiter)@ValueSource for simple values, @CsvSource for tabular data, @MethodSource for complex objectsname = "{0}..." for readable output./gradlew test --info or mvn test and verify all parameter combinations executeJUnit 5 parameterized tests require junit-jupiter (includes params). Add assertj-core for assertions:
<!-- Maven -->
<dependency>
<groupId>org.junit.jupiter</groupId>
<artifactId>junit-jupiter</artifactId>
<scope>test</scope>
</dependency>
// Gradle
testImplementation("org.junit.jupiter:junit-jupiter")
@ValueSource — Simple Valuesimport org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.ValueSource;
import static org.assertj.core.api.Assertions.*;
@ParameterizedTest
@ValueSource(strings = {"hello", "world", "test"})
void shouldCapitalizeAllStrings(String input) {
assertThat(StringUtils.capitalize(input)).isNotEmpty();
}
@ParameterizedTest
@ValueSource(ints = {1, 2, 3, 4, 5})
void shouldBePositive(int number) {
assertThat(number).isPositive();
}
@ParameterizedTest
@ValueSource(ints = {Integer.MIN_VALUE, -1, 0, 1, Integer.MAX_VALUE})
void shouldHandleBoundaryValues(int value) {
assertThat(Math.incrementExact(value)).isGreaterThan(value);
}
@CsvSource — Tabular Dataimport org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.CsvSource;
@ParameterizedTest
@CsvSource({
"[email protected], true",
"[email protected], true",
"invalid-email, false",
"user@, false",
"@example.com, false"
})
void shouldValidateEmailAddresses(String email, boolean expected) {
assertThat(UserValidator.isValidEmail(email)).isEqualTo(expected);
}
@MethodSource — Complex Dataimport org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.MethodSource;
import java.util.stream.Stream;
@ParameterizedTest
@MethodSource("additionTestCases")
void shouldAddNumbersCorrectly(int a, int b, int expected) {
assertThat(Calculator.add(a, b)).isEqualTo(expected);
}
static Stream<Arguments> additionTestCases() {
return Stream.of(
Arguments.of(1, 2, 3),
Arguments.of(0, 0, 0),
Arguments.of(-1, 1, 0),
Arguments.of(100, 200, 300)
);
}
@EnumSource — Enum Values@ParameterizedTest
@EnumSource(Status.class)
void shouldHandleAllStatuses(Status status) {
assertThat(status).isNotNull();
}
@ParameterizedTest
@EnumSource(value = Status.class, names = {"ACTIVE", "INACTIVE"})
void shouldHandleSpecificStatuses(Status status) {
assertThat(status).isIn(Status.ACTIVE, Status.INACTIVE);
}
@ParameterizedTest(name = "Discount of {0}% should be calculated correctly")
@ValueSource(ints = {5, 10, 15, 20})
void shouldApplyDiscount(int discountPercent) {
double result = DiscountCalculator.apply(100.0, discountPercent);
assertThat(result).isEqualTo(100.0 * (1 - discountPercent / 100.0));
}
ArgumentsProviderclass RangeValidatorProvider implements ArgumentsProvider {
@Override
public Stream<? extends Arguments> provideArguments(ExtensionContext context) {
return Stream.of(
Arguments.of(0, 0, 100, true),
Arguments.of(50, 0, 100, true),
Arguments.of(-1, 0, 100, false),
Arguments.of(101, 0, 100, false)
);
}
}
@ParameterizedTest
@ArgumentsSource(RangeValidatorProvider.class)
void shouldValidateRange(int value, int min, int max, boolean expected) {
assertThat(RangeValidator.isInRange(value, min, max)).isEqualTo(expected);
}
@ParameterizedTest
@ValueSource(strings = {"", " ", null})
void shouldThrowExceptionForInvalidInput(String input) {
assertThatThrownBy(() -> Parser.parse(input))
.isInstanceOf(IllegalArgumentException.class);
}
name = "{0}..." for readable output@MethodSource for complex objects, @CsvSource for tabular data@ValueSource limitation: Only supports primitives, strings, and enums — not objects or null directly@CsvSource@MethodSource visibility: Factory methods must be static in the same test class{0}, {1}, etc. to reference parametersToolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take giuseppe-trisciuoglio/unit-test-parameterized from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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