Spock is an awesome test framework for testing our Java or Groovy code. Spock itself is written with Groovy and provides a nice syntax to define our tests, or specifications in Spock terminology. To configure support for using Spock in our Gradle build is very easy with the JVM Test Suite plugin (included with the Java plugin). The plugin gives us a nice syntax to define different types of tests, for example integration tests, with their own source set, dependencies and configuration. To use Spock as testing framework we only have to use the method useSpock within a test configuration. The default version of Spock that is used is 2.1-groovy-3.0 when we use Gradle 7.6. If we want to use another version we can use a String parameter when we use the useSpock method with the version we want to use.

Since Gradle 7.3 we can use the JVM Test Suite plugin to define in a declarative way tests for our build. For example adding integration tests with a new source set and dependencies becomes easier with this plugin. The plugin is automatically part of the Java plugin so we don’t have to define it explicitly in our build. Configuring the default test task can also be done using the syntax of the JVM TestSuite plugin. We can use several methods from the JvmTestSuite class in our configuration. For example if we want to use Spock as testing framework we can simply add the method useSpock in our build script. Or if we want to use the JUnit 5 Jupiter engine we can use useJUnitJupiter. These methods will add dependencies in the testImplementation configuration. There is a default version for the dependencies if we use the method without arguments. But we can also define the version as String argument for these methods. The version catalog for our project is the place to store version for our dependencies, so it would be nice if we could use the version defined in our version catalog as argument for the use<TestFramework> methods. We can reference the version very simple by using libs.versions.<version-key>. This will return the value we defined as version in our version catalog.

The dw::core::Strings module has useful functions for working with string values. One of the functions is dasherize. The function takes a string argument and replaces spaces, underscores and camel-casing into dashes. The resulting string value with hyphens is also called kebab-casing. The dasherize function also turns any uppercase character to lowercase.

When we are working with a multi-module project in Maven we might want to exclude a module when we invoke a build command. We might only be interested in partially building some modules. We can use the command line option -pl or --projects to specify a list of modules that need to be in our build. But we can also use ! followed by the module name to exclude modules from our build.

The version catalog in Gradle is very useful to define a list of dependencies in one single place. In our build script we references dependencies from the version catalog using type safe accessors when we define a dependency for a configuration. Sometimes multiple dependencies belong to each other and are used in combination with each other. In the version catalog we can define bundles of such dependency groups. Instead of referencing each dependency individually we can reference a bundle from the version catalog in our build script. This keeps our build script cleaner and updating a bundle only needs a change in the version catalog.

A version catalog in Gradle is a central place in our project where we can define dependency references with their version or version rules. A dependency reference is defined using an identifier with a corresponding dependency definition containing the coordinates of the dependency. Now we can reference the dependency using the identifier in for example a dependency configuration, e.g. implementation(libs.spring.core). If there is a version change we want to apply we only have to make the change in our version catalog. An added bonus is that Gradle generates type safe accessors for the identifier we use in our version catalog, so we can get code completion in our IntelliJ IDEA when we want to reference a dependency from the version catalog.

Besides dependencies we need to build and test our software we can also include definitions for Gradle plugins including their version.

In a previous post we learned how to read text file contents with the slurp function. To write text file content we use the spit function. We content is defined in the second argument of the function. The first argument allows several types that will turn into a Writer object used for writing the content to. For example a string argument is used as URI and if that is not valid as a file name of the file to read. A File instance can be used directly as argument as well. But also Writer, BufferedWriter, OutputStream, URI, URL and Socket. As an option we can specify the encoding used to write the file content using the :encoding keyword. The default encoding is UTF-8 if we don’t specify the encoding option. With the option :append we can define if content needs to be appended to an existing file or the content should overwrite existing content in the file.

The slurp funtion in Clojure can be used to read the contents of a file and return it as a string value. We can use several types as argument for the function. For example a string argument is used as URI and if that is not valid as a file name of the file to read. A File instance can be used directly as argument as well. But also Reader, BufferedReader, InputStream, URI, URL, Socket, byte[] and char[]. As an option we can specify the encoding used to read the file content using the :encoding keyword. The default encoding is UTF-8 if we don’t specify the encoding option.

DataWeave has some very nice features to transform data objects. One of those nice features is the update operator. With the update operator we can change values of keys in an object using a very concise syntax. We don’t have to go through all keys and create a new object, but we can pinpoint the exact key and change the value. To get the correct key we use selectors. Once we have the key we can set a new value. We can define a variable to contain the current value if we want to use it to define a new value. Also is it possible to add a condition that needs to be true to change the value. Finally the update operator supports upserting a value if the key might not exist yet.

In a previous post we learned about the macros SV, SVI and SVD that return a string representation of variables with their name and value. Groovy 4 also added the NP and NPL macros that we can use to inspect variables. Instead of returning a GString instance these macros return a NamedValue instance or a list of NamedValue value instances. The NamedValue class is a simple class with a property name, containing the name of the variable, and property val with the value of the variable. The macro NP can be used when we have a single variable and result is a single NamedValue instance. An the macro NVL can be used with multiple variables. The result is a list of NamedValue instances where each instance represent a variable passed as argument to NVL.

Groovy 4 added some built-in macros that we can use in our code. A macro is code that will create new code. It does this by manipulating the Abstract Syntax Tree (AST) before the code is compiled. So when we use a macro, the macro will change the AST and those changes will be compiled. The three built-in macros SV, SVI and SVD can create a GString instance with the names and values of the variables that are passed as argument. This can be very useful to create some meaningful debugging statements. Normally we would have to type in the variable name ourselves followed by the value. Now with these macros we don’t have to type the variable as the macro will add that to the AST for us.

DataWeave has a nice language feature called literal types. Literal types are types with a single predefined values and can be defined using a String, Number or Boolean value. So the value of a literal type is a fixed value. We can combine multiple literal types into a new type using a union type to define an enumaration in DataWeave. The enumaration can only be one of the literal types used to define it.
Together with overloaded functions literal types are very useful. We can define a function where one of the input arguments is a literal type to define specific behaviour based on the literal type. Then we can overload the function for other literal types with different behaviour. DataWeave will make sure the correct function is called based on the value of the input argument and how it matches to the literal type value.