Oftentimes, it is useful to exercise the same test code multiple times, with varying inputs and expected results. Spock’s data driven testing support makes this a first class feature.

## Introduction

Suppose we want to specify the behavior of the Math.max method:

class MathSpec extends Specification {
def "maximum of two numbers"() {
expect:
// exercise math method for a few different inputs
Math.max(1, 3) == 3
Math.max(7, 4) == 7
Math.max(0, 0) == 0
}
}

Although this approach is fine in simple cases like this one, it has some potential drawbacks:

• Code and data are mixed and cannot easily be changed independently

• Data cannot easily be auto-generated or fetched from external sources

• In order to exercise the same code multiple times, it either has to be duplicated or extracted into a separate method

• In case of a failure, it may not be immediately clear which inputs caused the failure

• Exercising the same code multiple times does not benefit from the same isolation as executing separate methods does

Spock’s data-driven testing support tries to address these concerns. To get started, let’s refactor above code into a data-driven feature method. First, we introduce three method parameters (called data variables) that replace the hard-coded integer values:

class MathSpec extends Specification {
def "maximum of two numbers"(int a, int b, int c) {
expect:
Math.max(a, b) == c
...
}
}

We have finished the test logic, but still need to supply the data values to be used. This is done in a where: block, which always comes at the end of the method. In the simplest (and most common) case, the where: block holds a data table.

## Data Tables

Data tables are a convenient way to exercise a feature method with a fixed set of data values:

class MathSpec extends Specification {
def "maximum of two numbers"(int a, int b, int c) {
expect:
Math.max(a, b) == c

where:
a | b | c
1 | 3 | 3
7 | 4 | 7
0 | 0 | 0
}
}

The first line of the table, called the table header, declares the data variables. The subsequent lines, called table rows, hold the corresponding values. For each row, the feature method will get executed once; we call this an iteration of the method. If an iteration fails, the remaining iterations will nevertheless be executed. All failures will be reported.

Data tables must have at least two columns. A single-column table can be written as:

where:
a | _
1 | _
7 | _
0 | _

A sequence of two or more underscores can be used to split one wide data table into multiple narrower ones. Without this separator and without any other data variable assignment in between there is no way to have multiple data tables in one where block, the second table would just be further iterations of the first table, including the seemingly header row:

where:
a | _
1 | _
7 | _
0 | _
__

b | c
1 | 2
3 | 4
5 | 6

This is semantically exactly the same, just as one wider combined data table:

where:
a | b | c
1 | 1 | 2
7 | 3 | 4
0 | 5 | 6

The sequence of two or more underscores can be used anywhere in the where block. It will be ignored everywhere, except for in between two data tables, where it is used to separate the two data tables. This means that the separator can also be used as styling element in different ways. It can be used as separator line like shown in the last example or it can for example be used visually as top border of tables additionally to its effect of separating them:

where:
_____
a | _
1 | _
7 | _
0 | _
_____
b | c
1 | 2
3 | 4
5 | 6

## Isolated Execution of Iterations

Iterations are isolated from each other in the same way as separate feature methods. Each iteration gets its own instance of the specification class, and the setup and cleanup methods will be called before and after each iteration, respectively.

## Sharing of Objects between Iterations

In order to share an object between iterations, it has to be kept in a @Shared or static field.

 Note Only @Shared and static variables can be accessed from within a where: block.

Note that such objects will also be shared with other methods. There is currently no good way to share an object just between iterations of the same method. If you consider this a problem, consider putting each method into a separate spec, all of which can be kept in the same file. This achieves better isolation at the cost of some boilerplate code.

## Syntactic Variations

The previous code can be tweaked in a few ways.

First, since the where: block already declares all data variables, the method parameters can be omitted.[1]

You can also omit some parameters and specify others, for example to have them typed. The order also is not important, data variables are matched by name to the specified method parameters.

Second, inputs and expected outputs can be separated with a double pipe symbol (||) to visually set them apart.

With this, the code becomes:

class MathSpec extends Specification {
def "maximum of two numbers"() {
expect:
Math.max(a, b) == c

where:
a | b || c
1 | 3 || 3
7 | 4 || 7
0 | 0 || 0
}
}

Alternatively to using single or double pipes you can also use any amount of semicolons to separate data columns from each other:

class MathSpec extends Specification {
def "maximum of two numbers"() {
expect:
Math.max(a, b) == c

where:
a ; b ;; c
1 ; 3 ;; 3
7 ; 4 ;; 7
0 ; 0 ;; 0
}
}

Pipes and semicolons as data column separator can not be mixed within one table. If the column separator changes, this starts a new stand-alone data table:

class MathSpec extends Specification {
def "maximum of two numbers"() {
expect:
Math.max(a, b) == c
Math.max(d, e) == f

where:
a | b || c
1 | 3 || 3
7 | 4 || 7
0 | 0 || 0

d ; e ;; f
1 ; 3 ;; 3
7 ; 4 ;; 7
0 ; 0 ;; 0
}
}

## Reporting of Failures

Let’s assume that our implementation of the max method has a flaw, and one of the iterations fails:

maximum of two numbers [a: 1, b: 3, c: 3, #0]   PASSED
maximum of two numbers [a: 7, b: 4, c: 7, #1]   FAILED

Condition not satisfied:

Math.max(a, b) == c
|    |   |  |  |  |
|    |   7  4  |  7
|    42        false
class java.lang.Math

maximum of two numbers [a: 0, b: 0, c: 0, #2]   PASSED

The obvious question is: Which iteration failed, and what are its data values? In our example, it isn’t hard to figure out that it’s the second iteration (with index 1) that failed even from the rich condition rendering. At other times this can be more difficult or even impossible.[2] In any case, Spock makes it loud and clear which iteration failed, rather than just reporting the failure. Iterations of a feature method are by default unrolled with a rich naming pattern. This pattern can also be configured as documented at Unrolled Iteration Names or the unrolling can be disabled like described in the following section.

## Method Uprolling and Unrolling

A method annotated with @Rollup will have its iterations not reported independently but only aggregated within the feature. This can for example be used if you produce many test cases from calculations or if you use external data like the contents of a database as test data and do not want the test count to vary:

@Rollup
def "maximum of two numbers"() {
...

Note that up- and unrolling has no effect on how the method gets executed; it is only an alternation in reporting. Depending on the execution environment, the output will look something like:

maximum of two numbers   FAILED

Condition not satisfied:

Math.max(a, b) == c
|    |   |  |  |  |
|    |   7  4  |  7
|    42        false
class java.lang.Math

The @Rollup annotation can also be placed on a spec. This has the same effect as placing it on each data-driven feature method of the spec that does not have an @Unroll annotation.

Alternatively the configuration file setting unrollByDefault in the unroll section can be set to false to roll up all features automatically unless they are annotated with @Unroll or are contained in an @Unrolled spec and thus reinstate the pre Spock 2.0 behavior where this was the default.

Disable Default Unrolling
unroll {
unrollByDefault false
}

It is illegal to annotate a spec or a feature with both the @Unroll and the @Rollup annotation and if detected this will cause an exception to be thrown.

To summarize:

A feature will be uprolled

• if the method is annotated with @Rollup

• if the method is not annotated with @Unroll and the spec is annotated with @Rollup

• if neither the method nor the spec is annotated with @Unroll and the configuration option unroll { unrollByDefault } is set to false

A feature will be unrolled

• if the method is annotated with @Unroll

• if the method is not annotated with @Rollup and the spec is annotated with @Unroll

• if neither the method nor the spec is annotated with @Rollup and the configuration option unroll { unrollByDefault } is set to its default value true

## Data Pipes

Data tables aren’t the only way to supply values to data variables. In fact, a data table is just syntactic sugar for one or more data pipes:

...
where:
a << [1, 7, 0]
b << [3, 4, 0]
c << [3, 7, 0]

A data pipe, indicated by the left-shift (<<) operator, connects a data variable to a data provider. The data provider holds all values for the variable, one per iteration. Any object that Groovy knows how to iterate over can be used as a data provider. This includes objects of type Collection, String, Iterable, and objects implementing the Iterable contract. Data providers don’t necessarily have to be the data (as in the case of a Collection); they can fetch data from external sources like text files, databases and spreadsheets, or generate data randomly. Data providers are queried for their next value only when needed (before the next iteration).

## Multi-Variable Data Pipes

If a data provider returns multiple values per iteration (as an object that Groovy knows how to iterate over), it can be connected to multiple data variables simultaneously. The syntax is somewhat similar to Groovy multi-assignment but uses brackets instead of parentheses on the left-hand side:

@Shared sql = Sql.newInstance("jdbc:h2:mem:", "org.h2.Driver")

def "maximum of two numbers"() {
expect:
Math.max(a, b) == c

where:
[a, b, c] << sql.rows("select a, b, c from maxdata")
}

Data values that aren’t of interest can be ignored with an underscore (_):

...
where:
[a, b, _, c] << sql.rows("select * from maxdata")

The multi-assignments can even be nested. The following example will generate these iterations:

a b c

['a1', 'a2']

'b1'

'c1'

['a2', 'a1']

'b1'

'c1'

['a1', 'a2']

'b2'

'c2'

['a2', 'a1']

'b2'

'c2'

...
where:
[a, [b, _, c]] << [
['a1', 'a2'].permutations(),
[
['b1', 'd1', 'c1'],
['b2', 'd2', 'c2']
]
].combinations()

## Data Variable Assignment

A data variable can be directly assigned a value:

...
where:
a = 3
b = Math.random() * 100
c = a > b ? a : b

Assignments are re-evaluated for every iteration. As already shown above, the right-hand side of an assignment may refer to other data variables:

...
where:
row << sql.rows("select * from maxdata")
// pick apart columns
a = row.a
b = row.b
c = row.c

## Accessing Other Data Variables

There are only two possibilities to access one data variable from the calculation of another data variable.

The first possibility are derived data variables like shown in the last section. Every data variable that is defined by a direct assignment can access all previously defined data variables, including the ones defined through data tables or data pipes:

...
where:
a = 3
b = Math.random() * 100
c = a > b ? a : b

The second possibility is to access previous columns within data tables:

...
where:
a | b
3 | a + 1
7 | a + 2
0 | a + 3

This also includes columns in previous data tables in the same where block:

...
where:
a | b
3 | a + 1
7 | a + 2
0 | a + 3

and:
c = 1

and:
d     | e
a * 2 | b * 2
a * 3 | b * 3
a * 4 | b * 4

## Multi-Variable Assignment

Like with data pipes, you can also assign to multiple variables in one expression, if you have some object Groovy can iterate over. Unlike with data pipes, the syntax here is identical to standard Groovy multi-assignment syntax:

@Shared sql = Sql.newInstance("jdbc:h2:mem:", "org.h2.Driver")

def "maximum of two numbers multi-assignment"() {
expect:
Math.max(a, b) == c

where:
row << sql.rows("select a, b, c from maxdata")
(a, b, c) = row
}

Data values that aren’t of interest can be ignored with an underscore (_):

...
where:
row << sql.rows("select * from maxdata")
(a, b, _, c) = row

## Combining Data Tables, Data Pipes, and Variable Assignments

Data tables, data pipes, and variable assignments can be combined as needed:

...
where:
a | b
1 | a + 1
7 | a + 2
0 | a + 3

c << [3, 4, 0]

d = a > c ? a : c

## Type Coercion for Data Variable Values

Data variable values are coerced to the declared parameter type using type coercion. Due to that custom type conversions can be provided as extension module or with the help of the @Use extension on the specification (as it has no effect to the where: block if applied to a feature).

def "type coercion for data variable values"(Integer i) {
expect:
i instanceof Integer
i == 10

where:
i = "10"
}
@Use(CoerceBazToBar)
class Foo extends Specification {
def foo(Bar bar) {
expect:
bar == Bar.FOO

where:
bar = Baz.FOO
}
}
enum Bar { FOO, BAR }
enum Baz { FOO, BAR }
class CoerceBazToBar {
static Bar asType(Baz self, Class<Bar> clazz) {
return Bar.valueOf(self.name())
}
}

## Number of Iterations

The number of iterations depends on how much data is available. Successive executions of the same method can yield different numbers of iterations. If a data provider runs out of values sooner than its peers, an exception will occur. Variable assignments don’t affect the number of iterations. A where: block that only contains assignments yields exactly one iteration.

## Closing of Data Providers

After all iterations have completed, the zero-argument close method is called on all data providers that have such a method.

## Unrolled Iteration Names

By default, the names of unrolled iterations are the name of the feature, plus the data variables and the iteration index. This will always produce unique names and should enable you to identify easily the failing data variable combination.

The example at Reporting of Failures for example shows with maximum of two numbers [a: 7, b: 4, c: 7, #1], that the second iteration (#1) where the data variables have the values 7, 4 and 7 failed.

With a bit of effort, we can do even better:

def "maximum of #a and #b is #c"() {
...

This method name uses placeholders, denoted by a leading hash sign (#), to refer to data variables a, b, and c. In the output, the placeholders will be replaced with concrete values:

maximum of 1 and 3 is 3   PASSED
maximum of 7 and 4 is 7   FAILED

Math.max(a, b) == c
|    |   |  |  |  |
|    |   7  4  |  7
|    42        false
class java.lang.Math

maximum of 0 and 0 is 0   PASSED

Now we can tell at a glance that the max method failed for inputs 7 and 4.

An unrolled method name is similar to a Groovy GString, except for the following differences:

• Expressions are denoted with # instead of \$, and there is no equivalent for the \${…​} syntax.

• Expressions only support property access and zero-arg method calls.

Given a class Person with properties name and age, and a data variable person of type Person, the following are valid method names:

def "#person is #person.age years old"() { // property access
def "#person.name.toUpperCase()"() { // zero-arg method call

Non-string values (like #person above) are converted to Strings according to Groovy semantics.

The following are invalid method names:

def "#person.name.split(' ')[1]" {  // cannot have method arguments
def "#person.age / 2" {  // cannot use operators

If necessary, additional data variables can be introduced to hold more complex expressions:

def "#lastName"() {
...
where:
person << [new Person(age: 14, name: 'Phil Cole')]
lastName = person.name.split(' ')[1]
}

Additionally, to the data variables the tokens #featureName and #iterationIndex are supported. The former does not make much sense inside an actual feature name, but there are two other places where an unroll-pattern can be defined, where it is more useful.

def "#person is #person.age years old [#iterationIndex]"() {

will be reported as

╷
└─ Spock ✔
└─ PersonSpec ✔
└─ #person.name is #person.age years old [#iterationIndex] ✔
├─ Fred is 38 years old [0] ✔
├─ Wilma is 36 years old [1] ✔
└─ Pebbles is 5 years old [2] ✔

Alternatively, to specifying the unroll-pattern as method name, it can be given as parameter to the @Unroll annotation which takes precedence over the method name:

@Unroll("#featureName[#iterationIndex] (#person.name is #person.age years old)")
def "person age should be calculated properly"() {
// ...

will be reported as

╷
└─ Spock ✔
└─ PersonSpec ✔
└─ person age should be calculated properly ✔
├─ person age should be calculated properly[0] (Fred is 38 years old) ✔
├─ person age should be calculated properly[1] (Wilma is 36 years old) ✔
└─ person age should be calculated properly[2] (Pebbles is 5 years old) ✔

The advantage is, that you can have a descriptive method name for the whole feature, while having a separate template for each iteration. Furthermore, the feature method name is not filled with placeholders and thus better readable.

If neither a parameter to the annotation is given, nor the method name contains a #, the configuration file setting defaultPattern in the unroll section is inspected. If it is set to a non-null string, this value is used as unroll-pattern. This could for example be set to

• #featureName to have all iterations reported with the same name, or

• #featureName[#iterationIndex] to have a simply indexed iteration name, or

• #iterationName if you make sure that in each data-driven feature you also set a data variable called iterationName that is then used for reporting

### Special Tokens

This is the complete list of special tokens:

• #featureName is the name of the feature (mostly useful for the defaultPattern setting)

• #iterationIndex is the current iteration index

• #dataVariables lists all data variables for this iteration, e.g. x: 1, y: 2, z: 3

• #dataVariablesWithIndex the same as #dataVariables but with an index at the end, e.g. x: 1, y: 2, z: 3, #0

### Configuration

Set Default Unroll-Pattern
unroll {
defaultPattern '#featureName[#iterationIndex]'
}

If none of the three described ways is used to set a custom unroll-pattern, by default the feature name is used, suffixed with all data variable names and their values and finally the iteration index, so the result will be for example my feature [x: 1, y: 2, z: 3, #0].

If there is an error in an unroll expression, for example typo in variable name, exception during evaluation of a property or method in the expression and so on, the test will fail. This is not true for the automatic fall back rendering of the data variables if there is no unroll-pattern set in any way, this will never fail the test, no matter what happens.

The failing of test with errors in the unroll expression can be disabled by setting the configuration file setting validateExpressions in the unroll section to false. If this is done and an error happens, the erroneous expression #foo.bar will be substituted by #Error:foo.bar.

Disable Unroll-pattern Expression Asserting
unroll {
validateExpressions false
}

Some reporting frameworks, or IDEs support proper tree based reporting. For these cases it might be desirable to omit the feature name from the iteration reporting.

Disable repetition of feature name in iterations
unroll {
includeFeatureNameForIterations false
}

With includeFeatureNameForIterations true

╷
└─ Spock ✔
└─ ASpec ✔
└─ really long and informative test name that doesn't have to be repeated ✔
├─ really long and informative test name that doesn't have to be repeated [x: 1, y: a, #0] ✔
├─ really long and informative test name that doesn't have to be repeated [x: 2, y: b, #1] ✔
└─ really long and informative test name that doesn't have to be repeated [x: 3, y: c, #2] ✔
With includeFeatureNameForIterations false
╷
└─ Spock ✔
└─ ASpec ✔
└─ really long and informative test name that doesn't have to be repeated ✔
├─ x: 1, y: a, #0 ✔
├─ x: 2, y: b, #1 ✔
└─ x: 3, y: c, #2 ✔
 Note The same can be achieved for individual features by using @Unroll('#dataVariablesWithIndex').

1. The idea behind allowing method parameters is to enable better IDE support. However, recent versions of IntelliJ IDEA recognize data variables automatically, and even infer their types from the values contained in the data table.
2. For example, a feature method could use data variables in its given: block, but not in any conditions.