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Working With Legacy Code: Extending Shiny Modules with the Sprout Technique and Test Driven Development

Jakub Sobolewski
[This article was first published on Tag: r - Appsilon | Enterprise R Shiny Dashboards, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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Making changes to a legacy system can be daunting. How can we develop code when we don’t have unit tests for a part of a system we need to change? How do we ensure a new feature is unit-tested when the rest of the codebase lacks them? One approach would be to just scrape the module off (or even the whole project!) and start from scratch, but it’s often not worth the cost. So what is the alternative?

Use the Sprout Technique with Test Driven Development.

Table of Contents:

The Task

Let’s consider an app that displays names of tables from a data lake that are used to build visuals. It informs users from which version of data the plots are derived. This feature needs to be extended to show tables changes history to allow users to see previous versions of data, by whom and when they were changed. Let’s formulate this requirement in a form of acceptance criteria:

Note that we don’t know yet how these criteria should be satisfied. Currently, we know only what needs to be delivered to meet the business goal.

Sprout Technique

  1. Identify insertion point.
  2. Develop code in isolation.
  3. Call your code from the Legacy Code.

Identify Insertion Point

There is a module that already displays the names of tables used. We need to identify the boundaries of the feature we’re extending. In this case, it’s just a pair of uiOutput and renderUI in one legacy (untested) module that displays currently used tables names. This is the place where we will inject our new code.

mod_data_ui <- function(id) {
  ns <- shiny::NS(id)
  shiny::div(
    shiny::h1("Data"),
    # ...,
    shiny::uiOutput(ns("data_summary"))
  )
}

mod_data_server <- function(input, output, session) {
  # ...
  output$data_summary <- renderUI({
    # Some business logic
  })
}

Learning about the code where you are injecting a new feature will help you understand what data is available there and how the new code can interact with its surroundings – it will help you drive the decision on what the new module’s interface can be.

Develop Code in Isolation

Let’s recall our criteria:

From exploration of the existing code we see that an object called StudyData is available in the parent module that has a get_data_history method allowing you to fetch the data you need.

StudyData <- R6::R6Class(
  classname = "StudyData",
  public = list(
    # Other methods
    # ...
    get_data_history = function() {
      # ...
    }
  )
)

Knowing that we can create an implementation list for the feature:

Let’s set up our first test, we will use Arrange, Act, Assert pattern to guide our thinking:

describe("mod_data_history", {
  it("should fetch data history", {
    # Arrange
    
    # Act
    
    # Assert
  })
})

Let’s focus on the first item in our implementation list. Given that we need to use StudyData object there are two possibilities:

Let’s stick to the first option to keep gates open for this module to fetch some more data to display. Let’s call our new module mod_data_history . We expect the module should call the study_data$get_data_history() method.

We need to mock a StudyData object to simulate its behavior, it allows us not to rely on actual implementation (which requires connection to the data lake). Since StudyData is a R6 class, we could mock an object with structure by hand, or we can create a reusable, simple routine that clones a R6 object:

create_mock_r6_class <- function(generator) {
  checkmate::assert_class(generator, "R6ClassGenerator")
  structure(
    purrr::imap(generator$public_methods, mockery::mock),
    class = generator$classname
  )
}

Then our mock object generator will look like:

.create_mock_study_data <- function() {
  create_mock_r6_class(StudyData)
}

Note that we’re creating a wrapper for create_mock_r6_class(StudyData) – it may seem redundant now, but it’ll allow you to quickly substitute/extend this mock in all tests that use it.

Then our test becomes:

.create_mock_study_data <- function() {
  create_mock_r6_class(StudyData)
}

describe("mod_data_history", {
  it("should fetch data history", {
    # Arrange
    study_data <- .create_mock_study_data()

    # Act

    # Assert
  })
})

For now, we only expect that this module will call this get_data_history method. We can use mockery::expect_called to check if this method has been called once. Let’s put that into the Assert block:

describe("mod_data_history", {
  it("should fetch data history", {
    # Arrange
    study_data <- .create_mock_study_data()

    shiny::testServer(
      app = mod_data_history_server,
      args = list(study_data = study_data), {
        # Act

        # Assert
        mockery::expect_called(
          study_data$get_data_history,
          n = 1
        )
      }
    )
  })
})

Running tests will throw errors that mod_data_history_server object cannot be found. Let’s create the module:

mod_data_history_ui <- function(id) {
  ns <- shiny::NS(id)

}

mod_data_history_server <- function(id, study_data) {
  shiny::moduleServer(id, function(input, output, session) {

  })
}

Now the test fails with an expected message , mock object has not been called yet:

Failure (test-mod_data_history.R:12): mod_data_history: should fetch data history mock object has not been called 1 time

To make the test pass we add a call to this method in the module:

mod_data_history_ui <- function(id) {
  ns <- shiny::NS(id)
}

mod_data_history_server <- function(id, study_data) {
  shiny::moduleServer(id, function(input, output, session) {
    data_history <- study_data$get_data_history()

  })
}



✔ | F W S  OK | Context
✔ |         1 | mod_data_history                                                                 

══ Results ══════════════════════════════════════════════════════════════════════════════════════
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 1 ]

🎯 Your tests hit the mark 🎯

So far so good, we have met the first implementation requirement.

Now we need to display the data that we just fetched. That means we need another test case for the second item in our list:

it("should display data history", {
  # Arrange
  study_data <- .create_mock_study_data()

  shiny::testServer(
    app = mod_data_history_server,
    args = list(study_data = study_data), {
      # Act

      # Assert

    }
  )
})

We don’t know yet what type of HTML should be produced to display this data, but from the server side we may expect that a method that parses history data and builds a HTML will be called. We will force output$data_history to evaluate and check if the display method has been called. Note that we only make one assumption – to which output slot we send the HTML – we don’t lock ourselves to a specific rendering engine with this test (e.g., renderTable, renderUI). We’re not checking explicitly what value output$data_history yields.

.study_data_history <- function() {
  tibble::tibble(
    data_name = c("data_1_v1", "data_1_v2", "data_1_v3"),
    user = c("user_1", "user_2", "user_1"),
    updated = c("2023-06-29 17:49:12", "2023-05-29 17:49:12", "2023-04-29 17:49:12"),
    size = c(1000, 1000, 1000)
  )
}

.create_mock_study_data <- function() {
  mock <- create_mock_r6_class(StudyData)
  mock$get_data_history <- mockery::mock(.study_data_history())
  mock
}

# ...

it("should display data history", {
  # Arrange
  study_data <- .create_mock_study_data()
  mock_render_method <- mockery::mock()

  shiny::testServer(
    app = mod_data_history_server,
    args = list(study_data = study_data), {
      # Act
      output$data_history

      # Assert
      mockery::expect_args(
        mock_render_method,
        n = 1,
        data = .study_data_history()
      )
    }
  )
})

You can see how we extended the mock in .create_mock_study_data to mock a return value from its method. This test fails as expected – we don’t have an output yet:

✔ | F W S  OK | Context
✖ | 1       1 | mod_data_history
─────────────────────────────────────────────────────────────────────────────────────────────────
Error (test-mod_data_history.R:40): mod_data_history: should display data history
Error in `.subset2(x, "impl")$getOutput(name)`: The test referenced an output that hasn't been defined yet: output$proxy1-data_history

Let’s add outputs, we will use {reactable} as it implements all features we need to display the data in a shape we need.

mod_data_history_ui <- function(id) {
  ns <- shiny::NS(id)
  reactable::reactableOutput(ns("data_history"))
}

mod_data_history_server <- function(id, study_data) {
  shiny::moduleServer(id, function(input, output, session) {
    data_history <- study_data$get_data_history()

    output$data_history <- reactable::renderReactable({
      render_data_history(data_history)
    })
  })
}

Tests still fail , this time with an error:

Error (test-mod_data_history.R:40): mod_data_history: should display data history
Error in `render(data_history)`: could not find function "render_data_history"

We need to either stub render_data_history it or inject it to the module. Let’s use stubbing for now, as we don’t need to parametrize this module with a rendering function:

it("should display data history", {
  # Arrange
  study_data <- .create_mock_study_data()
  mock_render_method <- mockery::mock()
  mockery::stub(mod_data_history_server, "render_data_history", mock_render_method)

  shiny::testServer(
    app = mod_data_history_server,
    args = list(study_data = study_data), {
      # Act
      output$data_history

      # Assert
      mockery::expect_args(
        mock_render_method,
        n = 1,
        data = .study_data_history()
      )
    }
  )
})

Test are green now . We fulfilled both points of our implementation plan:

✔ | F W S  OK | Context
✔ |         4 | mod_data_history [0.4s]

══Results ══════════════════════════════════════════════════════════════════════════════════════
Duration: 0.4 s

[ FAIL 0 | WARN 0 | SKIP 0 | PASS 4 ]

The whole test file looks like this:

.study_data_history <- function() {
  tibble::tibble(
    data_name = c("data_1_v1", "data_1_v2", "data_1_v3"),
    user = c("user_1", "user_2", "user_1"),
    updated = c("2023-06-29 17:49:12", "2023-05-29 17:49:12", "2023-04-29 17:49:12"),
    size = c(1000, 1000, 1000)
  )
}

.create_mock_study_data <- function() {
  mock <- create_mock_r6_class(StudyData)
  mock$get_data_history <- mockery::mock(.study_data_history())
  mock
}

describe("mod_data_history", {
  it("should fetch data history", {
    # Arrange
    study_data <- .create_mock_study_data()
	
    shiny::testServer(
      app = mod_data_history_server,
      args = list(study_data = study_data), {
        # Act

        # Assert
        mockery::expect_called(
          study_data$get_data_history,
          n = 1
        )
      }
    )
  })

	it("should display data history", {
	  # Arrange
	  study_data <- .create_mock_study_data()
	  mock_render_method <- mockery::mock()
	  mockery::stub(mod_data_history_server, "render_data_history", mock_render_method)
	
	  shiny::testServer(
	    app = mod_data_history_server,
	    args = list(study_data = study_data), {
	      # Act
	      output$data_history
	
	      # Assert
	      mockery::expect_args(
	        mock_render_method,
	        n = 1,
	        data = .study_data_history()
	      )
	    }
	  )
	})
})

Now we only need an implementation of the render_data_history function, the design of this function can also be driven by tests. Start from listing observable criteria – how we expect this function to behave. Add the first test case, red → green → refactor. The function will accept data from StudyData$get_data_history and return a reactable object, since we chose this package for rendering the data.

Call Your Code from the Legacy Code

Once we implement render_data_history function, we can inject new module to the existing code:

mod_data_ui <- function(id) {
  ns <- shiny::NS(id)
  shiny::div(
    shiny::h1("Data"),
    # ...,
    mod_data_history_ui(ns("data_summary"))
  )
}

mod_data_server <- function(input, output, session) {
  # ...
  mod_data_history_server("data_summary", study_data)
}

Now we can mark our acceptance criteria as done!

Summarizing Sprout Technique and Test Driven Development

Using Sprout Technique and Test Driven Development, we’ve successfully injected a new module into a legacy one. Thanks to tests we have a documented characterization of this new module, it:

We have robust tests that expect a call to an established interface and expect a call to a rendering function. This test suite characterizes what this module does without knowing those functions’ implementation details. Lower level details are covered by unit tests for both functions – it allows this module and those functions to evolve independently of each other. Tests for the module will remain valid when:

Start using TDD now to iterate faster and more confidently with legacy codebases!

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