Testing

Data-Driven Tests

Data-driven testing runs an entire test suite once per row of a test data set — a simple table of columns and rows attached to the suite. Instead of duplicating a login case for ten user accounts, you write the case once, reference columns like {{data.email}}, and let Routebase iterate.

Where data sets live

Data sets belong to a test suite. Open the suite in the Test Runner, click Suite Settings at the bottom of the sidebar, and scroll to the Data Sets section. Before you create one, the section shows No test data sets yet — "Create a data set to parameterize your test cases with different input values." — with Import CSV/JSON and Create First Data Set buttons.

Each data set card shows its name, its size (2 cols · 10 rows), and an optional description. Managing data sets requires the tests:write permission.

Creating and editing a data set

  1. In Data Sets, click Add Data Set.
  2. Enter a Name (e.g. User registration data) and an optional Description, then click Create Data Set. A new set starts with one column and one empty row.
  3. Expand the set and click Edit Data to open the inline table editor:
    • Rename columns by typing into the header cells. Every column needs a name, and names must be unique — the editor blocks saving otherwise.
    • Add and remove columns with the column buttons (at least one column must remain).
    • Click Add Row for more rows; edit cells directly in the table.
  4. Click Save. Use Edit Name to change the name or description later.

All cell values are stored as text; type conversion happens where the value is used.

The data set editor with columns and rows in the Suite Configuration sheet

Importing data

Click Import to open the Import Test Data dialog — "Upload a CSV or JSON file, or paste data directly to import into a test data set."

  • Upload File supports CSV, JSON, and TSV files. The data set name is pre-filled from the filename.
  • Or Paste Data accepts raw CSV or JSON; the format is auto-detected and shown as a badge (CSV detected / JSON detected).
  • CSV: the first row is treated as headers; quoted fields and escaped quotes are handled.
  • JSON: an array of objects (keys become columns), an array of arrays, or an array of primitives (single Value column).

A Preview shows the first 10 rows. Then choose the Import Mode:

Mode Effect
Create new data set Imports into a fresh data set (name required).
Replace existing data set All existing data will be replaced with the imported data.
Append to existing data set Imported rows will be added to the existing data.

Click Import Data to finish.

Referencing row values

Reference a column anywhere a request template accepts placeholders — URL, headers, body, and assertion expected values:

{{data.<columnName>}}

For example, with columns email and expectedStatus:

  • URL: {{BASE_URL}}/users?email={{data.email}}
  • Assertion: Status Code equals {{data.expectedStatus}} — expected values are substituted per row, so a single case can assert different outcomes for different rows.

Data placeholders are highlighted in purple in the editor, and typing {{data. opens an autocomplete listing the selected data set's columns. Data variables coexist with environment variables (referenced by their bare name, {{BASE_URL}}), fixtures ({{fixture.*}}), and dynamic values ({{$uuid}}, {{$timestamp}}, ...) in the same template.

Running a data-driven suite

  1. In the suite toolbar, open the data set selector (it reads No Data Set when nothing is selected) and pick a data set — the option shows its row count, and the tooltip its dimensions.
  2. Click Run All.

Routebase executes the full suite once per data row. Rows are independent: each iteration starts fresh with its own row values — extracted runtime variables do not carry over between rows.

Reading the results

After a data-driven run, the results panel shows a per-row breakdown:

  • A header with the data set name, a rows passed badge (e.g. 8/10 rows passed), the total duration, the row count, and a progress bar.
  • One expandable entry per row (Row 1, Row 2, ...) with a pass/fail icon, a preview of the row's first values, and its duration.
  • Expanding a row reveals its Data Values (every column/value pair) and the result of each test case in that iteration — status code, error message, and duration.

The completion toast summarizes the run, e.g. "All 24 test cases passed across 8 data rows".

Data sets vs. fixtures

Both feed data into tests, but they solve different problems:

Use a data set when... Use a fixture when...
You want to repeat the same suite for each row You want one structured blob of reference data
Values are flat (one row = one iteration) Values are nested objects/arrays
The data varies the inputs of a run The data is shared across tests, mocks, and docs
  • Test Suites — the cases a data-driven run executes
  • Test Data — fixtures, seeds, and snapshots
  • Test Scenarios — multi-step workflows with value chaining
  • Variables — environment-scoped values for your templates