Cannot understand the difference in custom types

[sharadregoti]

I have a custom type of map called as collection which is map[string]Fields. You can check the image for detailed info.
When my test case failed, I am not able to understand the difference in the 4th argument clearly.

Types info

Test Case

[brackendawson]

You have primed your mock with an initialised Collection, possibly using the literal syntax: Collection{}. But then you have called the mock with a nil Collection. The test output does show this: 4: main.Collection(nil) vs 4: main.Collection{}, however this part is less helpful: 4: FAIL: (main.Collection=map[]) != (main.Collection=map[]).

This will happen with non-custom slices and maps as well. This line is the source of the format:

testify/mock/mock.go

Line 811 in 6241f9a

expectedFmt = fmt.Sprintf("(%[1]T=%[1]v)", expected)

Perhaps it could be changed to use the go syntax here also by adding this #:

        expectedFmt = fmt.Sprintf("(%[1]T=%#[1]v)", expected)

For the OP

You need to prime the mock with a nil Collection, this is one way of doing that when your type is a map or a slice:

package main

import (
	"context"
	"testing"

	"github.com/stretchr/testify/mock"
)

type Collection map[string]int

type MockThing struct {
	mock.Mock
}

func (m *MockThing) SchemaInspection(ctx context.Context, alias, name, logs string, collection Collection) {
	m.Called(ctx, alias, name, logs, collection)
}

func TestCollection(t *testing.T) {
	thing := &MockThing{}
	thing.Test(t)

	// Incorrect
	thing.On("SchemaInspection",
		mock.Anything,
		"alias",
		"db_name",
		"invocation_logs",
		Collection{},
	).Return().Once()
	thing.SchemaInspection(context.Background(), "alias", "db_name", "invocation_logs", nil)

	// Correct
	thing.On("SchemaInspection",
		mock.Anything,
		"alias",
		"db_name",
		"invocation_logs",
		*new(Collection),
	).Return().Once()
	thing.SchemaInspection(context.Background(), "alias", "db_name", "invocation_logs", nil)
}

[dolmen]

@sharadregoti Do not post issues as screenshot. Copy-pasting code is not possible. In addition the contrast is very low on your snapshot and is hard to read for me.