>  what is a good strategy for generating the underlying data for the tests?

I would use a modified version of the second approach:

Generate fake data such that for each request X there is a well-defined, intentionally constructed set of results Y that will be returned.

But instead of querying the database directly your searchengine should be implemented against datasource-specific repository-interfaces

Each repository-interface has one implementation that uses the database and one fake implementation that can read the result from a human-readable textfile. This way your testdata is less dependant to database-schema-changes.

• While testing the searchengine uses the fake-repository.
• For each test there is test-specific answer-file that can be maintained with a text editor.

What is a good strategy for generating the underlying data for the tests?

I would use a modified version of the second approach:

Generate fake data such that for each request X there is a well-defined, intentionally constructed set of results Y that will be returned.

But instead of querying the database directly your searchengine should be implemented against datasource-specific repository-interfaces

Each repository-interface has one implementation that uses the database and one fake implementation that can read the result from a human-readable textfile. This way your testdata is less dependant to database-schema-changes.

• While testing the searchengine uses the fake-repository.
• For each test there is test-specific answer-file that can be maintained with a text editor.

     >  what is a good strategy for generating the underlying data for the tests?

I would use a modified version of the second approach:

Generate fake data such that for each request X there is a well-defined, intentionally constructed set of results Y that will be returned.

But instead of querying the database directly your searchengine should be implemented against datasource-specific repository-interfaces

Each repository-interface has one implementation that uses the database and one fake implementation that can read the result from a human-readable textfile.

• While testing the searchengine uses the fake-repository.
• For each test there is test-specific answer-file that can be maintained with a text editor.

     >  what is a good strategy for generating the underlying data for the tests?

I would use a modified version of the second approach:

Generate fake data such that for each request X there is a well-defined, intentionally constructed set of results Y that will be returned.

But instead of querying the database directly your searchengine should be implemented against datasource-specific repository-interfaces

Each repository-interface has one implementation that uses the database and one fake implementation that can read the result from a human-readable textfile. This way your testdata is less dependant to database-schema-changes.

• While testing the searchengine uses the fake-repository.
• For each test there is test-specific answer-file that can be maintained with a text editor.