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Dado o seguinte comando SQL:
SELECT a.* FROM T1 a INNER JOIN T2 b ON a.id = b.id_t1 ORDER BY a.id desc;
Informe se é verdadeiro (V) ou falso (F) o que se afirma a seguir e assinale a alternativa com a sequência correta.
( ) A tabela T1 precisa ter uma coluna chamada “a”.
( ) Caso a tabela T2 não possua dados, é correto afirmar que a consulta não retornará nenhum resultado.
( ) Considerando a coluna “id” da tabela T1 contendo um valor inteiro incrementado automaticamente a cada inserção, é correto afirmar que o resultado apresentará primeiramente as últimas linhas inseridas, desde que tenham o relacionamento correspondente na tabela T2.
Set the Table
When you begin writing tests, you will discover a common pattern:
1. Create some objects
2. Stimulate them
3. Check the results
While the stimulation and checking steps are unique test-to-test, the creation step is often familiar. I have a 2 and 3. If I add them, I expect 5. If I subtract them, I expect – 1, if I multiply them, I expect 6. The stimulation and expected results are unique, the 2 and the 3 don’t change.
If this pattern repeats at different scales (and it does), then we’re faced with the question of how often do we want to create new objects. Looking back at our initial set of constraints, two constraints come into conflict:
· Performance—we would like our tests to run as quickly as possible
· Isolation—we would the success or failure of one test to be irrelevant to other tests
For performance sake, assuming creating the objects (we’ll call them collectively the “fixture”) is expensive, we would like to create them once and then run lots of tests.
But sharing objects between tests creates the possibility of test coupling. Test coupling can have an obvious nasty effect, where breaking one test causes the next ten to fail even though the code is correct. Test coupling can have a subtle really nasty effect, where the order of tests matters. If I run A before B, they both work, but if I run B before A, then A fails. Worse, the code exercised by B is wrong, but because A ran first, the test passes.
Kent Beck – Test-Driven Development By Example. Addison-Wesley Professional; Edição: 1. Novembro, 2002. Page 82.
Set the Table
When you begin writing tests, you will discover a common pattern:
1. Create some objects
2. Stimulate them
3. Check the results
While the stimulation and checking steps are unique test-to-test, the creation step is often familiar. I have a 2 and 3. If I add them, I expect 5. If I subtract them, I expect – 1, if I multiply them, I expect 6. The stimulation and expected results are unique, the 2 and the 3 don’t change.
If this pattern repeats at different scales (and it does), then we’re faced with the question of how often do we want to create new objects. Looking back at our initial set of constraints, two constraints come into conflict:
· Performance—we would like our tests to run as quickly as possible
· Isolation—we would the success or failure of one test to be irrelevant to other tests
For performance sake, assuming creating the objects (we’ll call them collectively the “fixture”) is expensive, we would like to create them once and then run lots of tests.
But sharing objects between tests creates the possibility of test coupling. Test coupling can have an obvious nasty effect, where breaking one test causes the next ten to fail even though the code is correct. Test coupling can have a subtle really nasty effect, where the order of tests matters. If I run A before B, they both work, but if I run B before A, then A fails. Worse, the code exercised by B is wrong, but because A ran first, the test passes.
Kent Beck – Test-Driven Development By Example. Addison-Wesley Professional; Edição: 1. Novembro, 2002. Page 82.
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