*/
    @Test
    public void testLoadPropertiesUrlThrowIOException() {
        exception.expect(IORuntimeException.class);
        exception.expectMessage(is("[ECL0040]IOException occurred, because java.io.IOException: load"));
        final Properties properties = new IOExceptionOccurProperties();
        PropertiesUtil.load(properties, url);
    }

			queryParams: map[string]string{},
			expected:    ErrNone,
		},
	}

	// Run each test case individually.
	for i, testCase := range testCases {
		// Turn the map[string]string into map[string][]string, because Go.
		query := url.Values{}
		for key, value := range testCase.queryParams {
			query.Set(key, value)
		}
		// Create a request to use.

### Motivation

This library makes it easy to test that your app Does The Right Thing when it
makes HTTP and HTTPS calls. It lets you specify which responses to return and
then verify that requests were made as expected.

Because it exercises your full HTTP stack, you can be confident that you're
testing everything. You can even copy & paste HTTP responses from your real web
server to create representative test cases. Or test that your code survives in

 *       that we could probably work around. Or we could manually strip the test from open-source
 *       Guava while continuing to run it internally, as we do with many other tests. This would
 *       suffice because we our Android users and tests are using the open-source version, which
 *       would no longer have the problematic test. But why bother when we can instead strip it with
 *       a more precisely named annotation?

  @Nullable V getIfCached(@Nullable Object key) {
    Entry<K, V> entry = cacheEntry; // store local reference for thread-safety

    // Check cache. We use == on purpose because it's cheaper and a cache miss is ok.
    if (entry != null && entry.getKey() == key) {
      return entry.getValue();
    }
    return null;
  }

  void clearCache() {
    cacheEntry = null;
  }

 * Null row keys, columns keys, and values are not supported.
 *
 * <p>Lookups by row key are often faster than lookups by column key, because the data is stored in
 * a {@code Map<R, Map<C, V>>}. A method call like {@code column(columnKey).get(rowKey)} still runs
 * quickly, since the row key is provided. However, {@code column(columnKey).size()} takes longer,

import jcifs.Configuration;

/**
 * Unit tests for {@link SmbComQueryInformation}.  The class is very small and
 * mainly focuses on serialising a command header that contains a file name.
 * <p>
 * Because most of the logic lives in {@link jcifs.internal.smb1.ServerMessageBlock}
 * the tests create a mock {@link Configuration} and instantiate SmbComQueryInformation

 * Null row keys, columns keys, and values are not supported.
 *
 * <p>Lookups by row key are often faster than lookups by column key, because the data is stored in
 * a {@code Map<R, Map<C, V>>}. A method call like {@code column(columnKey).get(rowKey)} still runs
 * quickly, since the row key is provided. However, {@code column(columnKey).size()} takes longer,

)

// IsRegisterShift reports whether the token is one of the ARM register shift operators.
func IsRegisterShift(r ScanToken) bool {
	return ROT <= r && r <= LSH // Order looks backwards because these are negative.
}

func (t ScanToken) String() string {
	switch t {
	case scanner.EOF:
		return "EOF"
	case scanner.Ident:
		return "identifier"
	case scanner.Int:
		return "integer constant"

But if your connection to the server is lost, the **running process** will probably die.

And if the server is restarted (for example after updates, or migrations from the cloud provider) you probably **won't notice it**. And because of that, you won't even know that you have to restart the process manually. So, your API will just stay dead. 😱

### Run Automatically on Startup { #run-automatically-on-startup }