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perform only one task at a time (and we can t split them into pieces), so we re looking for a set of jobs that we can actually do, and that gives us as large a total profit as possible. However, to keep things simple, this time all tasks take the same amount of time one time step. If d is the latest deadline, as measured in time steps from the starting point, we can then start with an empty schedule of d empty slots, and then fill those slots with tasks. The solution to this problem is, in a way, doubly greedy. First, we consider the tasks by decreasing profit (that is, we start with the most profitable task); that s the first greedy part. Then comes the second part: we place each task in the latest possible free slot that it can occupy, based on its deadline. If there is no free, valid slot, we discard the task. Once we re done, if we haven t filled out all the slots, we re certainly free to perform tasks earlier, so as to remove the gaps it won t affect the profit (or allow us to perform any more tasks). To get a feel for this solution, you might want to actually implement it (Exercise 7-20). The solution sounds intuitively appealing; we give the profitable tasks precedence, and we make sure they use a minimum of our precious early time, by pushing them as far toward their deadline as possible. But, once again, we won t rely on intuition. We ll use a bit of induction, showing that as we add tasks in this greedy fashion, our schedule stays promising.



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The client code for this example, which is shown below, demonstrates that we can pass an argument to a method of a session bean that exists on the server, operate on the argument in the method, and return a different value to the client. More specifically, the code loops through the arguments that were passed to the client s main() method via the command line, and passes them one at a time to the getEchoString() method of the session bean class. This is accomplished by calling the getEchoString() method of the bean interface that exists on the client:





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Spring s testing support facilities can inject beans automatically from the managed application context into your tests as test fixtures. When using Spring s JUnit 3.8 legacy support in releases prior to 2.5, your test class can extend the AbstractDependencyInjectionSpringContextTests base class, which is a subclass of AbstractSingleSpringContextTests, to have its test fixtures injected automatically. This class supports two ways of performing dependency injection. The first auto-wires beans by type via setter methods. The second auto-wires beans by name via protected fields. In Spring 2.5 s TestContext framework, DependencyInjectionTestExecutionListener can automatically inject dependencies into your tests. If you have this listener registered, you can simply annotate a setter method or field of your test with Spring s @Autowired annotation or JSR-250 s @Resource annotation to have a fixture injected automatically. For @Autowired, the fixture will be injected by type, and for @Resource, it will be injected by name. This support is consistent with the annotation support in Spring 2.5.

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Caution Although the following proof does not involve any deep math or rocket science, it is a bit involved and might hurt your brain. If you re not up for it, feel free to skip ahead to the chapter summary.

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Injecting Test Fixtures with JUnit 3.8 Legacy Support When using Spring s JUnit 3.8 legacy support to create tests, your test class can extend AbstractDependencyInjectionSpringContextTests to have its test fixtures injected from beans in the managed application context. You can define a setter method for the fixture you want to be injected. package com.apress.springrecipes.bank; import org.springframework.test.AbstractDependencyInjectionSpringContextTests; public class AccountServiceJUnit38LegacyTests extends AbstractDependencyInjectionSpringContextTests { private AccountService accountService; public void setAccountService(AccountService accountService) { this.accountService = accountService; } protected void onSetUp() throws Exception { accountService.createAccount(TEST_ACCOUNT_NO); accountService.deposit(TEST_ACCOUNT_NO, 100); } ... }

for (int i = 0; i < args.length; i++) { String returnedString = simpleSession.getEchoString(args[i]); System.out.println("sent string: " + args[i] + ", received string: " + returnedString); }

Note that invoking the getEchoString() method of the bean interface on the client invokes the getEchoString() method of the session bean class on the server. This is possible due to the stub classes described above. These stub classes are also the reason that invoking the create() method of the bean s home interface on the client was able to cause the session bean to be created on the server. When the create() method of the home interface was called using the following line of code:

As is invariably the case, the initial, empty solution is promising. In moving beyond the base case, it s important to remember that the schedule is really promising only if it can be extended to an optimal schedule using the remaining tasks (as this is the only way we re allowed to extend it). Now, assume we have a promising partial schedule P. Some of its slots are filled in, and some are not. The fact that P is promising means that it can be extended to an optimal schedule let s call it S. Also, let s say T is the next task under consideration. We now have four cases to consider: T won t fit in P, because there is no room before the deadline. In this case, T can t affect anything, so P is still promising once T is discarded. T will fit in P, and it ends up in the same position as in S. In this case, we re actually extending toward S, so P is still promising. T will fit, but it ends up somewhere else. This might seem somewhat troubling. T will fit, but S doesn t contain it. Even more troubling, perhaps.

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