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Before we move onto the next circle, where we will add enough functionality to our plug-in to fully integrate testing into programming and touch all of the activities necessary to build production-quality plug-ins, let's review what we've seen so far Much of the architecture of Eclipse is driven from the desire to build the whole environment out of plug-ins but to maintain fast startup Startup time should be constant, not proportional to the number of installed plug-ins In particular, you don't want to pay for plug-ins that are installed but not used If I have a C++ environment installed but I don't use it, it should have no impact at all on my computing experience Resolving the conflict between building the system out of lots of little pieces and being able to start up quickly drives the Eclipse architecture We laid out a design for our contributed JUnit plug-in as a core test runner with various presentations of tests and results Our goal in Circle One was to take the core test runner through the entire Contribution Circle PDE extends the basic Eclipse Java development functionality with support for editing the declarative plug-in description (the manifest, stored in pluginxml) PDE lets us run our new plug-in in a separate workspace Rather than clump all of our functionality together monolithically, we divided our system into the core test-running functionality and an extension point that would allow presentations to be notified of testing progress Extensions were only loaded the first time a test was run, keeping startup time to a minimum We packaged our plug-in for automated installation and discovery The plug-in was put into a feature and the feature was placed on an update site The plug-in is ready to be extended

Rainy Day Policy. Example The plan s minimum funded status is 100 percent of ABO. The company will make the necessary contributions to maintain this funded status. In years when the company s pretax profits are above $X, the company will make additional, tax-deductible contributions in order to attain 150 percent ABO funded status. Alternatives Vary the hurdle $X, use profitability (profit margin greater than X percent), use different profitable target (175 percent ABO)

From Wald s equation for the expectation of a sum of a random number of independent random variables (see reference 7), then E(h 1) E(cos i ) E(hopt ) = hopt E(h) E(cos i ) Now, i, E(cos i ) =

[ Team LiB ]

E(h) 1 + = E(Ch ) sin hopt sin hopt Assuming large values for hopt (i.e., events happening far away from the wall, which is the most interesting case in practice since the detection and propagation dif culty increases with distance), we have (since for 0 it is 1 sin ) and 2 2 the result follows. 15.2.4 Local Optimization: The Min-two Uniform Targets Protocol (M2TP) Reference 5 further assumes that the search phase always returns two points p , and p , each uniform in ( , ), and that a modi ed protocol M2TP selects the best of the two points, with respect to the local (vertical) progress. This is in fact an optimized version of the Local Target Protocol. In a similar way as in the proof of the previous lemma, the authors prove the following result: Lemma 2 [5]. The expected hops ef ciency of the min-two uniform targets protocol in the a-uniform case is 2 2(1 cos )

and lim E(Ch ) = 2 ( /2)2 = 2(1 0) 8 1.24

[ Team LiB ]

Advantages Explicit recognition of company s profitability and ability to make contributions above minimum level Builds cushion for unprofitable years rainy day fund when expectation is little, if any contribution would be made Avoids adverse impact of being underfunded, such as balance sheet hit (other comprehensive income), PBGC notification to participants, etc. Provides security to pension participants Reduces PBGC premium to low level and possibly to minimum level

2

When we began writing the code for this book, we did it in a classic exploratory style We thought of some functionality we'd like to add We browsed in Eclipse to get some ideas about how to implement it We put some code together We looked at the result of the new code in the run-time workspace We repeated as necessary As we made progress, we quickly learned developing only in the exploratory style we were not able to write code without errors Say we had added some functionality Were we sure we hadn't broken something We aggressively refactored our code to improve its structure Were we sure we hadn't broken something We wanted a way to regain confidence in our code The benefits we were looking for from our development strategy were:

Thus, reference 5 proves the following: Lemma 3 [5]. The expected hops ef ciency of the min-two uniform targets protocol is 1 E(Ch ) 2 8 1.24

for large h and for 0 . 2 Remark that, with respect to the expected hops ef ciency of the local target protocol, the min-two uniform targets protocol achieves, because of the one additional search, a relative gain that is ( /2 2 /8)/( /2) 21.5%. Reference 5 also experimentally investigates the further gain of additional (i.e., m > 2) searches.