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In some release build functions, however, you might see some instructions interspersed between PUSH and MOV CPUs with multiple pipelines, such as those in the Pentium family, can decode multiple instructions at a time, so the optimizer will try to set up the instruction stream to take advantage of this capability Depending on the optimizations you chose when compiling your code, you can also have functions that don't use EBP as the frame pointer Those procedures have what is called frame pointer omission (FPO) data When you look at the disassembly for functions with FPO data, the code in the function looks as if it just starts manipulating data You'll find out how to identify one of these functions in the next section The following common epilog undoes the operations of the prolog and is the one you'll see the most in debug builds This epilog matches the preceding prolog.

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This static Int64 property returns the number of bytes that are currently in use by all AppDomains controlled by the current CLR instance . The number is accurate as of the last garbage collection .

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__asm { // Standard epilog teardown MOV ESP , EBP POP EBP } In release builds, using the LEAVE instruction introduced earlier is faster than using the MOV/POP sequence, so you might see that the epilog will be just a LEAVE instruction The LEAVE instruction is identical to the MOV/POP sequence In debug builds, the compilers default to MOV/POP Interestingly, the x86 CPU has a corresponding ENTER instruction to set up the prolog, but it's slower than the PUSH/MOV/ADD sequence, so the compilers don't use it How compilers choose to generate code depends a great deal on whether your program is optimized for speed or for size If you optimize for size, as I strongly recommended in 277 // Restore the stack value // Restore the saved stack frame register EBP , ESP ESP , 20h // Save the stack frame register.

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Similar to the complexity metrics discussed in 7, "Analyzing Risk with Code Complexity," code churn measurements can be indicative of where more bugs are likely to be located In a sense, this is an intuitive metric Other than writing new code to add features, the reason for code changing (churning) is most often a result of fixing a known bug Significant percentages of bug fixes either don't actually fix the problem or cause another error to occur Both of these situations require additional code changes (churn) to fix the new (or remaining) failures Often, this scenario repeats; if the code is particularly complex, the cycle can continue for several iterations until all known bugs are fixed, and no new failures are found (odds are, however, that highly churned code will have even more bugs remaining).

// Set the local function stack frame to ESP // Make room on the stack for 0x20 bytes of // local variables The SUB instruction.

Firewalls typically allow all traffic exchanges originating from the internal network and block everything else. You then need to configure, where necessary, specific packet fil ters to allow traffic exchanges that originate from the external network to the VPN server. In Figure 10-31, for example, packet filters are configured to allow request traffic to the VPN server and answer traffic from the same server. Because a Web server is also deployed in the perimeter network, packet filters have been defined to allow requests and answers to and from the Web server as well. Packet filters are not necessary for access to services beyond the VPN server. Generally, firewalls cannot filter packets based on the information encapsulated within the VPN tunnel. Once these packets reach the VPN server, only then are they stripped of the VPN header and decrypted. At this point, they can be routed freely to any internal service.

Keep in mind that churn is another metric that falls into the "smoke-alarm" category; excessive churn doesn't always mean that there are dozens of new bugs to be found, but it is an indicator that you might need to take a closer look at the area of the product that is changing Research on Code Churn Microsoft Research has investigated the relationship of churn and bug density using several different churn metrics, including churned (changed) lines of code, deleted lines, and numbers of files changed The researchers generated churn metrics for the Microsoft Windows Server 2003 source code and bug data from the bug management system and found a correlation between the two Next, the researchers created a model using these metrics and used a randomly selected set containing two-thirds of the binaries to build a prediction model.

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