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As software development undergoes multiple iterations, the team is expected to build new functionality onto existing code over a short timeline Inability to do so could cause missed deadlines and nancial hardships Therefore, the team was in a dif cult position They had already accepted and paid for the outsourcers code, because it operated satisfactorily Now they had to discard several application components and spend additional resources to rewrite them They had turned to outsourcing to reduce costs, but if this trend continued, outsourcing was likely to become a liability The team manager decided that in order to make outsourcing a reliable and cost-effective development strategy, the company needed to mandate that outsourced code satisfy a speci c set of objective and measurable standards.

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With a code length of eight (spreading factor 8), which corresponds to a user data rate of 384 kbit/s in the downlink direction, only eight users can communicate at this speed With a code length of 256 on the other hand, 256 users.

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Thus, there was a need for a quality control system that would automatically check and measure the level of compliance of the submitted code with the required standards It was important that the outsourcers knew exactly what was expected of them, and the company was able to identify noncompliant code during each code delivery After evaluating the above problems, the team manager identi ed the need for a comprehensive approach to enforcing coding standards in order to improve product quality He realized that having coding standards has little bene t without a way of enforcing them comprehensively throughout the.

In addition to the use of conditional speci cations like Table 11.4 to calibrate simpler dependence measures, this form of speci cation is very useful for more complex forms of dependence (Cooper and Chapman, 1987). In this context the use of conditional speci cations can become a form of causal structuring of dependence, asking questions about why B ought to vary with A in a particular pattern. Once these questions are raised, it is often more practical to explicitly address the possible need for a causal modelling structure. Indeed, complex models of the SCERT (Synergistic Contingency Planning and Review Technique) variety discussed earlier can be seen as a way of addressing dependence in this spirit. If, for example, the reason the costs of items A and B are correlated is that they will be the responsibility of either contractor X or contractor Y , X being very ef cient and Y being very inef cient, with political pressures determining the outcome, it may be useful to model this choice directly, as indicated in Table 11.7. Table 11.8 computes the associated cost distribution, de ned as Cm , the m subscript denoting causally modelled. Those who do not follow the probability tree calculations embedded in Table 11.8 do not need to worry about the computational detail: what matters is the simplicity and clarity of a format like Table 11.7 relative to a format like Table 11.4, and the quite different results they yield. The result provided in Table 11.8 involves much stronger dependence than even Cp , with very little chance of a value of 20 and 16 or 24 much more likely. That is, the example provided by

(11.20)

Second, instead of trying to draw directly from the support of A, we draw from a truncated density with support that is easy to compute and encompasses the support of A (but is larger than it), transform back via = U A + (h + g)/2, and accept the falls within its (easy to verify) support, [g, h] The encompassing draw only if support we use for each element in the vector A is the hypercube [ Q, Q], where the scalar Q is the maximum Euclidean distance from to any of the 2k corners of the hyperrectangle de ned by the bounds Since by de nition [g, h], we should normally avoid the sometimes common pitfall of rejection sampling having to do an infeasible number of draws from A to accept each draw of .

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