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Anyone who has ever written a program knows that mistakes are inevitable Anyone who writes software professionally knows that producing good software requires a systematic approach to nding bugs By far the most widely used approach to bug nding is dynamic testing, which involves running the software and comparing ts output against an expected result Advocates of extreme programming want to see a lot of small tests (unit tests) written by the programmer even before the code is written Large software organizations have big groups of dedicated QA engineers who are responsible for nothing other than writing tests, running tests, and evaluating test results If you ve always thought of security as just another facet of software quality, you might be surprised to learn that it is almost impossible to improve software security merely by improving quality assurance In practice, most software quality efforts are geared toward testing program functionality The purpose is to find the bugs that will affect the most users in the worst ways Functionality testing works well for making sure that typical users with typical needs will be happy, but it just won t work for finding security defects that aren t related to security features Most software testing is aimed at comparing the implementation to the requirements, and this approach is inadequate for finding security problems The software (the implementation) has a list of things it s supposed to do (the requirements) Imagine testing a piece of software by running down the list of requirements and making sure the implementation fulfills each one If the software fails to meet a particular requirement, you ve found a bug This works well for testing software functionality, even security functionality, but it will miss many security problems because security problems are often not violations of the requirements Instead, security problems are frequently unintended functionality that causes the program to be insecure Whittaker and Thomson describe it with the diagram in Figure 11 [Whittaker and Thompson, 2003].





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each category reported above We report the individual project values, in millions of dollars, of each of the 10 projects in the following sets for each category: Hardware 5 f4:451; 4:701; 3:713g Personnel 5 f7:45; 8:089; 7:761; 8:286g Software 5 f1:237g Other 5 f4:09; 4:27g The total funding requested for all the projects is about $5405 million and the IT budget is $2539 million Thus the projects selected by the D-Rank method are {8286, 8089, 7761}, and those selected by the A-Rank method are {1237, 3713, 409, 427, 4451, 4701}, respectively Assuming that other non-IT-related costs are constant and ignoring these costs and adding a depreciation tax shield for software projects selected using the A-Rank method, we have after-tax pro ts for projects selected by D-Rank and A-Rank methods of $1,64507 and $1,64619 million, respectively We can repeat this procedure for the other nine values in the IT budget and revenue sets to obtain results for all 10 simulations Our actual simulation experiment, for a given complexity level, consisted of a company with one randomly generated IT budget value, revenue value, projects to be funded, and solution of the ITCB problem using four different approaches the A-Rank approach, the D-Rank approach, the FRSA approach, and the SSA approach We consider two different levels of complexity In the rst level of complexity, the number of projects to be considered for funding was considered constant and equal to 40 Ten projects within each of the four categories (hardware, personnel, software, and other were generated with particular means and standard deviations mentioned previously The worst-case solution search space complexity for the rst level of complexity was well over one trillion unique combinations In the second level of complexity, the value of projects to be considered for funding was considered constant and equal to 60 Fifteen projects within each of the four categories were randomly generated The worst-case solution search space complexity for the second level of complexity was over one million trillion unique combinations For each of the two levels of complexity 50 different random simulation experiments were conducted For our statistical tests on means, we use a simple pairwise difference in means t-test There are other multiple comparison procedures that are available, including Tukey s test and Scheffe s test The multiple comparison procedures are typically more suitable as post hoc tests after an analysis of variance (ANOVA) for some planned hypothesis is conducted The multiple comparison procedures also assume that the pairwise comparisons are not independent Given independent simulation runs, we nd the pairwise differences in means t-test suitable for our study For the rst level complexity, the average required funding for all 40 projects was $1688 million and the average IT budget was about.

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