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performance of GA trained ANN Hybrid approaches deserve merit for future investigation as well Using the Department of Defense (DOD) as an example, each military service represents a unique brand of war ghting and providing for the common defense The Joint Chiefs consider scenarios based on combining these unique brands for optimal effect The same can be said about the Department of Homeland Security, where each agency is responsible for a unique brand and contribution to securing the homeland What are the data pro les for these departments and their agencies What are the key operational and performance metrics to deliver speci c outcomes These are questions that must be answered precisely under a smart data strategy at every level of the organization to ensure accountability These questions extend throughout the enterprise to every contractor and contract and supply chain Smart data strategy is the common thread As stated previously, the enabling technology platform on which SOE and smart data is based permits individuals and functional areas of the organization within the enterprise to make improvements as they are ready Entities should not be placed in the position of doing nothing while waiting for another The goal is to promote change and improvement that can happen with local control Optimizing Performance and Breaking from Tradition An example of optimizing performance and breaking from tradition, with improved results, is evidenced by adopting a smart data strategy toward costs, as was shown by pendharkar and Rodger in The Relationship Between Software Development Team Size and Software Development Cost [4] We show that most of the software development cost is related to the programmers salaries and recent evidence shows that team-related factors can affect project performance and its ost Selecting an appropriate team size for software development projects continues to be a challenge for most project managers Larger teams represent better distribution of skills but lead to higher communication and coordination costs Smaller teams lead to lower communication and coordination costs but may result in programming biases that make software maintenance costly Smaller teams, due to homogeneous programmer backgrounds, may lack the problem-solving expertise for large size complex projects In addition to programmer skills, heterogeneity in team member personalities may be desirable in certain phases of the Systems development life cycle (SDLC) Additionally, it has been emphasized that diverse expertise in IT and business application experience is always desirable in any project Software development project tools provide advanced coordination and communication capabilities and are often used to control communication and coordination costs for large sized teams Without the use of software development tools, it is easy to show that team communication requirements increase exponentially with the increase in team size Since most projects use advanced software development tools, the assumption of exponential increase in software development cost with increase in.

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team size will seldom hold true in the real world Even a linear relationship between software development cost and team size may be considered a mute point, at best Understanding the relationship and impact of team size on software development cost is important because most managers, when confronted with strict deadlines, are tempted to increase team size Among the reasons for increasing team size are the following: 1 There is pressure to meet the schedule, as competitive advantage of the technology decreases with time 2 Assigning one person to a project does not mean linear completion time 3 Dividing work into small subprojects allows many people to work on the project simultaneously, which reduces the project completion time Although increasing team size for strict deadlines appears to be a good strategy, it is not really clear if it works well For example, increasing team size will more likely increase the number of defects found by the team and may not necessarily mean the project will be completed on time In fact, most software quality professionals admit that team size should be larger in the beginning or middle of the project than at the end of the project The larger team sizes in the beginning of the project allow for strict testing of the software product in early phases of SDLC In this case, we study the relationship between team size and software development cost Since larger size projects are likely to contain large team size and vice versa, we use software project size in function points (FPs) as a contextual variable We use real-world data from 540 software projects for our data analysis Our data were obtained from the International Software Benchmarking Standards Group (ISBSG) and is publicly available We use production economics theory as a guideline for testing the relationship between team size and software cost We use software effort as a surrogate measure for software cost because once the software effort is known software cost can be computed by multiplying software effort with average hourly programmer wage The existing literature in production function analysis provides three models that can allow us to test the relationship between team size and software effort These three models are the linear regression model, the log-linear model to test nonlinear relationships, and the nonparametric data envelopment analysis (DEA) model Linear and log-linear models assume that the variables used in the models regular values for linear regression and log-transformed values for log-linear models are normally distributed Using the SPSS software package, we onducted a Kolmogorov Smirnov (KS) normality test for three variables and their natural logarithm transformed values to check if the variables and their transformations were normally distributed The KS method tests the null hypothesis that there is no difference between the variable distribution and normal distribution Since a perfectly normal distribution has skew and kurtosis values both equal to zero, we report the skewness and kurtosis values for the variable distributions using the KS test.

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