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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 unction 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 conducted 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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