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data and add risk to any conclusions that you draw However, you may still be able to learn a lot from these projects; just be wary of placing too much reliance on your conclusions Project Rating The ISBSG considers that projects with a data quality rating of A or B are suitable for statistical analysis Projects rated C or D may still provide valuable data, but uncertainty about some of their size or effort values means that it is best not to include them in statistical analyses
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Refer to Normalized work effort in the Glossary
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Appendix A:
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What is in the iSBSG Repository
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Lines of Code Although the ISBSG Repository does include projects that are sized using LOC, these are not validated and should not be used for benchmarking Unless your aim is to benchmark your project or organization against the entire repository, you are probably not interested in projects that are very different from your own You will want to select projects that are similar to yours in important project attributes The ISBSG suggests that the most important criteria for selecting projects are: Size If yours is a really large project, there is not much value to you in studying small ones and vice versa Development type redevelopment New development, enhancement, or
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Primary programming language or language type For example, 3GL, 4GL Development platform Mainframe, midrange, or PC Other criteria that may be important are organization type, business area type, application type, user base, and development techniques Bear in mind that as you add more selection criteria, the number of projects selected inevitably gets smaller You can end up with small groups of projects, or perhaps even no projects that satisfy all criteria How important the group size is will depend on what you want to do with the data It is important that the data subset you use have integrity The key points are that you choose only appropriately rated data; measurements are defined the same way (that is, IFPUG versions and effort measures with different time units); and measurements apply to the same thing (that is, effort normalization and effort levels) Many practitioners will want to compare their IT development performance with relevant projects in the data set For many, the analysis process will consist of selecting projects based upon multiple criteria, followed by some form of summary analysis Some may wish to use regression analysis to derive the equation of the line of best fit through the data points and to use this equation as an estimating device When performing such analyses, it is worthwhile bearing in mind that use of many selection criteria could result in a very small or even zero sample size A sample size of more than 20 should provide reasonable results; however, not much can be concluded from a sample size of 5 or fewer It is not uncommon for people to use a mean or median value, or to use a regression line for estimation purposes Bear in mind that
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estimation parameters derived from a sample of the population may not have much relevance to a particular project It may well be that population surveys show that x percent of males will suffer a heart attack by the time they are 60 years of age This percentage does not indicate the probability that you will suffer a heart attack before you are 60 Consequently, a regression line, even if derived from a sample of projects with similar characteristics to your own, may not be a good estimation tool unless you have reason to believe that your attributes are broadly similar Means, medians, and regression lines should be used with great caution, especially where sample sizes are small and variances or standard deviations are high
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