Assessing Variation in Development Effort Consistency Using a Data Source with Missing Data

Abstract In this study the authors analyse the International Software Benchmarking Standards Group data repository, Release 8.0. The data repository comprises project data from several different companies. However, the repository exhibits missing data, which must be handled in an appropriate manner, otherwise inferences may be made that are biased and misleading. The authors re-examine a statistical model that explained about 62% of the variability in actual software development effort (Summary Work Effort) which was conditioned on a sample from the repository of 339 observations. This model exhibited covariates Adjusted Function Points and Maximum Team Size and dependence on Language Type (which includes categories 2nd, 3rd, 4th Generation Languages and Application Program Generators) and Development Type (enhancement, new development and re-development). The authors now use Bayesian inference and the Bayesian statistical simulation program, BUGS, to impute missing data avoiding deletion of observations with missing Maximum Team size and increasing sample size to 616. Providing that by imputing data distributional biases are not introduced, the accuracy of inferences made from models that fit the data will increase. As a consequence of imputation, models that fit the data and explain about 59% of the variability in actual effort are identified. These models enable new inferences to be made about Language Type and Development Type. The sensitivity of the inferences to alternative distributions for imputing missing data is also considered. Furthermore, the authors contemplate the impact of these distributions on the explained variability of actual effort and show how valid effort estimates can be derived to improve estimate consistency.