Statistical analysis with missing data
Statistical analysis with missing data
Software sizing and estimating: Mk II FPA (Function Point Analysis)
Software sizing and estimating: Mk II FPA (Function Point Analysis)
Empirical studies of assumptions that underlie software cost-estimation models
Information and Software Technology
Software Cost Estimation with Incomplete Data
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Software Engineering Economics
Software Engineering Economics
Software Development Cost Estimation Using Function Points
IEEE Transactions on Software Engineering
A Further Empirical Investigation of the Relationship Between MRE and Project Size
Empirical Software Engineering
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Dealing with Missing Software Project Data
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
A Procedure for Assessing the Influence of Problem Domain on Effort Estimation Consistency
Software Quality Control
Practical Statistics for Medical Research
Practical Statistics for Medical Research
A productivity benchmarking case study using Bayesian credible intervals
Software Quality Control
Using industry based data sets in software engineering research
Proceedings of the 2006 international workshop on Summit on software engineering education
A comprehensive empirical evaluation of missing value imputation in noisy software measurement data
Journal of Systems and Software
Tests for consistent measurement of external subjective software quality attributes
Empirical Software Engineering
Evaluation of preliminary data analysis framework in software cost estimation based on ISBSG R9 Data
Software Quality Control
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
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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.