The cognitive flexibility theory0: an approach for teaching Hypermedia Engineering
Proceedings of the 6th annual conference on Innovation and technology in computer science education
Comparing Software Prediction Techniques Using Simulation
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Comparing effort prediction models for web design and authoring using boxplots
ACSC '01 Proceedings of the 24th Australasian conference on Computer science
Improving Supervised Learning by Feature Decomposition
FoIKS '02 Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems
Issues on the Effective Use of CBR Technology for Software Project Prediction
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
An empirical study of maintenance and development estimation accuracy
Journal of Systems and Software
Applying the cognitive flexibility theory to teaching web engineering
ACE '03 Proceedings of the fifth Australasian conference on Computing education - Volume 20
The New Review of Hypermedia and Multimedia - Hypermedia and the world wide web
Combining techniques to optimize effort predictions in software project management
Journal of Systems and Software
Reliability and Validity in Comparative Studies of Software Prediction Models
IEEE Transactions on Software Engineering
A constrained regression technique for cocomo calibration
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Assessing the usability of a visual tool for the definition of e-learning processes
Journal of Visual Languages and Computing
A study of project selection and feature weighting for analogy based software cost estimation
Journal of Systems and Software
An application of Bayesian network for predicting object-oriented software maintainability
Information and Software Technology
Modeling the relationship between software effort and size using deming regression
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
A survey in the area of machine learning and its application for software quality prediction
ACM SIGSOFT Software Engineering Notes
Prediction of faults-slip-through in large software projects: an empirical evaluation
Software Quality Control
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The goal of this study was to investigate the efficacy of different data analysis techniques for software data. We used simulation to create datasets with a known underlying model and with non-Normal characteristics that are frequently found in software datasets: skewness, unstable variance, and outliers and combinations of these characteristics.We investigated three main statistically-based data analysis techniques: Residual Analysis; Multivariate regression; Classification and Regression Trees (CART). In addition to the standard 'Least Squares' version of the technique, we also investigated robust and non-parametric versions of the techniques.We found that standard multivariate regression techniques were best if the data only exhibited skewness. However, under more extreme conditions such as severe heteroscedasticity, the non-parametric residual analysis technique performed best.We also found that even when the analysis technique did not accurately recreate the true underlying model, the faulty model could generate reasonably good predictions. This study indicates that simulation is very useful technique for evaluating different data analysis techniques.