An empirical validation of software cost estimation models
Communications of the ACM
Decision analysis : a Bayesian approach
Decision analysis : a Bayesian approach
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
Reliability of function points measurement: a field experiment
Communications of the ACM
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Towards an Ontology of software maintenance
Journal of Software Maintenance: Research and Practice
An investigation of machine learning based prediction systems
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
Software Cost Estimation with Incomplete Data
IEEE Transactions on Software Engineering
Software Engineering Economics
Software Engineering Economics
Estimates, Uncertainty, and Risk
IEEE Software
Software Development Cost Estimation Using Function Points
IEEE Transactions on Software Engineering
Towards a Framework for Software Measurement Validation
IEEE Transactions on Software Engineering
A Causal Model for Software Cost Estimating Error
IEEE Transactions on Software Engineering
Learning How to Improve Effort Estimation in Small Software Development Companies
COMPSAC '00 24th International Computer Software and Applications Conference
Human Performance Estimating with Analogy and Regression Models: An Empirical Validation
METRICS '98 Proceedings of the 5th International Symposium on Software Metrics
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
Can Results from Software Engineering Experiments be Safely Combined?
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
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A description and a theoretical discussion are given of an approach that it is proposed will improve client confidence in estimates produced using algorithmic software estimation methods, such as Function Point or COCOMO estimation. The approach utilises Bayes Theorem and Bayesian inference. The underlying theory has been successfully used in other arenas of subjective measurement to help improve measurement consistency. It is also proposed that the approach can be used to improve estimation consistency during the estimation of the effort required to develop software development artefacts (e.g. project effort, milestone effort, requirements changes). Software developers will also be able to measure uncertainty in their estimates of artefacts using Bayesian inference. Outsourcers can use the approach to provide statements for client companies about the confidence they have in their estimates. The statements of confidence can then be used to assist outsourcers and clients during project negotiations. Examples are provided to show how the method can be used to measure estimate uncertainty, how estimators can be supported during their estimation procedures, and what kinds of statement can be made to aid project negotiations.