A comparison of case-based reasoning approaches
Proceedings of the 11th international conference on World Wide Web
Comparing effort prediction models for web design and authoring using boxplots
ACSC '01 Proceedings of the 24th Australasian conference on Computer science
Analysing primary and lower order project success drivers
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Assessing Project Success Using Subjective Evaluation Factors
Software Quality Control
Measuring Effort Estimation Uncertainty to Improve Client Confidence
Software Quality Control
Learning How to Improve Effort Estimation in Small Software Development Companies
COMPSAC '00 24th International Computer Software and Applications Conference
Empirical Software Engineering
Do adaptation rules improve web cost estimation?
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
Combining techniques to optimize effort predictions in software project management
Journal of Systems and Software
Group Processes in Software Effort Estimation
Empirical Software Engineering
Regression Models of Software Development Effort Estimation Accuracy and Bias
Empirical Software Engineering
IEEE Transactions on Software Engineering
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
Software development risk and project performance measurement: Evidence in Korea
Journal of Systems and Software
Assessing the Reliability of a Human Estimator
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Journal of Systems and Software
Exploring case-based reasoning for web hypermedia project cost estimation
International Journal of Web Engineering and Technology
Using correlation and accuracy for identifying good estimators
Proceedings of the 4th international workshop on Predictor models in software engineering
Journal of Computational Methods in Sciences and Engineering - Selected papers from the International Conference on Computer Science,Software Engineering, Information Technology, e-Business, and Applications, 2003
Web Cost Estimation and Productivity Benchmarking
Software Engineering
Software effort estimation terminology: The tower of Babel
Information and Software Technology
Probabilistic estimation of software size and effort
Expert Systems with Applications: An International Journal
Selection of strategies in judgment-based effort estimation
Journal of Systems and Software
Information and Software Technology
Evaluation of three methods to predict project success: a case study
PROFES'05 Proceedings of the 6th international conference on Product Focused Software Process Improvement
Journal of Systems and Software
Software development cost estimation using similarity difference between software attributes
Proceedings of the 2013 International Conference on Information Systems and Design of Communication
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Estimation of project development effort is most often performed by expert judgment rather than by using an empirically derived model (although such may be used by the expert to assist their decision). One question that can be asked about these estimates is how stable are they with respect to characteristics of the development process and product? This stability can be assessed in relation to the degree to which the project has advanced over time, the type of module for which the estimate is being made, and the characteristics of that module.In this paper we examine a set of expert-derived estimates for the effort required to develop a collection of modules from a large health-care system. Statistical tests are used to identify relationships between the type (screen or report) and characteristics of modules and the likelihood of the associated development effort being under-estimated, approximately correct, or over-estimated. Distinct relationships are found that suggest that the estimation process being examined was not unbiased to such characteristics. This is a potentially useful finding in that it provides an opportunity for estimators to improve their prediction performance.