Software engineering metrics and models
Software engineering metrics and models
A Discipline for Software Engineering
A Discipline for Software Engineering
An empirical study of maintenance and development estimation accuracy
Journal of Systems and Software
Using Prior-Phase Effort Records for Re-estimation During Software Projects
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
ISESE '04 Proceedings of the 2004 International Symposium on Empirical Software Engineering
An Empirical Analysis of Software Productivity over Time
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Optimal Project Feature Weights in Analogy-Based Cost Estimation: Improvement and Limitations
IEEE Transactions on Software Engineering
Using Multivariate Statistics (5th Edition)
Using Multivariate Statistics (5th Edition)
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Using genetic programming to improve software effort estimation based on general data sets
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering
Data accumulation and software effort prediction
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Replicated analyses of windowing approach with single company datasets
Proceedings of the 12th International Conference on Product Focused Software Development and Process Improvement
Can cross-company data improve performance in software effort estimation?
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
How to treat timing information for software effort estimation?
Proceedings of the 2013 International Conference on Software and System Process
The impact of parameter tuning on software effort estimation using learning machines
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
Building a second opinion: learning cross-company data
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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Models for estimating software development effort are commonly built and evaluated using a set of historical projects. An important question is which projects to use as training data to build the model: should it be all of them, or a subset that seems particularly relevant? One factor to consider is project age: is it best to use the entire history of past projects, or is it more appropriate in a rapidly changing world to use a window of recent projects? We investigate the effect on estimation accuracy of using a moving window, using projects from the ISBSG data set. We find that using a moving window can improve accuracy, and we make some observations about factors that influence the range of possible window sizes and the best window size.