Software engineering metrics and models
Software engineering metrics and models
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
An investigation on the use of machine learned models for estimating correction costs
Proceedings of the 20th international conference on Software engineering
A comparison of case-based reasoning approaches
Proceedings of the 11th international conference on World Wide Web
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
An Empirical Study of Analogy-based Software Effort Estimation
Empirical Software Engineering
Web Development: Estimating Quick-to-Market Software
IEEE Software
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
Cost estimation for web applications
Proceedings of the 25th International Conference on Software Engineering
Using Simulation to Evaluate Prediction Techniques
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
A Replicated Assessment of the Use of Adaptation Rules to Improve Web Cost Estimation
ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
A Simulation Study of the Model Evaluation Criterion MMRE
IEEE Transactions on Software Engineering
Software project economics: a roadmap
FOSE '07 2007 Future of Software Engineering
A review of studies on expert estimation of software development effort
Journal of Systems and Software
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As web-based applications become more popular and more sophisticated, so does the requirement for early accurate estimates of the effort required to build such systems. Case-based reasoning (CBR) has been shown to be a reasonably effective estimation strategy, although it has not been widely explored in the context of web applications. This paper reports on a study carried out on a subset of the ISBSG dataset to examine the optimal number of analogies that should be used in making a prediction. The results show that it is not possible to select such a value with confidence, and that, in common with other findings in different domains, the effectiveness of CBR is hampered by other factors including the characteristics of the underlying dataset (such as the spread of data and presence of outliers) and the calculation employed to evaluate the distance function (in particular, the treatment of numeric and categorical data).