Software engineering metrics and models
Software engineering metrics and models
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models
IEEE Transactions on Software Engineering
A replicated assessment and comparison of common software cost modeling techniques
Proceedings of the 22nd international conference on Software engineering
A Simulation Tool for Efficient Analogy Based Cost Estimation
Empirical Software Engineering
Human Performance Estimating with Analogy and Regression Models: An Empirical Validation
METRICS '98 Proceedings of the 5th International Symposium on Software Metrics
Using Public Domain Metrics To Estimate Software Development Effort
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
An Empirical Validation of the Relationship Between the Magnitude of Relative Error and Project Size
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
A Simulation Study of the Model Evaluation Criterion MMRE
IEEE Transactions on Software Engineering
Further Comparison of Cross-Company and Within-Company Effort Estimation Models for Web Applications
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Regression error characteristic surfaces
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Improving analogy-based software cost estimation by a resampling method
Information and Software Technology
Cross-company vs. single-company web effort models using the Tukutuku database: An extended study
Journal of Systems and Software
Comparing cost prediction models by resampling techniques
Journal of Systems and Software
Comparing Software Cost Prediction Models by a Visualization Tool
SEAA '08 Proceedings of the 2008 34th Euromicro Conference Software Engineering and Advanced Applications
Why comparative effort prediction studies may be invalid
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
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
Empirical Software Engineering
StatREC: a graphical user interface tool for visual hypothesis testing of cost prediction models
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
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The well-balanced management of a software project is a critical task accomplished at the early stages of the development process. Due to this requirement, a wide variety of prediction methods has been introduced in order to identify the best strategy for software cost estimation. The selection of the best technique is usually based on measures of error whereas in more recent studies researchers use formal statistical procedures. The former approach can lead to unstable and erroneous results due to the existence of outlying points whereas the latter cannot be easily presented to non-experts and has to be carried out by an expert with statistical background. In this paper, we introduce the regression error characteristic (REC) analysis, a powerful visualization tool with interesting geometrical properties, in order to validate and compare different prediction models easily, by a simple inspection of a graph. Moreover, we propose a formal framework covering different aspects of the estimation process such as the calibration of the prediction methodology, the identification of factors that affect the error, the investigation of errors on certain ranges of the actual cost and the examination of the distribution of the cost for certain errors. Application of REC analysis to the ISBSG10 dataset for comparing estimation by analogy and linear regression illustrates the benefits and the significant information obtained.