Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
A Procedure for Analyzing Unbalanced Datasets
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
Software Cost Estimation with Incomplete Data
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
An empirical study of maintenance and development estimation accuracy
Journal of Systems and Software
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Combining techniques to optimize effort predictions in software project management
Journal of Systems and Software
Dealing with Missing Software Project Data
METRICS '03 Proceedings of the 9th 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
Reliability and Validity in Comparative Studies of Software Prediction Models
IEEE Transactions on Software Engineering
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Cross-company and single-company effort models using the ISBSG database: a further replicated study
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
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
Replicating studies on cross- vs single-company effort models using the ISBSG Database
Empirical Software Engineering
Comparing cost prediction models by resampling techniques
Journal of Systems and Software
Data sets and data quality in software engineering
Proceedings of the 4th international workshop on Predictor models in software engineering
Confidence in software cost estimation results based on MMRE and PRED
Proceedings of the 4th international workshop on Predictor models in software engineering
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Comparing Software Cost Prediction Models by a Visualization Tool
SEAA '08 Proceedings of the 2008 34th Euromicro Conference Software Engineering and Advanced Applications
Recent methods for software effort estimation by analogy
ACM SIGSOFT Software Engineering Notes
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
A PSO-based model to increase the accuracy of software development effort estimation
Software Quality Control
LMES: A localized multi-estimator model to estimate software development effort
Engineering Applications of Artificial Intelligence
Grey relational effort analysis technique using robust regression methods for individual projects
International Journal of Computational Intelligence Studies
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The importance of Software Cost Estimation at the early stages of the development life cycle is clearly portrayed by the utilization of several models and methods, appeared so far in the literature. The researchers' interest has been focused on two well known techniques, namely the parametric Regression Analysis and the non-parametric Estimation by Analogy. Despite the several comparison studies, there seems to be a discrepancy in choosing the best prediction technique between them. In this paper, we introduce a semi-parametric technique, called LSEbA that achieves to combine the aforementioned methods retaining the advantages of both approaches. Furthermore, the proposed method is consistent with the mixed nature of Software Cost Estimation data and takes advantage of the whole pure information of the dataset even if there is a large amount of missing values. The paper analytically illustrates the process of building such a model and presents the experimentation on three representative datasets verifying the benefits of the proposed model in terms of accuracy, bias and spread. Comparisons of LSEbA with linear regression, estimation by analogy and a combination of them, based on the average of their outcomes are made through accuracy metrics, statistical tests and a graphical tool, the Regression Error Characteristic curves.