Multi-criteria decision analysis for customization of estimation by analogy method AQUA+
Proceedings of the 4th international workshop on Predictor models in software engineering
Software effort estimation based on weighted fuzzy grey relational analysis
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
A study of the non-linear adjustment for analogy based software cost estimation
Empirical Software Engineering
Fuzzy grey relational analysis for software effort estimation
Empirical Software Engineering
Customization support for CBR-based defect prediction
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
How to treat timing information for software effort estimation?
Proceedings of the 2013 International Conference on Software and System Process
A PSO-based model to increase the accuracy of software development effort estimation
Software Quality Control
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Estimation by analogy (EBA) predicts effort for a new project by aggregating effort information of similar projects from a given historical data set. Existing research results have shown that a careful selection and weighting of attributes may improve the performance of the estimation methods. This paper continues along that research line and considers weighting of attributes in order to improve the estimation accuracy. More specifically, the impact of weighting (and selection) of attributes is studied as extensions to our former EBA method AQUA, which has shown promising results and also allows estimation in the case of data sets that have non-quantitative attributes and missing values. The new resulting method is called AQUA+. For attribute weighting, a qualitative analysis pre-step using rough set analysis (RSA) is performed. RSA is a proven machine learning technique for classification of objects. We exploit the RSA results in different ways and define four heuristics for attribute weighting. AQUA+ was evaluated in two ways: (1) comparison between AQUA+ and AQUA, along with the comparative analysis between the proposed four heuristics for AQUA+, (2) comparison of AQUA+ with other EBA methods. The main evaluation results are: (1) better estimation accuracy was obtained by AQUA+ compared to AQUA over all six data sets; and (2) AQUA+ obtained better results than, or very close to that of other EBA methods for the three data sets applied to all the EBA methods. In conclusion, the proposed attribute weighing method using RSA can improve the estimation accuracy of EBA method AQUA+ according to the empirical studies over six data sets. Testing more data sets is necessary to get results that are more statistical significant.