Software engineering metrics and models
Software engineering metrics and models
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
An Empirical Study of Analogy-based Software Effort Estimation
Empirical Software Engineering
A Comparative Study of Cost Estimation Models for Web Hypermedia Applications
Empirical Software Engineering
Case-Based Reasoning with Confidence
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Cost estimation for web applications
Proceedings of the 25th International Conference on Software Engineering
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Using Grey Relational Analysis to Predict Software Effort with Small Data Sets
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
A flexible method for software effort estimation by analogy
Empirical Software Engineering
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Estimation by analogy EBA (effort estimation by analogy) is one of the proven methods for effort prediction in software engineering; in AI this would be called Case-Based Reasoning. In this paper we consider effort predictions using the EBA () method AQUA and pay attention to two aspects: (i) The influence of the set of analogs on the quality of prediction. The set of analogs is determined by a learning process incorporating the number of nearest neighbors and the threshold of the similarity measure used, (ii) Analyzing and understanding the conditions under which the prediction can be expected to be the most or the least accurate.We study two types of learning: One for finding the "best" set of analogs, and one for finding out factors for reliability. While both questions are relevant for different areas and disciplines, the focus of the paper is on estimation of effort in software engineering. For EBA method AQUA, the cases can be features or past projects characterized by attributes of various type. Classical estimation approaches just investigate the overall estimated quality of a system. However, in that case information is missing if and why estimation was performing the way it did. Bad estimates are often due to external influences. Therefore it is valuable for to find out under which conditions the estimates are more or less reliable.