Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
An investigation of machine learning based prediction systems
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
Software Engineering Economics
Software Engineering Economics
Machine Learning
A Simulation Tool for Efficient Analogy Based Cost Estimation
Empirical Software Engineering
Estimating Software Project Effort by Analogy Based on Linguistic Values
METRICS '02 Proceedings of the 8th 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
IEEE Transactions on Software Engineering
APSEC '07 Proceedings of the 14th Asia-Pacific Software Engineering Conference
Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation
IEEE Transactions on Software Engineering
Fuzzy grey relational analysis for software effort estimation
Empirical Software Engineering
Empirical Software Engineering
Analogy-based software effort estimation using Fuzzy numbers
Journal of Systems and Software
Predicting software project effort: A grey relational analysis based method
Expert Systems with Applications: An International Journal
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Fairly accurate cost and effort predictions of software projects have always been a challenging goal for both, industry as well as academia. Most approaches for effort estimation are either algorithmic or analogy based. The most well-known algorithmic models are COCOMO 81 [1] and Function Points [2]. Estimation by analogy, on the other hand, is essentially a form of case based reasoning. Fuzzy logic, Grey System Theory, Machine Learning techniques such as Genetic Algorithms, Support vector Machines, etc. have been used to optimize the prediction by analogy method. We study these analogy based approaches to software effort estimation and compare some of these techniques on the basis of widely used measures of accuracy.