Software engineering metrics and models
Software engineering metrics and models
An empirical validation of software cost estimation models
Communications of the ACM
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Estimating Software Project Effort Using Analogies
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 Engineering Economics
Software Engineering Economics
Software Engineering: A European Perspective
Software Engineering: A European Perspective
A comparative study of attribute weighting heuristics for effort estimation by analogy
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
A flexible method for software effort estimation by analogy
Empirical Software Engineering
Decision Support Analysis for Software Effort Estimation by Analogy
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Comparison of weighted grey relational analysis for software effort estimation
Software Quality Control
Alternative methods using similarities in software effort estimation
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Hi-index | 0.00 |
Effort estimation is a key step of any software project. This paper presents a method to estimate project effort using an improved version of analogy. Unlike estimation methods based on case-based reasoning, our method makes use of two nearest neighbors of the target project for estimation. An additional refinement based on the relative location of the target project is then applied to generate the effort estimate. We first identify the relationships between cost drivers and project effort, and then determine the number of past project data that should be used in the estimation to provide the best result. Our method is then applied to a set of maintenance projects. Based on a comparison of the estimation results from our estimation method and those of other estimation methods, we conclude that our method can provide more accurate results.