Instance-Based Learning Algorithms
Machine Learning
FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
Effort estimation using analogy
Proceedings of the 18th international conference on Software engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Software development cost estimation approaches – A survey
Annals of Software Engineering
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
Limiting the Dangers of Intuitive Decision Making
IEEE Software
A Comparative Study of Cost Estimation Models for Web Hypermedia Applications
Empirical Software Engineering
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
ENNA: software effort estimation using ensemble of neural networks with associative memory
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Special issue on repeatable results in software engineering prediction
Empirical Software Engineering
Local vs. global models for effort estimation and defect prediction
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Beyond data mining; towards "idea engineering"
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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Background: Most software effort estimation research focuses on methods that produce the most accurate models but very little focuses on methods of mapping those models to business needs. Aim: In our experience, once a manager knows a software effort estimate, their next question is how to change that estimate. We propose a combination of inference + visualization to let managers quickly discover the important changes to their project. Method: (1) We remove superfluous details from project data using dimensionality reduction, column reduction and feature reduction. (2) We visualize the reduced space of project data. In this reduced space, it is simple to see what project changes need to be taken, or avoided. Results: Standard software engineering effort estimation data sets in the PROMISE repository reduce to a handful of rows and just a few columns. Our experiments show that there is little information loss in this reduction: in 20 datasets from the PROMISE repository, we find that there is little performance difference between inference over all the data and inference over our reduced space. Conclusion: Managers can be offered a succinct representation of project data, within which it is simple to find critical the decisions that most impact project effort.