Software effort models should be assessed via leave-one-out validation
Journal of Systems and Software
Data science for software engineering
Proceedings of the 2013 International Conference on Software Engineering
The impact of parameter tuning on software effort estimation using learning machines
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
Beyond data mining; towards "idea engineering"
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
Software effort estimation as a multiobjective learning problem
ACM Transactions on Software Engineering and Methodology (TOSEM) - Testing, debugging, and error handling, formal methods, lifecycle concerns, evolution and maintenance
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Background: Despite decades of research, there is no consensus on which software effort estimation methods produce the most accurate models. Aim: Prior work has reported that, given M estimation methods, no single method consistently outperforms all others. Perhaps rather than recommending one estimation method as best, it is wiser to generate estimates from ensembles of multiple estimation methods. Method: Nine learners were combined with 10 preprocessing options to generate 9 \times 10 = 90 solo methods. These were applied to 20 datasets and evaluated using seven error measures. This identified the best n (in our case n=13) solo methods that showed stable performance across multiple datasets and error measures. The top 2, 4, 8, and 13 solo methods were then combined to generate 12 multimethods, which were then compared to the solo methods. Results: 1) The top 10 (out of 12) multimethods significantly outperformed all 90 solo methods. 2) The error rates of the multimethods were significantly less than the solo methods. 3) The ranking of the best multimethod was remarkably stable. Conclusion: While there is no best single effort estimation method, there exist best combinations of such effort estimation methods.