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PROFES '02 Proceedings of the 4th International Conference on Product Focused Software Process Improvement
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ACM SIGSOFT Software Engineering Notes
Group Processes in Software Effort Estimation
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Regression Models of Software Development Effort Estimation Accuracy and Bias
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Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
An analysis of data sets used to train and validate cost prediction systems
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FOSE '07 2007 Future of Software Engineering
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Information and Software Technology
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PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Column Pruning Beats Stratification in Effort Estimation
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
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Journal of Systems and Software
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Journal of Systems and Software
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Size doesn't matter?: on the value of software size features for effort estimation
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Journal of Systems and Software
The impact of parameter tuning on software effort estimation using learning machines
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Automated Software Engineering
ACM Transactions on Software Engineering and Methodology (TOSEM)
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This paper provides an extensive review of studies related to expert estimation of software development effort. The main goal and contribution of the review is to support the research on expert estimation, e.g., to ease other researcher's search for relevant expert estimation studies. In addition, we provide software practitioners with useful estimation guidelines, based on the research-based knowledge of expert estimation processes. The review results suggest that expert estimation is the most frequently applied estimation strategy for software projects, that there is no substantial evidence in favour of use of estimation models, and that there are situations where we can expect expert estimates to be more accurate than formal estimation models. The following 12 expert estimation ''best practice'' guidelines are evaluated through the review: (1) evaluate estimation accuracy, but avoid high evaluation pressure; (2) avoid conflicting estimation goals; (3) ask the estimators to justify and criticize their estimates; (4) avoid irrelevant and unreliable estimation information; (5) use documented data from previous development tasks; (6) find estimation experts with relevant domain background and good estimation records; (7) Estimate top-down and bottom-up, independently of each other; (8) use estimation checklists; (9) combine estimates from different experts and estimation strategies; (10) assess the uncertainty of the estimate; (11) provide feedback on estimation accuracy and development task relations; and, (12) provide estimation training opportunities. We found supporting evidence for all 12 estimation principles, and provide suggestions on how to implement them in software organizations.