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On the Value of Ensemble Effort Estimation
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 estimationExpand
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Exploiting the Essential Assumptions of Analogy-Based Effort Estimation
Background: There are too many design options for software effort estimators. How can we best explore them all? Aim: We seek aspects on general principles of effort estimation that can guide theExpand
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Finding conclusion stability for selecting the best effort predictor in software effort estimation
AbstractBackground:Conclusion Instability in software effort estimation (SEE) refers to the inconsistent results produced by a diversity of predictors using different datasets. This is largely due toExpand
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Transfer learning in effort estimation
When projects lack sufficient local data to make predictions, they try to transfer information from other projects. How can we best support this process? In the field of software engineering,Expand
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Combining Multiple Learners Induced on Multiple Datasets for Software Effort Prediction
Background: First approaches in software effort prediction depended on regression based models, whereas later models investigated more sophisticated methods like machine learning algorithms.Expand
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Software effort models should be assessed via leave-one-out validation
Context: More than half the literature on software effort estimation (SEE) focuses on model comparisons. Each of those requires a sampling method (SM) to generate the train and test sets. DifferentExpand
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When to use data from other projects for effort estimation
Collecting the data required for quality prediction within a development team is time-consuming and expensive. An alternative to make predictions using data that crosses from other projects or evenExpand
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Active learning and effort estimation: Finding the essential content of software effort estimation data
Background: Do we always need complex methods for software effort estimation (SEE)? Aim: To characterize the essential content of SEE data, i.e., the least number of features and instances requiredExpand
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How to Find Relevant Data for Effort Estimation?
Background: Building effort estimators requires the training data. How can we find that data? It is tempting to cross the boundaries of development type, location, language, application and hardwareExpand
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Defect Prediction between Software Versions with Active Learning and Dimensionality Reduction
Accurate detection of defects prior to product release helps software engineers focus verification activities on defect prone modules, thus improving the effectiveness of software development. AExpand
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