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Testing the Stationarity Assumption in Software Effort Estimation Datasets
Software effort estimation (SEE) models are typically developed based on...
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Optimizing Software Effort Estimation Models Using Firefly Algorithm
Software development effort estimation is considered a fundamental task ...
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Discovering patterns of correlation and similarities in software project data with the Circos visualization tool
Software cost estimation based on multivariate data from completed proje...
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Sustainable Research Software Hand-Over
Scientific software projects evolve rapidly in their initial development...
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The Cost and Benefits of Static Analysis During Development
Without quantitative data, deciding whether and how to use static analys...
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Sequential Model Optimization for Software Process Control
Many methods have been proposed to estimate how much effort is required ...
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A deep learning model for estimating story points
Although there has been substantial research in software analytics for e...
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Time-Aware Models for Software Effort Estimation
It seems logical to assert that the dynamic nature of software engineering practice would mean that software effort estimation (SEE) modelling should take into account project start and completion dates. That is, we should build models for future projects based only on data from completed projects; and we should prefer data from recent similar projects over data from older similar projects. Research in SEE modelling generally ignores these recommendations. In this study two different model development approaches that take project timing into account are applied to two publicly available datasets and the outcomes are compared to those drawn from three baseline (non-time-aware) models. Our results indicate: that it is feasible to build accurate effort estimation models using project timing information; that the models differ from those built without considering time, in terms of the parameters included and their weightings; and that there is no statistical significance difference as to which of the two model building approaches is superior in terms of accuracy.
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