111,449 research outputs found
Comparison of Gaussian process modeling software
Gaussian process fitting, or kriging, is often used to create a model from a
set of data. Many available software packages do this, but we show that very
different results can be obtained from different packages even when using the
same data and model. We describe the parameterization, features, and
optimization used by eight different fitting packages that run on four
different platforms. We then compare these eight packages using various data
functions and data sets, revealing that there are stark differences between the
packages. In addition to comparing the prediction accuracy, the predictive
variance--which is important for evaluating precision of predictions and is
often used in stopping criteria--is also evaluated
Connecting Software Metrics across Versions to Predict Defects
Accurate software defect prediction could help software practitioners
allocate test resources to defect-prone modules effectively and efficiently. In
the last decades, much effort has been devoted to build accurate defect
prediction models, including developing quality defect predictors and modeling
techniques. However, current widely used defect predictors such as code metrics
and process metrics could not well describe how software modules change over
the project evolution, which we believe is important for defect prediction. In
order to deal with this problem, in this paper, we propose to use the
Historical Version Sequence of Metrics (HVSM) in continuous software versions
as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN),
a popular modeling technique, to take HVSM as the input to build software
prediction models. The experimental results show that, in most cases, the
proposed HVSM-based RNN model has a significantly better effort-aware ranking
effectiveness than the commonly used baseline models
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