Transfer defect learning. Abstract: Many software defect prediction approaches have been proposed and most are effective in within-project prediction settings.
Our transfer learning approaches significantly improve the performance of cross- project defect prediction in terms of F-measure [21] for both data sets.
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In this paper, we apply a state-of-the-art transfer learning approach, TCA, to make feature distributions in source and target projects similar. In addition, we ...
In this paper, we apply a state-of-the-art transfer learning approach, TCA, to make feature distributions in source and target projects similar. In addition, we ...
In this paper, we apply a state-of-the-art transfer learning approach, TCA, to make feature distributions in source and target projects similar.
A state-of-the-art transfer learning approach is applied to make feature distributions in source and target projects similar, and a novel transfer defect ...
Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a new related task, which is presently very common in deep ...
In this paper, we apply a state-of-the-art transfer learning approach, TCA, to make feature distributions in source and target projects similar.
Feb 26, 2023 · We, therefore, propose a transfer learning approach, namely TransferD2, to correctly identify defects on a dataset of source objects and extend its application ...
Mar 18, 2024 · Instance-based transfer learning involves directly transferring knowledge or models from one domain to another by reusing labeled data instances ...