![]() For example, researchers could use pre-trained models published by large IT companies like Google and Facebook and fine-tune the model via deep transfer learning to adapt to downstream tasks. Among the four methods, network-based deep transfer learning is generally accepted by researchers and has been practically used in many domains. Deep transfer learning can be organized into four categories: instance-based deep transfer learning, which uses instances in source domain by appropriate weights mapping-based deep transfer learning, which maps instances from two domains into a new data space with better similarity network-based deep transfer learning, which reuses the parts of the network pre-trained in the source domain and adversarial-based deep transfer learning, which uses adversarial learning to find transferable features that are both suitable for both domains. Can we use a model that “understands” source code for software defect prediction? How should we use such a model for software defect prediction?ĭeep transfer learning is a special form of transfer learning that uses a non-linear deep learning model for transfer learning. However, due to the limited dataset size in software defect prediction (compared to massive lines of source code directly available in open source repositories), it is hard to believe that a deep learning model trained for software defect prediction can really “understand” the source code itself. Many researchers use various kinds of deep learning models, e.g., Convolutional Neural Networks (CNN), Long-short Term Memory (LSTM) models, and Transformers for software defect prediction, and achieve promising results. Instead of using hand-crafted metrics that are designed top-down, deep learning models are able to generate code features bottom-up from source code and could describe both syntax and semantic information. The same trend also appears in software defect prediction because deep learning models are more capable of extracting information from long texts, i.e., source code. Since AlexNet, deep learning has been growing rapidly in image recognition, speech recognition, and natural language processing. ![]() For decades, hand-crafted metrics have been used in software defect prediction.
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