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Bug localization plays an important role in software quality control. Many supervised machine learning models have been developed based on historical bug-fix information. Despite being successful, these methods often require sufficient historical data (i.e., labels), which is not always available especially for newly developed software projects. In response, cross-project bug localization techniques have recently emerged whose key idea is to transferring knowledge from label-rich source project to locate bugs in the target project. However, a major limitation of these existing techniques lies in that they fail to capture the specificity of each individual project, and are thus prone to negative transfer.To address this issue, we propose an adversarial transfer learning bug localization approach, focusing on only transferring the common characteristics (i.e., public information) across projects. Specifically, our approach (CooBa) learns the indicative public information from cross-project bug reports through a shared encoder, and extracts the private information from code files by an individual feature extractor for each project. CooBa further incorporates adversarial learning mechanism to ensure that public information shared between multiple projects could be effectively extracted. Extensive experiments on four large-scale real-world data sets demonstrate that the proposed CooBa significantly outperforms the state of the art techniques.

 

With the requirements of natural language applications, multi-task sequence labeling methods have some immediate benefits over the single-task sequence labeling methods. Recently, many state-of-the-art multi-task sequence labeling methods were proposed, while still many issues to be resolved including (C1) exploring a more general relationship between tasks, (C2) extracting the task-shared knowledge purely and (C3) merging the task-shared knowledge for each task appropriately. To address the above challenges, we propose MTAA , a symmetric multi-task sequence labeling model, which performs an arbitrary number of tasks simultaneously. Furthermore, MTAA extracts the shared knowledge among tasks by adversarial learning and integrates the proposed multi-representation fusion attention mechanism for merging feature representations. We evaluate MTAA on two widely used data sets: CoNLL2003 and OntoNotes5.0. Experimental results show that our proposed model outperforms the latest methods on the named entity recognition and the syntactic chunking task by a large margin, and achieves state-of-the-art results on the part-of-speech tagging task.