As User Reviews (URs) of mobile Apps are proven to provide valuable feedback for maintaining and evolving applications, how to make full use of URs more efficiently in the release cycle of mobile Apps has become a widely concerned and researched topic in the Software Engineering (SE) community. In order to speed up the completion of coding work related to URs to shorten the release cycle as much as possible, the task of User Review-based code localization is proposed and studied in depth. However, due to the lack of large-scale ground truth dataset (i.e., truly related pairs), existing methods are all unsupervised learning-based. In order to light up supervised learning approaches, which are driven by large labeled datasets, for Review2Code, and to compare their performances with unsupervised learning-based methods, we first introduce a large-scale human-labeled ground truth dataset, including the annotation process and statistical analysis. Then, a benchmark consisting of two SOTA unsupervised learning-based and four supervised learning-based Review2Code methods is constructed based on this dataset. We believe that this paper can provide a basis for in-depth exploration of the supervised learning-based Review2Code solutions.

Generating person images has been a promising approach to enhance the input richness for re-identification (reID) tasks in recent works. A key challenge is that the generated data often contains noise, which is caused by identity inconsistency between the generated person and the original input and failure cases in generative adversarial networks (GAN). Directly training using generated images may greatly affect learning good feature embeddings, resulting in unsatisfactory reID performance. This work presents a two-stage framework that can generate high-quality person images and purify failure cases for reID training. Experimental results demonstrate that our proposed generative model can produce person images with superior appearance consistency comparing with other state-of-the-art methods. Furthermore, we show that our method yields a significant improvement in re-identification (reID) task on public datasets with insufficient training data.

A weakly-supervised framework is proposed that cannot only make class inference but also provides reasonable decision basis in bone X-ray images. We implement it in three stages progressively: (1) design a classification network and use positive class activation map (PCAM) for attention location; (2) generate masks from attention maps and lead the model to make classification prediction from the activation areas; (3) label lesions in very few images and guide the model to learn simultaneously. We test the proposed method on a bone X-ray dataset. Results show that it achieves significant improvements in lesion location.