In recent years, researchers have proposed various approaches to detect code comments that explicitly acknowledge Technical Debt (TD), which are referred to as Self-Admitted Technical Debt (SATD) comments. Previous studies have proven that hidden patterns can be learned from SATD code snippets to predict whether the code snippets hold TD without the aid of comments. In this study, we refer to such TD as unadmitted TD, i.e., TD whose code snippets exhibit patterns similar to those of SATD, but are not annotated with comments indicating the existence of TD. Given that current unadmitted TD datasets are limited to method-level and conditional-statement-level code snippets, we construct the world’s most comprehensive dataset of code snippets and their corresponding comments, which includes 18 popular Java open-source projects and covers code snippets at the file, class, method and block levels. Around this dataset, we have conducted four key research activities.Firstly, we propose an automated framework for data collection and annotation, which extracts commented code snippets of varying granularity from projects and assigns SATD labels using three state-of-the-art SATD detection approaches. Secondly, we conduct a rigorous evaluation process, including the validity test, reliability test and manual verification, to ensure the accuracy and consistency of the dataset before further analysis and utilization. Additionally, we propose a metric-based detection approach named LiteM that detects unadmitted TD solely based on code metrics. As for the real-world scenarios where training data is scarce, we further introduce LiteMC, which generates pseudo-labels for commented code snippets and then employs LiteM to train a model on these pseudo-labeled data, enabling the detection of unadmitted TD in uncommented code snippets. The experimental results demonstrate the effectiveness and efficiency of both LiteM and LiteMC. The dataset and the code are available athttps://github.com/HduDBSI/Dataset4TD.

As one of the most common code smells, feature envy reduces the cohesion of classes and increases the coupling between classes, thus leading to difficulty of software maintainability. Though many studies have made good achievements on feature envy detection, they often despise or even ignore the inherent calling relationships between methods, causing unimpressive detection efficiency. To address this problem, we propose a Graph Neural Network (GNN) based approach towards feature envy detection. We first collect code metrics and calling relationships, and then convert them to the form of a graph, where the node represents the code metrics of a method and the edge represents the calling relationship between methods. Particularly, considering the unbalance of positive and negative samples, we introduce a graph augmenter to obtain an enhanced graph. Finally, we feed the enhanced graph into a GNN model for training and predicting. We conducted extensive experiments on a dataset containing five open-source software projects. The result shows that our approach achieves 78.90% in terms of average F1-score, which is 37.98% absolutely higher than the best comparison approach. Besides, we propose a refactoring recommendation approach based on calling strength. It achieves 61.44% of accuracy, which is 5.13% absolutely higher than the best competitive. Our code and datasets are available at https://github.com/HduDBSI/Feature-Envy-Detection.

With the development of video and image processing technology, the field of video tampering forensics is facing enormous challenges. Specifically, as the fundamental basis of judicial forensics, passive forensics for object removal video forgery is particularly essential. To extract tampering traces in video more sufficiently, the author proposed a spatiotemporal trident network based on the spatial rich model (SRM) and 3D convolution (C3D), which provides three branches and can theoretically improve the detection and localization accuracy of tampered regions. Based on the spatiotemporal trident network, a temporal detector and a spatial locator were designed to detect and locate the tampered regions in the temporal and spatial domains of videos. For the temporal detector, 3D CNNs were employed in three branches as the encoders and a bidirectional long short-term memory (BiLSTM) as the decoder. For the spatial locator, a backbone network named C3D-ResNet12 was designed as the encoder of the three branches, and the region proposal networks (RPNs) were employed as the decoders in three branches. In addition, we optimized the loss functions of the above two algorithms based on focal loss and GIoU loss. The experimental results revealed the effectiveness of spatiotemporal detection and localization algorithms: for temporal forgery detection, the accuracy of the frame classification increased to 99+%; for spatial forgery localization, the successful localization rate of the tampered regions in forged frames reached 96+%, and the mean intersection over union of the located tampered regions and the real tampered regions reached 62+%.