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In software development, due to the demands from users or the limitations of time and resources, developers tend to adopt suboptimal solutions to achieve quick software development. In such a way, the released software usually involves not-quite-right code that is called technical debt, which will significantly decrease the quality of software and increase the maintenance cost. Recently, the concept of self-admitted technical debt (SATD) is proposed and refers to technical debt that is self-admitted by developers in code comments. Existing studies mainly focus on detecting technical debt by classifying code comments into either “SATD” or “non-SATD.” However, different types of SATD has different impacts on software maintenance and needs to be handled by different developers. Therefore, the detected SATD should be further classified so that developers can understand and remove technical debt better. In this article, we propose a new method based on eXtreme Gradient Boosting (XGBoost) to classify SATD into multiple classes. In our approach, we first preprocess the original code comments and adopt the easy data augmentation strategy to overcome the class unbalance problem. Then, chi-square is leveraged to select representative features from the textual feature set. Finally, we apply XGBoost to train a classifier and use the trained classifier to partition each comment into the corresponding class. We experimentally investigate the effectiveness of our approach on a public dataset, including 62 566 code comments from 10 open-source projects. Experimental results show that our approach achieves 56.66% in terms of macroaveraged precision, 59.07% in terms of macroaveraged recall, and 55.77% in terms of macroaveraged F-measure on average, and outperforms the natural language processing based method by 4.98%, 5.32%, and 3.17%, respectively. In addition, the experimental results also demonstrate that the data augmentation strategy is effective in improving the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

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+%. [ABSTRACT FROM AUTHOR]