Ultra-Wideband (UWB) indoor positioning systems suffer significant accuracy degradation in Non-Line-of-Sight (NLoS) conditions, where multipath distortions in the Channel Impulse Response (CIR) lead to biased range estimates. While deep learning (DL) models have shown potential in predicting such errors from CIR data, most prior studies focus on isolated architectures and static pipelines, without evaluating their broader integration into full positioning systems. This work presents a comparative study of three DL models—Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory networks (BiLSTM), and Transformer—for CIR-based range error prediction, integrated into both Weighted Least Squares (WLS) and Extended Kalman Filter (EKF) frameworks. In WLS, predicted errors are used as adaptive weights to suppress unreliable measurements; in EKF, they dynamically scale the measurement noise covariance for more accurate filtering. We evaluate all models across four realistic indoor environments using a public UWB dataset. Our results show that Transformer-EKF achieves the best performance, reducing mean positioning error to 0.59m in Environment 2 (severe NLoS/multipath) while maintaining real-time inference at 0.059 milliseconds per position. These findings establish a comprehensive benchmark for learning-based UWB positioning and demonstrate the value of fusing data-driven range correction with model-based tracking.

Developers continuously interact in crowd-sourced community-based question-answer (Q&A) sites. Reportedly, ∼ 30% of all software professionals visit the most popular Q&A site StackOverflow (SO) every day. Software engineering (SE) research studies are also increasingly using SO data. To find out the trend, implication, impact, and future research potential utilizing SO data, a systematic mapping study needs to be conducted. Following a rigorous reproducible mapping study approach, from 18 reputed SE journals and conferences, we collected 384 SO-based research articles and categorized them into 10 facets (i.e., themes). We found that SO contributes to 85% of SE research compared with popular Q&A sites such as Quora, and Reddit. We found that 18 SE domains directly benefited from SO data whereas Recommender Systems , and API Design and Evolution domains use SO data the most (15% and 16% of all SO-based research studies, respectively). API Design and Evolution , and Machine Learning with/for SE domains have consistent upward publication. Deep Learning Bug Analysis and Code Cloning research areas have the highest potential research impact recently. With the insights, recommendations, and facet-based categorized paper list from this mapping study, SE researchers can find out potential research areas according to their interest to utilize large-scale SO data.

In the past couple of decades, significant research efforts have been devoted to the prediction of software bugs (i.e., defects). In general, these works leverage a diverse set of metrics, tools, and techniques to predict which classes, methods, lines, or commits are buggy. However, most existing work in this domain treats all bugs the same, which is not the case in practice. The more severe the bugs the higher their consequences. Therefore, it is important for a defect prediction method to estimate the severity of the identified bugs, so that the higher severity ones get immediate attention. In this paper, we provide a quantitative and qualitative study on two popular datasets (Defects4J and Bugs.jar), using 10 common source code metrics, and two popular static analysis tools (SpotBugs and Infer) for analyzing their capability to predict defects and their severity. We studied 3,358 buggy methods with different severity labels from 19 Java open-source projects. Results show that although code metrics are useful in predicting buggy code (Lines of the Code, Maintainable Index, FanOut, and Effort metrics are the best), they cannot estimate the severity level of the bugs. In addition, we observed that static analysis tools have weak performance in both predicting bugs (F1 score range of 3.1%–7.1%) and their severity label (F1 score under 2%). We also manually studied the characteristics of the severe bugs to identify possible reasons behind the weak performance of code metrics and static analysis tools in estimating their severity. Also, our categorization shows that Security bugs have high severity in most cases while Edge/Boundary faults have low severity. Finally, we discuss the practical implications of the results and propose new directions for future research.