Behavioral bugs caused by incorrect state changes are particularly challenging to identify because they depend on specific code execution paths. While code property graph (CPG) combine multiple code views through abstract syntax trees (AST), their built-in redundancy from syntax details and fixed connection rules make them hard to scale-a major problem when analyzing large software systems. We introduce QVoG, a new framework that improves CPG by combining graphbased code analysis with state behavior checking. Our main innovation lies in simplifying the CPG at the statement level by consolidating control and data flows into meaningful code blocks and optimizing the edges. This approach reduces the graph size by more than 10 times compared to AST-based methods while maintaining accuracy. This lightweight design allows easy integration of state tracking, where we match object lifecycle rules to simplified CPG connections using replaceable patterns. The combination of streamlined graphs and state-aware analysis helps QVoG effectively find difficult-to-identify behavioral bugs, successfully detecting 25 issues (including 17 confirmed cases and 2 official CVE) in real-world projects. Importantly, QVoG analyzes raw source code without requiring compilation and supports projects exceeding 1 million lines of code.

To achieve obstacle avoidance for mobile robots in environments with unknown static and dynamic obstacles, and considering the experience of pedestrians, the robot needs to acquire the features of both static and dynamic obstacles. Current advanced methods focus on obstacle avoidance effects but lack spatio-temporal modeling of dynamic information, thus failing to perform efficient obstacle avoidance that distinguishes between static and dynamic obstacles. This study proposes a method within the framework of deep reinforcement learning, where the dynamic feature-aware module is combined with temporal reasoning to effectively extract the spatio-temporal dynamic information around the robot, thereby achieving precise obstacle avoidance for both static and dynamic obstacles. Additionally, the robot can determine the non-intrusive areas for pedestrians based on spatio-temporal information, realizing safe and socially acceptable robots. We designed a large number of simulation experiments to compare our method with the currently advanced methods, fully verifying the obstacle avoidance performance and the improvement of pedestrian comfort experience of our method. Moreover, through robustness experiments and qualitative analysis of trajectories, the stability and dynamic information discrimination ability of the proposed method were further verified.

Somatic variants play a crucial role in the occurrence and progression of cancer. However, in the absence of matched normal controls, distinguishing between germline and somatic variants becomes challenging in tumor samples. The existing tumor-only genomic analysis methods either suffer from limited performance or insufficient interpretability due to an excess of features. Therefore, there is an urgent need for an alternative approach that can address these issues and have practical implications. Here, we presented OncoTOP, a computational method for genomic analysis without matched normal samples, which can accurately distinguish somatic mutations from germline variants. Reference sample analysis revealed a 0% false positive rate and 99.7% reproducibility for variant calling. Assessing 2864 tumor samples across 18 cancer types yielded a 99.8% overall positive percent agreement and a 99.9% positive predictive value. OncoTOP can also accurately detect clinically actionable variants and subclonal mutations associated with drug resistance. For the prediction of mutation origins, the positive percent agreement stood at 97.4% for predicting somatic mutations and 95.7% for germline mutations. High consistency of tumor mutational burden (TMB) was observed between the results generated by OncoTOP and tumor-normal paired analysis. In a cohort of 97 lung cancer patients treated with immunotherapy, TMB-high patients had prolonged PFS (P = .02), proving the reliability of our approach in estimating TMB to predict therapy response. Furthermore, microsatellite instability status showed a strong concordance (97%) with polymerase chain reaction results, and leukocyte antigens class I subtypes and homozygosity achieved an impressive concordance rate of 99.3% and 99.9% respectively, compared to its tumor-normal paired analysis. Thus, OncoTOP exhibited high reliability in variant calling, mutation origin prediction, and biomarker estimation. Its application will promise substantial advantages for clinical genomic testing.

Deep Learning (DL) has been widely applied in various fields. Unlike traditional software, DL programs possess the “black box” characteristic that can make it challenging for developers to debug when anomalous behaviors arise. In particular, silent bugs, a type of bugs in DL programs, can lead to erroneous behaviors without causing system crashes or suspensions, and they do not display error messages to users. This makes silent bugs more difficult for developers to discover, locate, and fix. In this paper, we present the first detailed study of silent bugs in PyTorch programs. We collect 14,523 posts from the official PyTorch forum and use a LLM-based semi-automated approach to filter the silent bugs. By analyzing the symptoms, root causes, and patterns of silent bugs, we have derived several important findings and implications: (1) most silent bugs cause abnormal outputs, which requires the design of more flexible test oracles to detect them, (2) the wide range of symptoms and root causes do not necessarily have one-to-one correspondences, which makes detecting and debugging silent bugs more challenging, (3) silent bugs exhibit common bug patterns, such as redundant, missing, or misplaced operations. Building upon these findings, we design and implement an extensible rule-based tool PYSIASSIST to help developer debug and resolve silent bugs. Evaluation results show that Pysiassist achieves 92.4% precision and 85.3% recall, outperforming existing techniques.