Visual analysis of spatiotemporal density distributions is crucial for understanding spatiotemporal dynamics. However, existing methods suffer from visual occlusion and information loss when simultaneously displaying multiple density distributions. We present Strunkmap as an abstract approach to address these challenges. We introduce anisotropic kernel density estimation to enhance the accuracy of density generation. We extract the trunks of density distributions to identify the overall spatial patterns. Path scanning and trunk-outline matching strategies are employed to preserve local spatial structure. We design a stacked trunk plot that enables lossless density representation while conserving substantial screen space. Based on the visual design, Strunkmap integrates multiple heatmaps within a single map to effectively display temporal evolution of density distributions without visual occlusion. Ablation studies and comparative experiments validate the superiority of Strunkmap in accuracy and efficiency for hotspot identification and trend exploration. Theoretical analysis demonstrates Strunkmap's scalability, which we further verify through large-scale spatiotemporal data visualization. Color encoding schemes and scaling ratios are discussed to illustrate the flexibility. Our evaluations with user feedback demonstrate that Strunkmap is a viable solution with significant potential to real-world applications.

Modern code review is essential for software quality, but the complexity of codebases and time demands of manual reviews drive interest in automation for greater efficiency and consistency.However, current automated methods often fail to generate meaningful review comments and lack explainability, limiting developers' understanding and trust.This paper presents EXE-Reviewer, aimed at generating more EXplainable and Effective review comments.To enhance effectiveness, we integrate focus information into an existing model to improve its ability to extract key insights, thereby elevating comment quality.To improve explainability, we connect explanatory information (justification behind solutions) to causality, utilizing causality extraction techniques and introducing an explanatory loss.Furthermore, we devise two metrics to assess the quantity and quality of explanatory content, enhancing insight into the model's explanations.We compare EXE-Reviewer to state-ofthe-art methods in terms of effectiveness and explainability of the generated review comments.Experimental results show that EXE-Reviewer achieves a BLEU-4 score of 7.36%, surpassing the state-of-the-art baseline of 18.52%.Meanwhile, both explainability metrics and empirical study demonstrate notable improvements in explainability of the review comments generated by EXE-Reviewer, highlighting the effectiveness of our approach in generating accurate and comprehensible review comments to developers.

A criminal community is an interior group where individuals commit criminal activities with high intention. Therefore, the detection is of great importance to prevent potential crimes early in the stage. Prior studies focused on methods in modularity or network analysis based on topology. These approaches, however, do not work well for detecting minority communities, which is also a key issue in criminal detection. The main reasons are: 1) modularity-based approach cannot identify the inside community structure due to the resolution limit, 2) topology-based network analysis cannot fully leverage personal feature information, such as the amount and frequency of criminal transactions. To address these problems, this paper proposes a novel framework named DccGraph (Detect criminal communities using a Graph neural network) to enhance the overall performance of detecting criminal communities, especially minority ones. First, we extract the feature information of criminals and balance the distribution of criminal community members to construct an Augmented Criminal Network(ACN), which alleviates representation collapse and distinguish the feature of criminals in minority communities. In that case, it is capable to go beyond the resolution limit and locate minority communities effectively. Then, we design a criminal-oriented siamese graph encoder to capture both structural and feature information of criminals in the ACN. Specifically, feature interference and connection disturbance of criminals are employed to enrich the feature representation. To the best of our knowledge, DccGraph is the first framework to apply a graph neural network on criminal community detection. Experiments on several real-life dataset and benchmark datasets show that: DccGraph successfully outperforms eight baselines by 29.25%, 48.04%, 35.37%, and 40.98% on ACC, NMI, ARI, and F1, respectively. The dataset and the code for this framework are publicly available.

Method names are crucial to program comprehension and maintenance. Recently, many approaches have been proposed to automatically recommend method names and detect inconsistent names. Despite promising, their results are still suboptimal considering the three following drawbacks: 1) These models are mostly trained from scratch, learning two different objectives simultaneously. The misalignment between two objectives will negatively affect training efficiency and model performance. 2) The enclosing class context is not fully exploited, making it difficult to learn the abstract functionality of the method. 3) Current method name consistency checking methods follow a generate-then-compare process, which restricts the accuracy as they highly rely on the quality of generated names and face difficulty measuring the semantic consistency.In this paper, we propose an approach named AUMENA to AUtomate MEthod NAming tasks with context-aware prompt-tuning. Unlike existing deep learning based approaches, our model first learns the contextualized representation(i.e., class attributes) of programming language and natural language through the pre-training model, then fully exploits the capacity and knowledge of large language model with prompt-tuning to precisely detect inconsistent method names and recommend more accurate names. To better identify semantically consistent names, we model the method name consistency checking task as a two-class classification problem, avoiding the limitation of previous generate-then-compare consistency checking approaches. Experiment results reflect that AUMENA scores 68.6%, 72.0%, 73.6%, 84.7% on four datasets of method name recommendation, surpassing the state-of-the-art baseline by 8.5%, 18.4%, 11.0%, 12.0%, respectively. And our approach scores 80.8% accuracy on method name consistency checking, reaching an 5.5% outperformance. All data and trained models are publicly available.