Software fault localization is widely known to be costly, which motivates researchers to develop automated root cause analysis techniques. While many existing approaches focus on pinpointing the location of bugs, they often overlook the process of locating them. However, in practice, while the exact location or fix of a bug can be subjective, the explanation of how a bug manifests is often more objective and broadly agreed upon. Such explanations can thus offer a stronger foundation for understanding and resolving bugs. In this work, we propose, TraceWalker, a tool-supported root cause analysis approach that generates a step-by-step debugging plan to guide developers toward the root cause with convincing explanations. Technically, we build the execution trace as a causal model where each step, control dependency, and data dependency is modeled with quantified suspiciousness. Based on the suspiciousness, TraceWalker traverses the execution trace to guide users toward the root cause. Specifically, each travel is on an estimated decision to explore control or data dependencies on the trace. While guiding the programmers to the root cause, TraceWalker collects their agreement on the estimated decision as feedback, which in turn updates the predicted root cause with a new planned travelling path. Our simulation experiments on Defects4j dataset show that (1) TraceWalker is effective in root-cause location comparing to state-of-the-art approaches, (2) TraceWalker can generate accurate explanation and save the debugging efforts to reach the root cause and (3) TraceWalker is robust against potential incorrect feedbacks. Moreover, our user study on 21 participants show that TraceWalker can boost the debugging performance with convincing explanation in practice.

Recent advances in large language models (LLMs) offer promising potential for automating formal methods. However, applying them to formal verification remains challenging due to the complexity of specification languages, the risk of hallucinated output, and the semantic gap between natural language and formal logic. We introduce PAT-Agent, an end-to-end framework for natural language autoformalization and formal model repair that combines the generative capabilities of LLMs with the rigor of formal verification to automate the construction of verifiable formal models. In PAT-Agent, a Planning LLM first extracts key modeling elements and generates a detailed plan using semantic prompts, which then guides a Code Generation LLM to synthesize syntactically correct and semantically faithful formal models. The resulting code is verified using the Process Anal y sis Toolkit (PAT) model checker against user-specified properties, and when discrepancies occur, a Repair Loop is triggered to iteratively correct the model using counterexamples. To improve flexibility, we built a web-based interface that enables users, particularly non-FM-experts, to describe, customize, and verify system behaviors through user-LLM interactions. Experimental results on 40 systems show that PAT-Agent consistently outperforms baselines, achieving high verification success with superior efficiency. The ablation studies confirm the importance of both planning and repair components, and the user study demonstrates that our interface is accessible and supports effective formal modeling, even for users with limited formal methods experience.

Timely and accurate acquisition of land surface information in urban area is essential in monitoring the process of urbanization, it is critical for urban planning and management. Aims to investigate urban land cover classification using high resolution remote sensing data, this study proposed an object-oriented image analysis based approach for urban land cover mapping using LiDAR and CCD images. The proposed approach based on the analytic hierarchy process, the optimal scale for image segmentation and methods of image classification are investigated. The main land cover types are determined as low built-up, high built-up, tree, grassland and road, with the multi-scale image segmentation, the corresponding feature spaces are adopted for fuzzy classification. The experiment was carried out in Zhangye city, Gansu province. The results show that the overall accuracy is 93.42, and the Kappa is 0.91.