Unit testing is a foundational practice in software engineering, essential for verifying program correctness, ensuring reliability, and supporting long-term maintainability. Rust, as a systems programming language emphasizing safety and performance, has seen rapid adoption in critical software infrastructures. However, writing effective unit tests in Rust is challenging due to its strict type system, ownership model, and module structure. Existing Rust unit test generation approaches can be divided into three categories: search-based, fuzzing-based, and large language models (LLMs)-based methods. Search-based and fuzzing-based methods often suffer from poor readability and lack semantic alignment with developer intent. LLMs-based methods can generate human-like, context-aware test cases, showing promise in Rust test generation. Despite their promise, LLMs-based methods still have two challenges. (1) There is a lack of high-quality training data to align the focal methods and their unit tests. (2) Rust’s strict type system, ownership rules, and modular design introduce barriers for generating test cases with high compile success rate, execution success rate, and code coverage. To address these issues, we propose RustTest, an LLM-based framework for automated Rust unit test generation. RustTest includes three stages: dataset fine-tuning, rich context-aware prompting, and post-processing optimization. In the first stage, we fine-tune an LLM on the constructed high-quality dataset. In the second stage, we construct rich, context-aware prompts that combine semantic intent (e.g., natural language descriptions and input-output examples) with structural information (e.g., callers, callees, and module metadata). In the third stage, we apply post-processing optimization techniques that leverage compiler feedback and coverage analysis to refine generated test cases. Experimental results on 230 focal methods from 85 real-world Rust projects show that RustTest outperforms state-of-the-art baselines, achieving a $\mathbf{1 3 5. 1 \%}$ improvement in compilation success rate, a 345.9% increase in execution success rate, and notable gains in both line and branch coverage. These findings highlight the effectiveness and practicality of RustTest for high-quality, executable, and semantically meaningful Rust unit test generation.

ABSTRACT Patches can help fix security vulnerabilities and optimize software performance, thereby enhancing the quality and security of the software. Unfortunately, patches generated by automated program repair tools are not always correct, as they may introduce new bugs or fail to fully rectify the original issue. Various methods for evaluating patch correctness have been proposed. However, most methods face the challenge of capturing long‐distance dependencies in patch correctness evaluation, which leads to a decline in the predictive performance of the models. To address the challenge, this paper presents a method named Qamhaen to evaluate the correctness of patches generated by APR. Specifically, text embedding of bugs and patches component address the challenge of long‐distance dependencies across functions in patch correctness evaluation by using bug reports and patch descriptions as inputs instead of code snippets. BERT is employed for pretraining to capture these dependencies, followed by an additional multihead self‐attention mechanism for further feature extraction. Similarity evaluator component devises a similarity calculation to assess the effectiveness of patch descriptions in resolving issues outlined in bug reports. Comprehensive experiments are conducted on a dataset containing 9135 patches and a patch correctness assessment metric, and extensive experiments demonstrate that Qamhaen outperforms baseline methods in terms of overall performance across AUC, F1, +Recall, ‐Recall, and Precision. For example, compared to the baseline, Qamhaen achieves an F1 of 0.691, representing improvements of 24.2%, 22.1%, and 6.3% over the baseline methods, respectively.

Beidou geostationary earth orbit (GEO) satellite-based global navigation satellite system reflectometry (GNSS-R) technique has been developed for measuring surface deformation in a carrier-phase-based, cost-effective, and continuous manner. However, several improper assumptions in the original system and method, such as neglecting the slight GEO satellite movement during angle determination and deeming inter-channel phase difference as time-invariant, can introduce certain errors in estimating the surface deformation. In this work, these issues were analyzed and improved for more accurate estimations. Utilizing a Wilkinson power divider, the inter-channel phase error is estimated and calibrated with direct signal in the master channel. The GEO-motion phase error is theoretically modelled and compensated for based on precise ephemeris. A compensating algorithm of GNSS-R deformation retrieval technique utilizing Beidou GEO satellites is presented, by which encouraging results are yielded from field experiments emulating long-term monitoring. On artificial targets, the retrieval root mean square (RMSE) is better than 8 mm.