Verifying hardware designs in embedded systems is crucial but often labor-intensive and time-consuming. While existing solutions have improved automation, they frequently rely on unrealistic assumptions. To address these challenges, we introduce a novel framework, UVLLM, which combines Large Language Models (LLMs) with the Universal Verification Methodology (UVM) to relax these assumptions. UVLLM significantly enhances the automation of testing and repairing error-prone Register Transfer Level (RTL) codes, a critical aspect of verification development. Unlike existing methods, UVLLM ensures that all errors are triggered during verification, achieving a syntax error fix rate of 86.99% and a functional error fix rate of 71.92% on our proposed benchmark. These results demonstrate a substantial improvement in verification efficiency. Additionally, our study highlights the current limitations of LLM applications, particularly their reliance on extensive training data. We emphasize the transformative potential of LLMs in hardware design verification and suggest promising directions for future research in AI-driven hardware design methodologies. The Repo. of dataset and code: https://github.com/SEU-ACAL/reproduce-UVLLM-DAC-25/.

Verification presents a major bottleneck in Integrated Circuit (IC) development, consuming nearly 70% of the total development effort. While the Universal Verification Methodology (UVM) is widely used in industry to improve verification efficiency through structured and reusable testbenches, constructing these testbenches and generating sufficient stimuli remain challenging. These challenges arise from the considerable manual coding effort required, repetitive manual execution of multiple EDA tools, and the need for in-depth domain expertise to navigate complex designs. Here, we present UVM2, an automated verification framework that leverages Large Language Models (LLMs) to generate UVM testbenches and iteratively refine them using coverage feedback, significantly reducing manual effort while maintaining rigorous verification standards. To evaluate UVM2, we introduce a benchmark suite comprising Register Transfer Level (RTL) designs of up to 1.6K lines of code. The results show that UVM2reduces testbench setup time by up to 38.82× compared to experienced engineers, and achieve average code and function coverage of 87.44% and 89.58%, outperforming state- of-the-art solutions by 20.96% and 23.51%, respectively.

The query efficiency of Spark SQL is significantly impacted by its configurations. Therefore, configuration tuning has drawn great attention, and various automatic configuration tuning methods have been proposed. However, existing methods suffer from two issues: (1) high tuning overhead: they need to repeatedly execute the workloads several times to obtain the training samples, which is time-consuming; and (2) low throughput: they need to occupy resources like CPU cores and memory for a long time, causing other Spark SQL workloads to wait, thereby reducing the overall system throughput. These issues impede the use of automatic configuration tuning methods in practical systems which have limited tuning budget and many concurrent workloads. To address these issues, this paper proposes aLow-Overhead andFlexible approach for Spark SQL configurationTuning, dubbedLOFTune. LOFTune reduces the tuning overhead via a sample-efficient optimization framework, which is proposed based on multi-task SQL representation learning and multi-armed bandit. Furthermore, LOFTune solves the low throughput issue with a recommendation-sampling-decoupled tuning framework. Extensive experiments validate the effectiveness of LOFTune. In the sampling-allowed case, LOFTune can save up to 90% of the workload runs comparing with the state-of-the-art methods. Besides, in the zero-sampling case, LOFTune can reduce up to 41.26% of latency.

Selecting a good execution plan can significantly improve the query efficiency of Spark SQL. Several machine learning-based techniques have been proposed to select good execution plans for DBMS, but none of them perform well on Spark SQL due to the following issues. (1) Limited compatibility with Spark SQL: these approaches rely on physical operator enumeration, while Spark SQL doesn't support it; (2) Unreliable cost estimation: they often select execution plans with poor performance due to inaccurate cost estimation; (3) Time-consuming plan enumeration: they take much time to generate a large number of candidate execution plans in Spark SQL. To overcome these issues, in this paper, we propose LEAP, the first learned query optimizer tailored for Spark SQL, which can be integrated seamlessly into Spark SQL and solves the compatibility issue. Also, to avoid the unreliable cost value estimation, LEAP selects execution plans with an estimation-free method, which directly performs comparisons between the plans. Furthermore, LEAP employs an efficient progressive plan enumeration algorithm with pruning techniques to find better plans with fewer enumerations. Extensive experiments on three public benchmarks show the effectiveness of LEAP. It reduces the end-to-end execution time of the native optimizer by up to 54% and other learned methods by up to 94%.

Community search on attributed graphs has been widely studied recently. Most earlier works aim to retrieve communities relevant to the query nodes QUand query keywords QW, and some recent works begin to focus on keyword-based attributed community search (KACS) with only query keywords QW, aiming to return a structural cohesive community with the highest score relevant to QW. However, these scores only consider the semantic similarity between user attributes and QWand neglect the semantic similarity between users in the community. Thus, we propose a new community model which considers both semantic similarities and uses triangle-connected k-truss to ensure structural cohesiveness, and study the top-r keyword-based attributed community search (rKACS) problem for a given QWto provide more candidates for users to choose the preferred communities. To find the top-r communities, we first propose the Basic algorithm, which gradually finds the communities with large scores through maximal clique enumerations. Then, we further propose an improved algorithm Incremental based on two novel optimization techniques, which can significantly reduce the search space and find the maximal cliques incrementally. Extensive experimental studies on four real-world datasets validated the effectiveness and efficiency of our methods.

Given a network with social and spatial information, cohesive group queries aim to find a group of strongly connected and closely co-located users. Most existing studies limit to finding groups with either the strongest social ties under certain spatial constraints or the minimum spatial distance under certain social constraints. It is difficult for users to decide which constraints they need to choose and how to prioritize the constraints to meet their real requirements since the social constraint and spatial constraint are different in nature. In this paper, we take a new approach to consider the constraints equally and study a skyline query. Specifically, given a road-social network consisting of a road network$G_r$and a location-based social network$G_s$, we aim to find a set of skyline cohesive groups, in which each group cannot be dominated by any other group in terms of social cohesiveness and spatial cohesiveness. The social cohesiveness is modeled by$(k, c)$-core/truss (a k-core/truss of size c), and the spatial cohesiveness is evaluated by the total travel cost of meeting point from group members. We provide exact solutions and highly efficient greedy solutions for this problem.

As an online, query-dependent variant of the well-known community detection problem, community search has been studied for years to find communities containing the query vertices. Along with the generation of graphs with rich attribute information, attributed community search has attracted increasing interest recently, aiming to select communities where vertices are cohesively connected and share homogeneous attributes. However, existing community models may include cut-edges/vertices and thus cannot well guarantee the strong connectivity required by a cohesive community. In this paper, we propose a new cohesive attributed community (CAC) model that can ensure both structure cohesiveness and attribute cohesiveness of communities. Specifically, for a query with vertex vq and keyword set S, we aim to find the cohesively connected communities containing vq with the most shared keywords in S. It is nontrivial as we need to explore all possible subsets of S to verify the existence of structure cohesive communities until we find the communities with the most common keywords. To tackle this problem, we make efforts in two aspects. The first is to reduce the candidate keyword subsets. We achieve this by exploring the anti-monotonicity and neighborhood-constraint properties of our CAC model so that we can filter out the unpromising keyword subsets. The second is to speed up the verification process for each candidate keyword subset. We propose two indexes TIndex and MTIndex to reduce the size of the candidate subgraph before the verification. Moreover, we derive two new properties based on these indexes to reduce the candidate keyword subsets further. We conducted extensive experimental studies on four real-world graphs and validated the effectiveness and efficiency of our approaches.