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Retrieval-Augmented Fine-Tuning for Improving Retrieve-and-Edit Based Assertion Generation

IEEE Transactions on Software Engineering 2025年第05期51卷 1591-1614页

作者： Li, Hongyan Sun, Weifeng Yan, Meng Xu, Ling Li, Qiang Zhang, Xiaohong Zhang, Hongyu Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

Unit Testing is crucial in software development and maintenance, aiming to verify that the implemented functionality is consistent with the expected functionality. A unit test is composed of two parts: a test prefix, which drives the unit under test to a particular state, and a test assertion, which determines what the expected behavior is under that state. To reduce the effort of conducting unit tests manually, Yu et al. proposed an integrated approach (integration for short), combining information retrieval with a deep learning-based approach to generate assertions for test prefixes, and obtained promising results. In our previous work, we found that the overall performance of integration is mainly due to its success in retrieving assertions. Moreover, integration is limited to specific types of edit operations and struggles to understand the semantic differences between the retrieved focal-test (focal-test includes a test prefix and a unit under test) and the input focal-test. Based on these insights, we then proposed a retrieve-and-edit approach named EditAS to learn the assertion edit patterns to improve the effectiveness of assertion generation in our prior study. Despite being promising, we find that the effectiveness of EditAS can be further improved. Our analysis shows that: ① The editing ability of EditAS still has ample room for improvement. Its performance degrades as the edit distance between the retrieval assertion and ground truth increases. Specifically, the average accuracy of EditAS is 12.38% when the edit distance is greater than 5. ② EditAS lacks a fine-grained semantic understanding of both the retrieved focal-test and the input focal-test themselves, which leads to many inaccurate token modifications. In particular, an average of 25.57% of the incorrectly generated assertions that need to be modified are not modified, and an average of 6.45% of the assertions that match the ground truth are still modified. Thanks to pre-trained models employing

关键词： Software design

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Connected Autonomous Vehicle Control Considering Human Driving Aggressive Lane Change from A cyber-physical Perspective

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Zhe Zhao, Min Sun, Dihua Wang, Liuping Lin, Jingdong Cheng, Senlin School of Engineering MelbourneVIC3000 Australia Chongqing University School of Automation Chongqing 400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing 400044 China

The aggressive lane-changing behavior of humandriven vehicles (HVs) under mixed traffic has a negative impact on connected autonomous vehicles (CAVs). This behavior is frequently observed in the vicinity of signalized intersection. From the perspective of cyber-physical systems (CPS), the negative impact on driving safety can be eliminated through the control algorithm at the CPS micro-scale level;and the negative impact on traffic efficiency can be suppressed through the CAV decision-making strategy at the CPS meso-scale level. This paper introduces a two-stage method aimed at safeguarding the right of way for CAVs and optimizing traffic efficiency in the scenario. Additionally, A CAV safety controller is developed to effectively handle the challenges posed by HVs violating game rules through continued aggressive lane changes. Simulation experiments demonstrate that compared to conventional game algorithms, the proposed method enhances traffic flow, increases average speeds, and diminishes traffic congestion in this scenario. The designed controller can ensure the safety of CAV driving in the target lane. © 1967-2012 IEEE.

关键词： Traffic congestion

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Adaptive Hybrid FFT: A Novel Pipeline and Memory-Based Architecture for Radix-2k FFT in Large Size Processing

arXiv

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arXiv 2025年

作者： Zhao, Fangyu Xiao, Chunhua Wang, Zhiguo Du, Xiaohua Dong, Bo College of Computer Science Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China Sichuan Huacun Zhigu Technology Co. Ltd. Chengdu China

In the field of digital signal processing, the fast Fourier transform (FFT) is a fundamental algorithm, with its processors being implemented using either the pipelined architecture, well-known for high-throughput applications but weak in hardware utilization, or the memory-based architecture, designed for area-constrained scenarios but failing to meet stringent throughput requirements. Therefore, we propose an adaptive hybrid FFT, which leverages the strengths of both pipelined and memory-based architectures. In this paper, we propose an adaptive hybrid FFT processor that combines the advantages of both architectures, and it has the following features. First, a set of radix-2kmulti-path delay commutators (MDC) units are developed to support high-performance large-size processing. Second, a conflict-free memory access scheme is formulated to ensure a continuous data flow without data contention. Third, We demonstrate the existence of a series of bit-dimension permutations for reordering input data, satisfying the generalized constraints of variable-length, high-radix, and any level of parallelism for wide adaptivity. Furthermore, the proposed FFT processor has been implemented on a field-programmable gate array (FPGA). As a result, the proposed work outperforms conventional memory-based FFT processors by requiring fewer computation cycles. It achieves higher hardware utilization than pipelined FFT architectures, making it suitable for highly demanding applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Pipeline processing systems

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GAML: Geometry-Aware Mutual Learning for polyp segmentation

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Biomedical Signal Processing and Control 2025年 108卷

作者： Wang, Kun Lu, Yuting Liu, Jin Zhang, Xiaohong School of Computer and Information Anhui Polytechnic University Wuhu241000 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Accurate and automatic polyp segmentation plays a pivotal role in early colorectal cancer diagnosis. However, this task remains challenging due to (i) the diverse sizes and irregular shapes of colonic polyps, and (ii) the ambiguity in polyp boundaries with the surrounding mucosa. To overcome these challenges, we propose a novel and effective Geometry-Aware Mutual Learning (GAML) for precise polyp segmentation, incorporating the following steps. First, we design a region feature and boundary-wise extractor to mine the internal and external geometric features of the target. Secondly, we introduce a geometry-aware context aggregation module (GACA) to learn long-range dependencies between pixels in the external structure region and pixels within the object. Thirdly, we present a geometry-aware graph reasoning module (GAGR) that enhances and propagates features across different domains. This is accomplished by utilizing region and boundary features as nodes in the graph, leading to accurate segmentation along the boundaries. Finally, the network incorporates latent representations of internal and external geometric structures and is adaptively trained to recognize and segment polyp lesions. Experimental results on four benchmark datasets (e.g., Kvasir-SEG, CVC-ClinicDB, BKAI, and Kvasir-Sessile) demonstrate that our method outperforms current state-of-the-art methods regarding various evaluation metrics. The source code is availab.e at https://***/DLWK/GAML. © 2025 Elsevier Ltd

关键词： Image segmentation

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Unified output feedback based prescribed performance consensus tracking control of heterogeneous multi-agent systems

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Luo, Dahui Wang, Yujuan Lewis, Frank L. Song, Yongduan Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University The School of Automation Chongqing University Chongqing400044 China Star Institute for Intelligent Systems Chongqing400044 China Uta Research Institute University of Texas at Arlington Fort WorthTX76118 United States

This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems (HMASs) with inconsistent system structure, where the performance behavior is allowed to be different from that of each other. Both the heterogeneous system structures and the nonidentical performance requirements make the control problem much more challenging than that of MASs with identical structure and performance requirement. This is mainly due to the coupling effect of the system dynamics and performance restriction of each agent in the cooperative control action. The key to solve this problem is to introduce a dual-phase performance-guaranteed method, in which the consensus tracking error is decomposed into auxiliary tracking error and filter tracking error and then the whole performance control is decomposed into two phases. By confining the two errors respectively, the practical tracking error can be proved to be explicitly confined within an arbitrarily given performance envelope by merely adjusting the design parameters rather than modifying control structure. Moreover, the prescribed performance control (PPC) result is not only uniform with any initial conditions and design parameters, allowing it to be global, but also unifying both the global and semi-global result into one frame, distinguishing itself from most existing PPC works where either only global or only semi-global result is guaranteed. Finally, the effectiveness of the proposed control scheme is confirmed by the simulation conducted on a group of tunnel-diode circuits (TDC). © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Levees

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CMCache: An Adaptive Cross-Level Data Placement Method for Multi-Level Cache

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Zeng, Zhaoyang Tan, Yujuan Ma, Zhulin Li, Jiali Zhao, Sanle Liu, Duo Chen, Xianzhang Ren, Ao Chongqing University College of Computer Science Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing Universityof Posts and Telecommunications College of Software Engineering China Chongqing University College of Big Data and Software Engineering Chongqing China

Multi-level cache systems enhance I/O performance by optimizing data placement across various cache levels from a global perspective. However, existing methods often struggle to place data at the optimal cache level promptly due to their reliance on historical access patterns and inflexible placement strategies. These methods face two main challenges: (1) For already cached data with sufficient access history, existing approaches only optimize movement between adjacent cache levels, potentially delaying data arrival at its globally optimal cache level and leading to unnecessary bandwidth consumption and increased latency. (2) For newly entered data without access history, current methods cannot accurately predict their future hotness and simply place them at a fixed cache level (i.e. first or last level), overlooking future accesses of new data and potentially resulting in high cache miss rates or cache pollution. To address these issues, we propose CMCache, an adaptive cross-level data placement method for multi-level cache. CMCache applies distinct placement strategies for cached and new data to reach the optimal level timely, considering their different characteristics. It also logically divides cache space into two sections to manage cached and new data separately, dynamically adjusting section sizes based on access patterns. This approach significantly improves data placement efficiency, achieving up to an 89% reduction in miss rates and a 79% decrease in average response times compared to existing methods. © 2025 IEEE.

关键词： Cache memory

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Impulsive stabilization of a class of nonlinear system with bounded gain error

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Chinese Physics B 2014年第12期23卷 103-108页

作者：马铁东赵飞亚 Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University College of Automation Chongqing University

Considering mechanical limitation or device restriction in practical application, this paper investigates impulsive stabilization of nonlinear systems with impulsive gain error. Compared with the existing impulsive analytical approaches,the proposed impulsive control method is more practically applicable, which includes control gain error with an acceptable boundary. A sufficient criterion for global exponential stability of an impulsive control system is derived, which relaxes the condition for precise impulsive gain efficiently. The effectiveness of the proposed method is confirmed by theoretical analysis and numerical simulation based on Chua＇s circuit.

关键词： bounded gain error impulsive control exponential stability Chua's circuit

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Feedback control scheme of traffic jams based on the coupled map car-following model

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Chinese Physics B 2013年第9期22卷 203-208页

作者：周桐孙棣华赵敏李华民 Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University College of Automation Chongqing University

Based on the pioneering work of Konishi et al. [Phys. Rev. E （1999） 60 4000], a new feedback control scheme is presented to suppress traffic jams based on the coupled map car-following model under the open boundary condition. The effect of the safe headway on the traffic system is considered. According to the control theory, the condition under which traffic jams can be suppressed is analyzed. The results are compared with the previous results concerning congestion control. The simulations show that the suppression performance of our scheme on traffic jams is better than those of the previous schemes, although all the schemes can suppress traffic jams. The simulation results are consistent with theoretical analyses.

关键词： traffic jam coupled map car-following model control scheme safe headway

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and service Management 2025年

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map

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Chinese Physics B 2015年第6期24卷 198-206页

作者：肖迪蔡洪坤郑洪英 Key Laboratory of Dependable Service Computing in Cyber Physical Society(Chongqing University) of Chinese Ministry of Education College of Computer ScienceChongqing University

In this paper, a compressive sensing （CS） and chaotic map-based joint image encryption and watermarking algorithm is proposed. The transform domain coefficients of the original image are scrambled by Arnold map firstly. Then the watermark is adhered to the scrambled data. By compressive sensing, a set of watermarked measurements is obtained as the watermarked cipher image. In this algorithm, watermark embedding and data compression can be performed without knowing the original image; similarly, watermark extraction will not interfere with decryption. Due to the characteristics of CS, this algorithm features compressible cipher image size, flexible watermark capacity, and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss. Simulation results and analyses show that the algorithm achieves good performance in the sense of security, watermark capacity, extraction accuracy, reconstruction, robustness, etc.

关键词： joint encryption and watermarking compressive sensing chaos Arnold map

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