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

IEEE Transactions on Software Engineering 2025年第5期51卷 1591-1614页

作者： Li, Hongyan Sun, Weifeng Yan, Meng Xu, Ling Li, Qiang Zhang, Xiaohong Zhang, Hongyu Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing University School of Big Data and Software Engineering 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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Syntactic-Enhanced Multi-Task Learning Model for Aspect Sentiment Triplet Extraction

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Data Science and Engineering 2025年 1-17页

作者： Shang, Jiaxing Zhang, Yuxuan Zhong, Linyang Li, Ruiyuan College of Computer Science Chongqing University Chongqing 400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing 400044 China

Aspect sentiment triplet extraction (ASTE), which aims to extract aspect terms, opinion terms, and sentiment polarity from textual comments, is a crucial task in aspect-based sentiment analysis. Most existing approaches focus on leveraging contextual information while neglecting the effective utilization of syntactic structures within the text. To improve extraction performance, this paper proposes a syntax-enhanced multi-task learning model, SE-ASTE, which jointly extracts aspect sentiment triplets by incorporating syntax connections and dependency edge type information. Specifically, the ASTE task is decomposed into three sub-tasks: opinion entity extraction, relation detection, and sentiment extraction. To capture syntactic dependencies, we employ a graph convolutional network with an attention mechanism, which computes the importance of dependency edges and aggregates node information in a targeted manner to generate a syntax-enhanced contextual representation. Subsequently, a self-attention module is utilized to generate task-specific features, while a sentiment extraction module, based on a affine scorer, captures sentiment relationships between words. Experimental results on the ASTE-Data-V2 dataset demonstrate that SE-ASTE achieves an average improvement of 1.45% in the F1-score compared to baseline models, highlighting its effectiveness in aspect sentiment triplet extraction. © The Author(s) 2025.

关键词： Aspect sentiment triplet extraction Aspect-level sentiment analysis Deep learning Dependency syntax analysis Graph convolution network

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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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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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Dipole Antenna Arrays with Inherently High Port Isolation Levels

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Chen, Zhiyuan Lu, Dacheng Tang, Ming-Chun Hu, Kun-Zhi Li, Dajiang Liu, Heng Yi, Da Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control College of Automation Chongqing400065 China Chongqing University of Posts and Telecommunications Institute of Industrial Internet Chongqing China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education School of Microelectronics and Communication Engineering Chongqing400044 China

In this letter, the dipole antenna arrays with self-decoupling characteristics are developed. In comparison with the traditional electric dipole antennas, the developed dipole antenna can be considered as comprising an electric dipole and two magnetic dipoles while maintaining a relatively simple structural design. The multi-coupling cancellation between adjacent antenna elements was achieved to achieve high port isolation levels. To validate the effectiveness of the developed self-decoupling method, a 1×2 antenna array is simulated, fabricated, and measured. The results demonstrated port isolation level exceeds 25 dB over the entire operational bandwidth of 3.3-3.8 GHz, and a maximum isolation level of ⁓63 dB was achieved. Furthermore, a 1×6 antenna array is simulated to validate the extensibility of the developed self-decoupling method, where the isolation levels between all ports exceeds 25 dB across the entire operational bandwidth of 3.3-3.8 GHz. © 2002-2011 IEEE.

关键词： Structural dynamics

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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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Disentangling Long- and Short-term Preferences Based on Latent Commonalities Enhancement for Next Point-of-Interest Recommendation

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Expert Systems with Applications 2025年 284卷

作者： Zhang, Qing Huang, Xiao Zhang, Qi Zhou, Wei Wen, Junhao School of Bigdata and Software Engineering Chongqing University Daxuecheng South Rd. 55 Chongqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Moe Chongqing400044 China College of Computer and Information Science Southwest University Chongqing400715 China

Some studies based on the joint modeling of users’ long- and short-term preferences have achieved significant performance improvements in the next POI prediction task. However, these methods fail to fully explore the potential behavioral commonalities within the long- and short-term visit sequences, thus failing to effectively mitigate the sparsity issue in check-in data. Meanwhile, due to the lack of labeled data that clearly distinguishes general tastes from short-term interests, existing joint modeling approaches for long- and short-term preferences struggle to provide clear supervision and differentiation for target preferences, leading to potential entanglement between these two preferences and consequently affecting the accuracy of preference modeling. Based on these, we propose a novel Disentangling Long- and Short-term Preferences Based on Latent Commonalities Enhancement for Next POI Recommendation (DLSPRec) method to accurately distinguish the differences and commonalities between users’ general tastes and short-term preferences, thereby improving the accuracy of the next location recommendation. This is the first attempt to disentangle long- and short-term preferences in the field of next POI recommendation. Through tests on three real-world datasets, we validated the superiority and rationality of the DLSPRec method. Compared to the suboptimal baseline model, DLSPRec achieved an average performance improvement of 10.39%, 9.69% and 16.39% on the PHO, NYC, and SIN datasets, respectively. The code is available at https://***/IsZSS/DLSPRec. © 2025

关键词： Contrastive learning Next POI recommendation Preferences disentanglement

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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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Pushing The Limit of LLM Capacity for Text Classification 25

Pushing The Limit of LLM Capacity for Text Classification

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Companion Proceedings of the ACM on Web Conference 2025

作者： Yazhou Zhang Mengyao Wang Qiuchi Li Prayag Tiwari Jing Qin Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing Chongqing China and Tianjin University Tianjin Tianjin China Zhengzhou University of Light Industry Zhengzhou China Copenhagen University Copenhagen Denmark Halmstad University Halmstad Sweden The Hong Kong Polytechnic University Hong Kong Hong Kong

ISBN: (纸本)9798400713316

In this era of open-ended language modeling, where task boundaries are gradually fading, an urgent question emerges: have we made significant progress in text classification with the full benefit of LLMs? To answer this question, we propose RGPT, an adaptive boosting framework tailored to produce a specialized text classification LLM by recurrently ensembling a pool of base learners. The base learners are constructed by adaptively adjusting the distribution of training samples and iteratively fine-tuning LLMs with them. Such base learners are then ensembled to be a specialized text classification LLM, by recurrently incorporating the historical predictions from the previous learners. Through a comprehensive empirical comparison, we show that RGPT significantly outperforms 8 state-of-the-art (SoTA) PLMs and 7 SoTA LLMs on four benchmarks by 2.90% on average. Further evaluation experiments reveal a clear superiority of RGPT over average human classification performance.

关键词： boosting

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