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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Yihao Qin Shangwen Wang Kui Liu Xiaoguang Mao Tegawendé F. Bissyandé National University of Defense Technology Changsha China Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China University of Luxembourg Luxembourg

ISBN: (纸本)9781728196305;9781728196312

The literature of Automated Program Repair is largely dominated by approaches that leverage test suites not only to expose bugs but also to validate the generated patches. Unfortunately, beyond the widely-discussed concern that test suites are an imperfect oracle because they can be incomplete, they can include tests that are flaky. A flaky test is one that can be passed or failed by a program in a non-deterministic way. Such tests are generally carefully removed from the repair benchmarks. In practice, however, flaky tests are available test suite of software repositories. To the best of our knowledge, no study has discussed this threat to validity for evaluation of program repair. In this work, we highlight this threat and further investigate the impact of flaky tests by reverting their removal from the Defects4J benchmark. Our study aims to characterize the impact of flaky tests for localizing bugs and the eventual influence on the repair performance. Among other insights, we find that (1) although flaky tests are few (≈0.3%) of total tests, they affect experiments related to a large proportion (98.9%) of Defects4J real-world faults; (2) most flaky tests (98%) actually provide deterministic results under specific environment configurations (with the jdk version influencing the results); (3) flaky tests drastically hinder the effectiveness of spectrum-based fault localization (e.g., the rankings of 90 bugs drop down while none of the bugs obtains better location results compared with results achieved without flaky tests); and (4) the repairability of APR tools is greatly affected by the presence of flaky tests (e.g., 10 state of the art APR tools can now fix significantly fewer bugs than when the benchmark is manually curated to remove flaky tests). Given that the detection of flaky tests is still nascent, we call for the program repair community to relax the artificial assumption that the test suite is free from flaky tests. One direction that we propose is to co

关键词： Location awareness Degradation Conferences Computer bugs Maintenance engineering Tools Benchmark testing

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Joint Partial Offloading and Resource Allocation for Distributed Multi-access Edge computing Enabled Vehicular Network

TechRxiv

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TechRxiv 2022年

作者： Ma, Guifu Hu, Manjiang Wang, Xiaowei Wu, Di Li, Haoran Bian, Yougang Zhu, Konglin The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Hunan University Wuxi214072 China The College of Design Hunan University Changsha410082 China The Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha410082 China The College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In the foreseeable Intelligent Transportation System (ITS), Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge computing (MEC) helps overcome this limitation effectively. In this paper, we investigate the vehicle and edge server collaborative computation offloading problem by jointly optimizing the partial offloading ratio and resource allocation ratio. Different from existing two-level centralized solution methods, we give a new perspective to deal with multi-variable non-convex optimization problem in a distributed manner. That is, by analyzing the feasibility conditions of the problem, we separate the coupling between variables and derive the bounds of partial offloading ratio and resource allocation ratio based on the constraints. We further separate the coupling variables among ICVs and transform the original problem into an equivalent form and decompose it into a two-level convex problem, where each sub-problem satisfies the task offloading requirements. We then develop two low-complexity distributed algorithms to obtain the optimal solution. Finally, extensive simulations demonstrate that our proposed algorithms can converge faster and significantly outperform the baseline schemes in terms of system cost. © 2022, CC BY.

关键词： Resource allocation

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SpTFS: Sparse Tensor Format Selection for MTTKRP via Deep Learning 20

SpTFS: Sparse Tensor Format Selection for MTTKRP via Deep Le...

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Supercomputing Conference

作者： Qingxiao Sun Yi Liu Ming Dun Hailong Yang Zhongzhi Luan Lin Gan Guangwen Yang Depei Qian Beihang University China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Tsinghua University China

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

Canonical polyadic decomposition (CPD) is one of the most common tensor computations adopted in many scientific applications. The major bottleneck of CPD is matricized tensor times Khatri-Rao product (MTTKRP). To optimize the performance of MTTKRP, various sparse tensor formats have been proposed such as CSF and HiCOO. However, due to the spatial complexity of the tensors, no single format fits all tensors. To address this problem, we propose SpTFS, a framework that automatically predicts the optimal storage format for an input sparse tensor. Specifically, SpTFS leverages a set of sampling methods to lower the sparse tensor to fix-sized matrices and specific features. Then, TnsNet combines CNN and the feature layer to accurately predict the optimal format. The experimental results show that SpTFS achieves prediction accuracy of 92.7% and 96% on CPU and GPU respectively.

关键词： Tensors High performance computing Neural networks Graphics processing units Sampling methods Sparse matrices Matrix decomposition

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An Efficient Rule Processing Architecture Based on Reconfigurable Hardware 1

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23rd Annual Conference on Computer engineering and Technology, NCCET 2019

作者： Chen, Mengdong Ren, Xiujiang Xie, Xianghui State Key Laboratory of Mathematical Engineering and Advanced Computing Henghua Science and Technology Park WuxiJiangsu214125 China

ISBN: (数字)9789811518508

ISBN: (纸本)9789811518492

In the recovery of security strings in identity authentication mechanism, the combination of dictionary and string transformation rules is an effective method. However, the processing of string transformation rules faces challenges such as performance and energy efficiency. The existing tools and researches are based on software. It’s difficult to meet the needs of actual recovery systems. In this paper, an efficient rule processing architecture based on reconfigurable hardware is proposed. The rules are processed using FPGA for the first time. A rule processor is designed and implemented on Xilinx Zynq XC7Z030 chip. The experimental results show that the performance of the rule processor is better than that of Intel i7-6700 CPU in typical cases. The performance power ratio of the rule processor is 1.4–2.1 times higher than that of NVIDIA GeForce GTX 1080 Ti GPU and 70 times higher than that of CPU, which effectively improves the speed and efficiency of rule processing. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Reconfigurable hardware

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Design and implementation of a domain specific rule engine 1

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22nd Annual Conference on Computer engineering and Technology, NCCET 2018

作者： Chen, Mengdong Zhou, Xinjian Wu, Dong Xie, Xianghui State Key Laboratory of Mathematical Engineering and Advanced Computing Henghua Science and Technology Park WuxiJiangsu214125 China

ISBN: (数字)9789811359194

ISBN: (纸本)9789811359187

Security strings are often needed in identity authentication mechanism. Security strings recovery is a reverse process, which does much calculations on a large amount of possible strings to find the right one, so that we can recover lost or forgotten strings and regain access to valuable information. In this reverse process, we need first process basic strings based on transformation rules, so as to generate new ones quickly. Rule processing is complex, which has high requirements for computing power, processing time, especially system power consumption. In response to the above requirements, this work puts forward the idea of accelerating the processing of rules using hardware for the first time, and a domain specific rule engine is designed and implemented on the existing FPGA platform. The experimental results show that the performance of the rule engine on a single Xilinx Zynq 7z030 FPGA is better than that of CPU, its performance power ratio is 3 times higher than that of GPU, and 50 times higher than that of CPU. The speed and energy efficiency of the rule processing is improved effectively. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Engines

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ZeroSpy: Exploring Software Inefficiency with Redundant Zeros 20

ZeroSpy: Exploring Software Inefficiency with Redundant Zero...

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Supercomputing Conference

作者： Xin You Hailong Yang Zhongzhi Luan Depei Qian Xu Liu Beihang University China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China North Carolina State University USA

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

Redundant zeros cause inefficiencies in which the zero values are loaded and computed repeatedly, resulting in unnecessary memory traffic and identity computation that waste memory bandwidth and CPU resources. optimizing compilers is difficult in eliminating these zero-related inefficiencies due to limitations in static analysis. Hardware approaches, in contrast, optimize inefficiencies without code modification, but are not widely adopted in commodity processors. In this paper, we propose ZeroSpy - a fine-grained profiler to identify redundant zeros caused by both inappropriate use of data structures and useless computation. ZeroSpy also provides intuitive optimization guidance by revealing the locations where the redundant zeros happen in source lines and calling contexts. The experimental results demonstrate ZeroSpy is capable of identifying redundant zeros in programs that have been highly optimized for years. Based on the optimization guidance revealed by ZeroSpy, we can achieve significant speedups after eliminating redundant zeros.

关键词： Optimizing compilers Memory management Redundancy Static analysis Production Software Optimization

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A survey of coverage-guided greybox fuzzing with deep neural models

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Information and Software Technology 2025年 186卷

作者： Junyang Qiu Yupeng Jiang Yuantian Miao Wei Luo Lei Pan Xi Zheng State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214123 China School of Computing Macquarie University Sydney 2109 NSW Australia School of Information and Physical Sciences University of Newcastle Newcastle 2308 NSW Australia School of Information Technology Deakin University Melbourne 3125 VIC Australia

Coverage-guided greybox fuzzing (CGF) has emerged as a powerful technique for software vulnerability detection, yet traditional techniques often struggle with the increasing complexity of modern software systems and the vastness of input spaces. Deep neural networks (DNNs) have begun to fundamentally transform CGF by addressing these limitations through automated feature extraction, adaptive input generation, and intelligent path prioritization. However, despite these advancements, critical gaps persist in understanding the state-of-the-art landscape. Existing studies often lack rigorous benchmarks to evaluate scalability and generalizability, fail to address the interpretability of neural-guided decisions, and overlook the integration of emerging paradigms such as large language models (LLMs) and neurosymbolic reasoning. This survey systematically bridges these gaps by providing a comprehensive taxonomy of DNN-driven CGF techniques, analyzing their strengths and limitations across key fuzzing stages—seed generation, selection, and mutation. We find that although DNNs have significantly improved fuzzing efficiency, challenges such as semantically invalid seeds, high computational overhead, and limited cross-domain adaptability remain unresolved. Most importantly, we identify two transformative directions with the potential to redefine CGF: (1) LLM-powered fuzzing , which combines generative AI with domain-specific fine-tuning to produce context-aware inputs; and (2) neurosymbolic integration , which merges the precision of symbolic execution with the scalability of neural networks to tackle path explosion. By synthesizing these insights, this survey not only clarifies the state-of-the-art but also outlines a roadmap for developing robust, explainable, and widely applicable intelligent fuzzers. The future of CGF lies in hybrid models that integrate data-driven learning with formal methods, paving the way for autonomous vulnerability discovery in an era of increasingl

关键词： Greybox fuzzing Software vulnerability Code coverage Deep learning Large language models

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SmartGift: Learning to Generate Practical Inputs for Testing Smart Contracts

SmartGift: Learning to Generate Practical Inputs for Testing...

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International Conference on Software Maintenance (ICSM)

作者： Teng Zhou Kui Liu Li Li Zhe Liu Jacques Klein Tegawendé F. Bissyandé Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Monash University Melbourne Australia Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg

With the boom of Initial Coin Offerings (ICO) in the financial markets, smart contracts have gained rapid popularity among consumers. Smart contract vulnerabilities however made them a prime target to malicious attacks that are leading to huge losses. The research community is thus applying various software engineering technologies to smart contracts to address them. In general, to detect vulnerabilities in smart contracts, mutation and fuzz based testing approaches have been widely studied and indeed achieved promising performance on benchmark datasets. Generating test inputs with mutation approaches essentially relies on the available test cases in a smart contract program. In our preliminary study, however, we observed that 56.4% of 218 identified open-source smart contract project repositories do not provide any test case for validation. Fuzzing test inputs leads to random values and lacks practical usefulness. Our work addresses this problem: we propose an approach, Smartgift, which generates practical inputs for testing smart contracts by learning from the transaction records of real-world smart contracts. Leveraging a collected set of over 60 thousand transaction records, Smartgift is able to generate relevant test inputs for ~77% smart contract functions, largely outperforming the traditional fuzzing approach (successful for only 60% functions). We further demonstrate the practicality of the test inputs by using them to replace the test inputs of the ContractFuzzer state of the art smart contract vulnerability detector: with inputs by Smartgift, ContractFuzzer can now detect 131 of the 154 vulnerabilities in its benchmark.

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Implementation of Hodgkin-Huxley Spiking Neuron Model Using FPGA 19

Implementation of Hodgkin-Huxley Spiking Neuron Model Using ...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Yi, Zexuan Fu, Wenlong Cao, Lihong Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781728105352

For the study of new computer architecture inspired by brain, implementing neuron function on hardware has become an emerging research direction. To study the feasibility of biological neuron implementation and acceleration characteristics on the hardware platform, the basic Hodgkin-Huxley (HH) neuron model and a small scale of synapse connection model is implemented on Xilinx Zynq7 Zedboard FPGA, high-level-synthesis method is used to reduce the burden of hardware design. The Hodgkin-Huxley model simulation result of FPGA are validated through high level software simulation tool in Brian2, furthermore, resource utilization and calculation time cost are provided in this paper, finally, a pipeline design of HH neuron model is used to accelerate calculations and reduce resource utilization, the result proves that this design is suitable for HH model simulation. © 2019 IEEE.

关键词： Neurons

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Representation Learning on Networks for Community Detection

Representation Learning on Networks for Community Detection

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International Conference on advanced Cloud and Big Data (CBD)

作者： Jingya Zhou Jianxi Fan Jin Wang Baolei Cheng School of Computer Science and Technology Soochow University Suzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Community detection is a fundamental problem in network analysis. In recent years, network representation learning has been leveraged to help the detection of potential communities. However, representation learning and community detection in these studies are usually optimized independently. In addition, they also have a strong assumption that the number of communities is prespecified before detection. To address those problems, we propose a joint representation learning and community detection (JRC) method. To allow the learned representations to be community-aware, JRC introduces two additional terms to the loss function for clustering. One is designed to getting node representations as close as possible to the exemplar of the community. Another is set up as a smoothness regularization to enforce the community properties of high cohesion and low coupling. Experimental results on multiple real-world network datasets show that JRC improves the quality of community detection and outperforms many competitive baselines.

关键词： Couplings Big Data

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