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Evaluating the usage of fault localization in automated program repair:an empirical study

Frontiers of computer science 2021年第1期15卷 53-67页

作者： Deheng YANG Yuhua QI Xiaoguang MAO Yan LEI College of Computer National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems Changsha 410073China Beijing Institute of Tracking and Communication Technology Beijing 100094China School of Big Data&Software Engineering Chongqing UniversityChongqing 400044China

Fault localization techniques are originally proposed to assist in manual debugging by generally producing a rank list of suspicious *** the increasing popularity of automated program repair,the fault localization techniques have been introduced to effectively reduce the search space of automated program *** developers who mainly focus on the rank information,current automated program repair has two strategies to use the fault localization information:suspiciousness-first algorithm(SFA)based on the suspiciousness accuracy and rank-first algorithm(RFA)relying on the rank ***,despite the fact that the two different usages are widely adopted by current automated program repair and may result in different repair results,little is known about the impacts of the two strategies on automated program *** this paper we empirically compare the performance of SFA and RFA in the context of automated program ***,we implement the two strategies and six well-studied fault localization techniques into four state-of-the-art automated program repair tools,and then use these tools to perform repair experiments on 60 real-world bugs from *** study presents a number of interesting findings:RFA outperforms SFA in 70.02%of cases when measured by the number of candidate patches generated before a valid patch is found(NCP),while SFA performs better in parallel repair and patch diversity;the performance of SFA can be improved by increasing the suspiciousness accuracy of fault localization techniques;finally,we use SimFix that deploys SFA to successfully repair four extra Defects4J bugs which cannot be repaired by SimFix originally using *** observations provide a new perspective for future research on the usage and improvement of fault localization in automated program repair.

关键词： automated program repair fault localization empirical study

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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Dynamic Bi-Elman Attention Networks (DBEAN): Dual-Directional Context-Aware Representation Learning for Enhanced Text Classification

arXiv

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arXiv 2025年

作者： Liao, Mengyao Lai, Zhenglin Xu, Dong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Text classification, a fundamental task in natural language processing (NLP), aims to categorize textual data into predefined labels. Traditional methods struggled with complex linguistic structures and semantic dependencies. The advent of deep learning, particularly recurrent neural networks (RNNs) and Transformer-based models, has significantly advanced the field by enabling nuanced feature extraction and context-aware predictions. Despite improvements, existing models exhibit limitations in balancing interpretability, computational efficiency, and long-range contextual understanding. This paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN), which integrates bidirectional temporal modelling with self-attention mechanisms. DBEAN dynamically assigns weights to critical segments of input, improving contextual representation while maintaining computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Elman neural networks

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Visual Explanations: Activation-Based Acute Lymphoblastic Leukemia Cell Classification

Visual Explanations: Activation-Based Acute Lymphoblastic Le...

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IEEE International Conference on Development and Learning, ICDL

作者： Muhammad Umair Raza Adil Nawaz Xintong Liu Zhuangzhuang Chen Victor C.M. Leung Jie Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

The saliency methods are widely used for generating heatmaps that emphasize the important portions of an input image for deep networks on a specific classification task. Interpretability is crucial for deploying deep neural networks in real-world applications. However, the heatmaps produced by current visual explainable methods may contain or visualize particulars differently. To analyze and compare the visualization of different methods, such as Gradient-based, Activation-based, Perturbation-based, and Region-based methods, we empirically evaluated them on the acute lymphoblastic leukemia (cancer cell) classification task using state-of-the-art convolutional neural networks. We also visualized the essential pathological features (salient parts) that are the reasons for the classification results on the classification of normal versus malignant cell (CNMC) dataset.

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An Entropy Based Unsupervised Visual Vocabulary Optimization for Visual Loop Closure Detection

An Entropy Based Unsupervised Visual Vocabulary Optimization...

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2023 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2023

作者： Li, Bo Yu, Xiangdong Feng, Xin He, Ying Zhu, Zhaohan Ge, Yongxin Geng, Li Department of Criminal Science Technology Chongqing Police College Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China School of Electrical Engineering Southwest Jiaotong University Sichuan China School of Big Data & Software Engineering Chongqing University Chongqing China New York City College of Technology City University of New York New York United States

ISBN: (纸本)9781510665040

In the task of simultaneous localization and mapping (SLAM), a mobile robot should be able to recognize the location that it has previously visited. This is termed as the loop closure detection. Current loop closure detection is implemented by matching of image features extracted from present image and previous images, where good feature representation is highly required. Bag-of-visual words (BoW) is known to be an effect feature representation for image and video matching task. However, to build the visual feature vocabulary in the unsupervised manner is still challenging. In this paper, we are aiming at evaluating the representability of the visual words and generating the discriminative vocabulary representation. We first present an entropy ranking based scheme to improve the spectral clustering algorithm by leveraging eigenvector selection and feature embedding simultaneously. We then propose to evaluate the representative ability of the visual vocabulary quantitatively based on the complexity of the decomposition of similarity matrix in the unsupervised manner. We perform experiments on the loop closure detection datasets. which contains strong perceptual aliasing in both indoor and outdoor scenarios. Results demonstrate that the proposed BoW based feature representation has strong representation ability in measuring the similarity of loop closure events and differentiating new visited places. © 2023 SPIE.

关键词： Entropy

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Drug-Target Interaction Prediction Based on Drug Subgraph Fingerprint Extraction Strategy and Subgraph Attention Mechanism 9th

Drug-Target Interaction Prediction Based on Drug Subgraph Fi...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Wang, Lizhi Zhang, Xiaolong Lin, Xiaoli Hu, Jing College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System Hubei Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9783031466700

Drug discovery is a major focus of modern research, and predicting drug-target interactions is one of the strategies to facilitate this research process. Traditional laboratory methods have long time cycles and are relatively costly, so the use of high-precision virtual screening is essential. Previous virtual screens have only considered the sequence structure of the drug or the graph structure of the drug, without considering the role of the drug subgraph structure. Therefore, this paper adopts a method for extracting drug subgraphs and designs a strategy for extracting drug subgraph fingerprints. Then, the fingerprint vector data of the drugs are trained by the graph neural network to produce drug information vectors containing the spatial structure of the drug subgraph. Finally, the subgraph attention mechanism is used to update the information on protein targets, facilitating the model to extract more information about the target site. The experimental results show that the model can predict drug-target interactions well, outperforming the state-of-the-art model in all metrics. And it is also effective in both classification prediction and affinity regression prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Graph neural networks

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Prediction of Compressive Strength of Green Concrete by Artificial Neural Network 1st

Prediction of Compressive Strength of Green Concrete by Arti...

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1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Verma, Manvendra Upreti, Kamal Dadhich, Priyanka Ghosh, Soumi Khatri, Vishal Singh, Prashant Department of Civil Engineering GLA University Uttar Pradesh Mathura India Department of Computer Science and Engineering Akhilesh Das Gupta Institute of Technology and Management Delhi India Department of Big Data Analytics Asia Pacific Institute of Management Delhi India Department of Information Technology Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India Department of Computer Science and Engineering Sunder Deep Engineering College Ghaziabad India

ISBN: (纸本)9783031250873

The concrete business has just come out with a brand-new product called geopolymer concrete. Because of its resilience in harsh environments and its high strength, it has the potential to revolutionise the whole construction industry. It is an excellent substitute for the traditional kind of concrete. It outperforms traditional concrete in terms of sustainability, ecology, durability, and economic viability. In this day and age, the use of machine learning strategies is not just the future of the research and development industry, but of all industries. These methods provide predictions about the outcomes by analysing the facts from the past. The process of determining the outcomes or worth of anything in the construction sector is very challenging, time consuming, and arduous. Without having to conduct destructive experiments or take samples, the strength of a mix design may now be more accurately predicted thanks to these procedures. By using deep learning and the random forest method, the purpose of this research is to predict the compressive strength of flyash-based geopolymer concrete. This will be accomplished by comparing the two algorithms with varying degrees of error and coefficient correlation. It has been shown via the use of simulated data that the random forest algorithm is the method that is most suited for making predictions about compressive strength. After the developing a model, the various errors were found for accuracy. The mean absolute error, root mean square error, relative absolute error, and root relative squared error are 3.46%, 5.94%, 64.32%, and 82.97%, respectively for the deep learning predicted compressive strength. The errors provide the proof of model accuracy to predict the compressive strength on the basis of ingredients proportions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Compressive strength

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What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs? 23

What Makes Good In-Context Demonstrations for Code Intellige...

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Proceedings of the 38th IEEE/ACM International Conference on Automated software engineering

作者： Shuzheng Gao Xin-Cheng Wen Cuiyun Gao Wenxuan Wang Hongyu Zhang Michael R. Lyu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350329964

Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models for making predictions. This new learning paradigm is training-free and has shown impressive performance in various natural language processing and code intelligence tasks. However, the performance of ICL heavily relies on the quality of demonstrations, e.g., the selected examples. It is important to systematically investigate how to construct a good demonstration for code-related tasks. In this paper, we empirically explore the impact of three key factors on the performance of ICL in code intelligence tasks: the selection, order, and number of demonstration examples. We conduct extensive experiments on three code intelligence tasks including code summarization, bug fixing, and program synthesis. Our experimental results demonstrate that all the above three factors dramatically impact the performance of ICL in code intelligence tasks. Additionally, we summarize our findings and provide takeaway suggestions on how to construct effective demonstrations, taking into account these three perspectives. We also show that a carefully-designed demonstration based on our findings can lead to substantial improvements over widely-used demonstration construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, 175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, respectively.

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Tkstagnet: A Top-K Spatio-Temporal Attention Gating Network for Air Pollution Prediction

SSRN

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SSRN 2024年

作者： Zhou, Hongliang Mao, Yingchi Li, Xiang Rong, Yi Chen, Ling Yin, Changkui College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China

Accurate air pollution prediction is of great significance for human health, travel and environmental management. As an emerging artificial intelligence technology, Transformer has already shown tremendous potential in air pollution prediction. Unfortunately, existing Transformer-based methods are challenging to accurately predict air pollution due to two limitations: 1) they neglect irrelevant spatial and temporal neighbors (e.g., neighboring sensors and timesteps) when modeling spatio-temporal correlations, and 2) they lack an adaptive mechanism to mine different effects caused by various external factors (e.g., meteorological conditions). Jointly taking these limitations into account, we propose a novel Transformer-based architecture for air pollution prediction, named Top-K Spatio-Temporal Attention Gating Network (TKSTAGNet). In this architecture, to effectively extract spatio-temporal correlations, we first design a TopKpooling Spatial Attention (TopK-SA) and a TopKpooling Temporal Attention (TopK-TA). In TopK-SA, the TopKpooling mechanism is introduced into spatial attention to filter out irrelevant spatial neighbors, accurately capturing spatial dependencies. In TopK-TA, the TopKpooling mechanism is integrated with temporal attention to identify and exclude irrelevant temporal neighbors, precisely extracting temporal dependencies. To adaptively fuse various external factors, a gating fusion module is then proposed. We conduct experiments on two real-world air pollution datasets from urban regions in Beijing and Shanghai, respectively. Experimental results demonstrate that TKSTAGNet outperforms state-of-the-art baselines in prediction precision and fitting the variation trends of air pollution. © 2024, The Authors. All rights reserved.

关键词： Air pollution

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Mellivora Capensis: A Backdoor-Free Training Framework on the Poisoned dataset without Auxiliary data

arXiv

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arXiv 2024年

作者： Pu, Yuwen Chen, Jiahao Zhou, Chunyi Feng, Zhou Li, Qingming Hu, Chunqiang Ji, Shouling The College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310027 China The School of Big Data & Software Engineering Chongqing University Chongqing400030 China

The efficacy of deep learning models is profoundly influenced by the quality of their training data. Given the considerations of data diversity, data scale, and annotation expenses, model trainers frequently resort to sourcing and acquiring datasets from online repositories. Although economically pragmatic, this strategy exposes the models to substantial security vulnerabilities. Untrusted entities can clandestinely embed triggers within the dataset, facilitating the hijacking of the trained model on the poisoned dataset through backdoor attacks, which constitutes a grave security concern. Despite the proliferation of countermeasure research, their inherent limitations constrain their effectiveness in practical applications. These include the requirement for substantial quantities of clean samples, inconsistent defense performance across varying attack scenarios, and inadequate resilience against adaptive attacks, among others. Therefore, in this paper, we endeavor to address the challenges of backdoor attack countermeasures in real-world scenarios, thereby fortifying the security of training paradigm under the data-collection manner. Concretely, we first explore the inherent relationship between the potential perturbations and the backdoor trigger, and demonstrate the key observation that the poisoned samples perform more robustness to perturbation than the clean ones through the theoretical analysis and experiments. Then, based on our key explorations, we propose a robust and clean-data-free backdoor defense framework, namely Mellivora Capensis (MeCa), which enables the model trainer to train a clean model on the poisoned dataset. MeCa can effectively identify poisoned samples and train a clean model on the poisoned dataset and eliminate the requirement of clean samples or knowledge about the poisoned dataset (e.g., poisoning ratio). We conduct extensive experiments in defending against 8 SOTA attacks (including 3 adaptive attacks) on 4 datasets (Imagnette, Tiny I

关键词： Adversarial machine learning

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