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13th International Conference on software Technology and engineering, ICSTE 2023

作者： Li, Xia Juza, Ryan Kennesaw State University The Department of Software Engineering and Game Design and Development Marietta United States

ISBN: (纸本)9798350371475

As modern software systems become more and more complex, bugs (also called faults) are prevalent and inevitable. Automated fault localization focuses on identifying faulty program elements that cause software failures. Mutation-based fault localization (MBFL) is one popular approach and has been proved to be more effective in real bugs. However, MBFL brings a problem of execution cost since it requires the execution of the whole test suite against a huge number of generated mutants. In this paper, we propose FastMBFL, a novel technique to improve the efficiency of MBFL by considering the importance of the impact information originated from failed test while maintaining the similar accuracy. The experimental results show that in terms of the widely used evaluation metric Mutant-Test Pair (MTP), FastMBFL can reduce the mutation cost by 24%-40% compared with the state-of-the-art technique. © 2023 IEEE.

关键词： Program debugging

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game theory in network security for digital twins in industry

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Digital Communications and Networks 2024年第4期10卷 1068-1078页

作者： Hailin Feng Dongliang Chen Haibin Lv Zhihan Lv School of Information Engineering Zhejiang A&F UniversityHangzhou311300China School of Data Science and Software Engineering Qingdao UniversityQingdao266000China North China Sea Offshore Engineering Survey Institute Ministry of Natural Resources North Sea BureauQingdao266000China Department of Game Design Faculty of ArtsUppsala UniversitySweden

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game *** the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game *** evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is *** this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and *** the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy *** y0?0.9,X converges rapidly in a relatively short *** y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to *** is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the *** greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability *** the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of *** study can provide an effective solution to protect the network security

关键词： Digital twins Industrial internet of things Network security game theory Attack and defense

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Improving Mutation-Based Fault Localization via Mutant Categorization 34

Improving Mutation-Based Fault Localization via Mutant Categ...

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34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： Li, Xia Tadikonda, Durga Nagarjuna Department of Software Engineering and Game Design Kennesaw State University United States

ISBN: (纸本)1891706543

Fault localization is one of the most important activities in software debugging. Among various fault localization techniques, mutation-based fault localization (MBFL) has been commonly studied with its promising performance. However, MBFL should be improved further by incorporating more useful program information. In this paper, we propose MuCatFL, a novel and lightweight technique for better MBFL via mutant categorization. In details, after executing the original test suite against all generated mutants, we categorize the mutants into two groups, positive mutants and negative mutants, to rank the tied program elements. We evaluate MuCatFL by performing an extensive study on 395 real software faults from the widely used benchmark Defects4J. The experimental results show that MuCatFL can significantly outperform MBFL techniques (e.g., localizing 138 faults within the Top-1 position on method-level, 43.75% more than traditional Metallaxis technique). We also investigate that only positive mutants can contribute to the effectiveness of MBFL. Our findings can also provide guidance for the strategies to reduce the execution cost of MBFL. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： software testing

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A Study to Evaluate the Impact of LoRA Fine-tuning on the Performance of Non-functional Requirements Classification

arXiv

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arXiv 2025年

作者： Li, Xia Kim, Allen The Department of Software Engineering and Game Design and Development Kennesaw State University Marietta United States

Classifying Non-Functional Requirements (NFRs) in software development life cycle is critical. Inspired by the theory of transfer learning, researchers apply powerful pre-trained models for NFR classification. However, full fine-tuning by updating all parameters of the pre-trained models is often impractical due to the huge number of parameters involved (e.g., 175 billion trainable parameters in GPT-3). In this paper, we apply Low-Rank Adaptation (LoRA) fine-tuning approach into NFR classification based on prompt-based learning to investigate its impact. The experiments show that LoRA can significantly reduce the execution cost (up to 68% reduction) without too much loss of effectiveness in classification (only 2%-3% decrease). The results show that LoRA can be practical in more complicated classification cases with larger dataset and pre-trained models. © 2025, CC BY-NC-ND.

关键词： software design

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Accelerating Mutation-Based Fault Localization via Optimized Mutant Execution

Accelerating Mutation-Based Fault Localization via Optimized...

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International Conference on software Technology and engineering (ICSTE)

作者： Xia Li Ryan Juza The Department of Software Engineering and Game Design and Development Kennesaw State University Marietta USA

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design and Application of a Media Big Data Processing and Analysis Model Based on Artificial Intelligence 4

Design and Application of a Media Big Data Processing and An...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Xiao, Shufang Wu, Siying Li, Kemin Wu, Dong Institute of Guangzhou University School of Game Department Software Engineering Guangdong Guangzhou China School of Art and Design Guangzhou College of Commerce Guangdong Guangzhou China School of Art Design and Creative Industries Guangzhou Southern University Guangdong Guangzhou China College of Art and Media Guangzhou Xinhua University Guangdong Guangzhou China

ISBN: (纸本)9798350352931

With the advent of the big data era, the amount and diversity of media data have grown exponentially, posing a major challenge to traditional data processing techniques. This paper studies an intelligent model to address these challenges, which uses deep learning and natural language processing to automatically extract and predict target features and achieve high-precision predictions, thereby improving the intelligence of the algorithm. A model that integrates convolutional neural networks and recurrent neural networks is established to effectively analyze multimodal data. It has an average accuracy of 89.072% in classification tasks and 89.184% in regression tasks, which is better than traditional methods such as sequence pattern mining, OLAP, and RDBMS. In addition, the model performs well in clustering tasks. In terms of efficiency, the model takes only 0.53 seconds on average to complete the task, with a CPU usage of 32.7%, a memory usage of 514MB, and an energy consumption of 0.045 units, all of which are better than the comparison methods. © 2024 IEEE.

关键词： Data accuracy

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Building the metaverse using digital twins at all scales,states,and relations

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Virtual Reality & Intelligent Hardware 2022年第6期4卷 459-470页

作者： Zhihan LV Shuxuan XIE Yuxi LI M.Shamim HOSSAIN Abdulmotaleb El SADDIK Department of Game Design Faculty of ArtsUppsala UniversitySweden College of Computer Science and Technology Qingdao UniversityQingdaoChina Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Electrical Engineering and Computer Science(EECS) University of OttawaOttawaOntarioCanada

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the *** objects can be at the micro-,meso-,or *** Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain ***,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social ***,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

关键词： Digital twins Metaverse Sensor

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Optimizing Uncertainty Estimation on Scientific Visualizations Using Learning Models

Optimizing Uncertainty Estimation on Scientific Visualizatio...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Almeida, Clara J. Guerrero-Pantoja, David Pautsch, Erik Rizzi, Silvio Thiruvathukal, George K. Pantoja, Maria University of California Santa Cruz Robotics and Electrical Engineering Santa Cruz United States University of California Santa Cruz Computer Science and Game Design Santa Cruz United States Loyola University Chicago Computer Science Chicago United States Argonne National Laboratory Argonne Leadership Computing Facility Lemont United States Computer Science and Software Engineering California Polytechnics State University San Luis Obispo United States

ISBN: (纸本)9798350355543

Scientific visualizations (SciVis) translate numerical and spatial data as images, enabling scientists to better understand the phenomena described by the data and gain insights that may be overlooked by statistical methods alone. In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis. By combining classical numerical techniques with DL methods, we can create models that potentially achieve the accuracies needed for real-world industrial applications and simulations. However, DL models often exhibit undue confidence, particularly when faced with inputs that differ significantly from the data they were trained on, known as out-of-distribution (OOD) inputs. This can lead to misclassifications where the model still produces high confidence scores, reflected in elevated softmax output values, even though its prediction is incorrect. Ideally, in such cases, the model should indicate uncertainty, effectively saying, "I do not know."In this work, we enhance the trustworthiness of DL models by quantifying their uncertainty and enabling selective classification, where the model abstains from making predictions when uncertainty is high. Instead of providing a single prediction, our model outputs a prediction distribution, allowing users to determine whether human intervention or other actions are necessary for handling high-uncertainty outputs. This paper evaluates uncertainty quantification across three different tasks: predicting airfoil pressure and velocity using a Reynolds-Averaged Navier-Stokes (RANS) model on a custom airfoil dataset, image classification using the ImageNet1K dataset, and digit recognition using the MNIST handwritten digits dataset. © 2024 IEEE.

关键词： deep learning ensemble learning evidential learning scientific visualization uncertainty

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design and Application of a Media Big Data Processing and Analysis Model Based on Artificial Intelligence

Design and Application of a Media Big Data Processing and An...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Shufang Xiao Siying Wu Kemin Li Dong Wu School of Game Department Software Engineering Institute of Guangzhou University Guangzhou Guangdong China School of Art and Design Guangzhou College of Commerce Guangzhou Guangdong China School of Art Design and Creative Industries Guangzhou Southern University Guangzhou Guangdong China College of Art and Media Guangzhou Xinhua University Guangzhou Guangdong China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

关键词： Deep learning Analytical models Computational modeling Process control Media Big Data Feature extraction Data models Natural language processing Computational efficiency

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Optimizing Uncertainty Estimation on Scientific Visualizations Using Learning Models 24

Optimizing Uncertainty Estimation on Scientific Visualizatio...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Clara J. Almeida David Guerrero-Pantoja Erik Pautsch Silvio Rizzi George K. Thiruvathukal Maria Pantoja Robotics and Electrical Engineering University of California Santa Cruz Santa Cruz USA Computer Science and Game Design University of California Santa Cruz Santa Cruz USA Computer Science Loyola University Chicago Chicago USA Argonne Leadership Computing Facility Argonne National Laboratory Lemont USA Computer Science and Software Engineering California Polytechnics State University San Luis Obispo USA

ISBN: (纸本)9798350355543

Scientific visualizations (SciVis) translate numerical and spatial data as images, enabling scientists to better understand the phenomena described by the data and gain insights that may be overlooked by statistical methods alone. In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis. By combining classical numerical techniques with DL methods, we can create models that potentially achieve the accuracies needed for real-world industrial applications and simulations. However, DL models often exhibit undue confidence, particularly when faced with inputs that differ significantly from the data they were trained on, known as out-of-distribution (OOD) inputs. This can lead to misclassifications where the model still produces high confidence scores, reflected in elevated softmax output values, even though its prediction is incorrect. Ideally, in such cases, the model should indicate uncertainty, effectively saying, "I do not know." In this work, we enhance the trustworthiness of DL models by quantifying their uncertainty and enabling selective classification, where the model abstains from making predictions when uncertainty is high. Instead of providing a single prediction, our model outputs a prediction distribution, allowing users to determine whether human intervention or other actions are necessary for handling high-uncertainty outputs. This paper evaluates uncertainty quantification across three different tasks: predicting airfoil pressure and velocity using a Reynolds-Averaged Navier-Stokes (RANS) model on a custom airfoil dataset, image classification using the ImageNet1K dataset, and digit recognition using the MNIST handwritten digits dataset.

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