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Two-Dimensional Multi-Deep CNNs for Accurate and Robust software Defect Detection: A Performance-Driven Approach

Two-Dimensional Multi-Deep CNNs for Accurate and Robust Soft...

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Emerging systems and Intelligent Computing (ESIC), International conference on

作者： Vinod Veeramachaneni Pradeep Kumar Mallick Subhashree Rout Piyush Kumar Pareek Department of Information Technology Colorado Technical University USA School of Computer Engineering KIIT Deemed to be University Department of Computer Science and Engineering Nalanda Institute of Technology Bhubaneswar Odisha Nitte Meenakshi Institute of Technology Yelahanka Bengaluru Karnataka India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

software systems are growing in size and complexity. These properties make software defect prevention challenging. Thus, automatically predicting software module defects can help engineers conserve resources. Several ways have been presented to discover and fix these issues cheaply. However, these solutions may need a performance bump. This paper introduces the Multi-Deep Convolutional Neural Network to improve binary and multi-class software classification. Instead of one-dimensional CNNs, the proposed model captures n-gram characteristics inside and between datasets using a two-dimensional multi-scale convolutional technique. Advanced crow search optimisation algorithm (ACSOA) is used to optimally select model parameters. Besides Apache Active MQ, the study employed Eclipse data sets to evaluate the recommended deep learning method. F1-score, recall, accuracy, and precision were utilized to validate the model. The results show that the suggested strategy finds software vulnerabilities better and more reliably than current models.

关键词： Accuracy Computational modeling Prevention and mitigation software algorithms Optimization methods computer architecture Syntactics software systems software reliability Convolutional neural networks

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AI-Driven Cybersecurity and Functional Framework for Remote Laboratories: A software engineering Perspective

AI-Driven Cybersecurity and Functional Framework for Remote ...

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Computational Intelligence Approaches and Applications (ICCIAA), International conference on

作者： Nesreen Otoum Enrique Arias Jesús Jover Alfaro Department of Software Engineering University of Petra Amman Jordan Department of Computer Systems University of Castilla-La Mancha Albacete Spain Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Remote laboratories have reshaped the way engineering courses provide hands-on experimentation by enabling students to control physical lab equipment via online interfaces. nevertheless, their ease, However, these platforms face challenges such as security vulnerabilities, performance instability, and limited user support. This paper introduces a layered framework that integrates artificial intelligence (AI) into a secure, modular design. Using conventional software engineering techniques, our approach improves cybersecurity through intelligent threat detection, automated resource balancing, and context-aware user interaction. We provide a comprehensive explanation of the framework's architecture, which methodically addresses critical requirements. This includes a proactive cybersecurity apparatus that adapts to emergent threats, as well as real-time device control and data acquisition. Ultimately, this work aims to assist educators and institutions build or modernize remote lab environments that offer robust protection and seamless learning outcomes, even under rapidly shifting demands.

关键词： Remote laboratories computer architecture Threat assessment Real-time systems Resource management Reliability computer security Protection Standards software engineering

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Research on Lightweight Object Detection and Image Recognition Based on Convolutional Neural Networks

Research on Lightweight Object Detection and Image Recogniti...

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2025 2nd International conference on Algorithms, software engineering and Network Security (ASENS)

作者： Wen Ha Jiahui Yang School of Computer Engineering Technology Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

With the rapid development of deep learning technology, Convolutional Neural Networks (CNNs) have achieved remarkable success in the field of computer vision. However, traditional CNN models often feature complex network structures and a large number of parameters, which limits their application on resource-constrained devices such as mobile devices and embedded systems. Therefore, researching lightweight convolutional neural networks is of great significance for promoting the popularization of computer vision technology on these devices. This paper first outlines the research background and significance of lightweight convolutional neural networks, and then introduces in detail the MobileNet series models, the RepViT model, and a lightweight convolutional neural network called FastEdgeCNN for image object recognition. Finally, experiments are conducted to verify the effectiveness of these lightweight models in object detection and image recognition tasks.

关键词： computer vision Image recognition Computational modeling software algorithms Object detection Network security Mobile handsets Convolutional neural networks Object recognition software engineering

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Multimodal Classification of software Issue Reports Using CLIP: A Fine-Tuned Approach for Bug and Feature Detection

Multimodal Classification of Software Issue Reports Using CL...

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International conference on Electrical, computer and Communication engineering (ECCE)

作者： Yasir Arafah Sumaiya Shejin Shamim Akhter Md Arafat Alam Khandaker Department of Computer Science and Engineering AISIP Lab Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Efficiently identifying software bugs is critical to accelerate software development processes. Traditional methods, which frequently depend on manual review or constraint-driven mechanisms, require a significant amount of time, susceptible to mistakes made by humans, and unable to scale effectively with the growing complexity of modern software systems. With texts, code snippets, and images from well-known repositories like VS Code, Flutter, Roslyn, and Kubernetes in total comprised of 4304 samples, this paper presents a new multimodal approach that uses Contrastive Language-Image Pretraining (CLIP) and its ViT-B/32 variant to effectively address this problem. We experimented with several CLIP variants, including ViT-B/32, RN50, RN101, RN50x4, ViT-B/16, ViT-L/14, and RN50x64, and observed that ViT-B/32 performed well under zero-shot settings, with an accuracy of 56.6%, precision of 54.08%, recall of 53.96% and F1-score of 54.02%, showing the usefulness of general-purpose models in task-specific situations. We performed k-fold cross-validation and hyperparameter tuning on the ViT-B/32 variant, which improved the model’s ability to classify software issues with an 18.8% improvement in accuracy, 19.1% in precision, 20.14% in recall, and 19.6% in F1-score over its zero-shot performance. These findings demonstrate a scalable framework for multimodal classification that goes beyond bugs and features. This framework has potential uses in issue prioritization, automated software maintenance, and productivity tools. These findings emphasize the benefits of using multimodal AI models, such as CLIP, to tackle practical software engineering problems, providing new insights and a solid basis for further developments in this field.

关键词： Solid modeling software maintenance Accuracy Codes computer bugs software systems software Tuning software engineering software development management

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LSMR: Synergy Randomness in Liquid State Machine and RRAM-based Analog-digital Accelerator 24

LSMR: Synergy Randomness in Liquid State Machine and RRAM-ba...

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43rd International conference on computer-Aided Design, ICCAD 2024

作者： Lin, Ning Wang, Songqi Zhang, Xinyuan Wang, Shaocong He, Yangu Zhang, Woyu Wang, Bo Li, Jiankun Li, Mingzi Cui, Binbin Li, Yi Chen, Jia Xia, Chunwei Xuan, Wei Chen, Xiaoming Shang, Dashan Wang, Zhongrui Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong ACCESS – AI Chip Center for Emerging Smart Systems Hong Kong Science Park Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The Hong Kong University of Science and Technology Hong Kong School of Computing University of Leeds Leeds United Kingdom Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400710773

Bio-inspired event sensors are gaining popularity at the edge, such as in robots and wearable electronics. This trend necessitates learning vast amounts of sensory data on the edge, often in few-shot or even zero-shot scenarios, posing challenges in both software and hardware. This paper presents a novel software-hardware co-design to address these issues. software-wise, we develop an SNN-ANN model, where the SNN encoder is a liquid state machine (LSM) that naturally processes events and significantly reduces learning complexity at the edge due to fixed random weights. The lightweight trainable ANN projection heads are optimized through contrastive learning, enabling zero-shot learning of multimodal events. Hardware-wise, we propose a hybrid analog (RRAM)-digital (CMOS) accelerator – LSMR. The analog in-memory computing core physically implements the LSM by leveraging RRAM stochasticity to generate fixed random weights. The digital core utilizes innovative reconfigurable systolic arrays to accelerate the contrastive learning of ANN projection heads. Extensive experimental outcomes from six neuromorphic datasets, encompassing visual, tactile, and auditory modalities, demonstrate that LSMR considerably improves energy efficiency by a range of 1.65× to 23.70×, in comparison to state-of-the-art edge devices. Simultaneously, it reduces training complexity by a range of 152.83× to 20,587.77× across various edge learning tasks. © 2024 Copyright is held by the owner/author(s).

关键词： Reconfigurable architectures

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F-Measure of Component Level Features to Predict Architectural Anti-Patterns in software

F-Measure of Component Level Features to Predict Architectur...

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International conference on Web Research (ICWR)

作者： Somayeh Kalhor Mohammad Reza Keyvanpour Department of Computer and Information Technology Engineering Islamic Azad University Qazvin Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

software systems are constantly evolving to meet the needs of their users, therefore, software complexity inevitably increases, violating software engineering principles, creating anti-patterns at various levels of abstraction, and reducing software quality. Early detection of these anti-patterns is valuable for developers, as their elimination can help prevent potential system failures. Anti-patterns can occur in various software, including web applications, which are also notable in web research. Various software features have been used to predict and detect anti-patterns at different levels of abstraction, and their threshold values affect the accuracy of this process. This study investigates the impact of specific component-level features on dependency detection between components of an open-source system. These dependencies are significant in detecting and predicting component anti-patterns such as cyclic and hub-like dependencies. This study analyzes structural, topological, and content similarity features in opensource code and compares the F-Measure values of these features in the detection of connections between components of the considered software code. The average F-Measure for topological features in OpenJPA 2.0.0 software is 0.73, and for content similarity features it is 0.76. Therefore, these two criteria are almost equally effective in predicting dependencies between components using machine learning algorithms.

关键词： Codes Machine learning algorithms Accuracy software quality Feature extraction software systems software Complexity theory software engineering

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Design Matrix Factorization and Knowledge-Guided Clustering for Trust-Aware Cross-Domain Recommendation systems

Design Matrix Factorization and Knowledge-Guided Clustering ...

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software engineering and computer Science (CSECS), International conference on

作者： Jiaqi Chang Fusheng Yu School of Mathematical Sciences Beijing Normal University Beijing People's Republic of China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The core challenge in designing an algorithm for trust-aware cross-domain recommendation systems lies in effectively integrating ratings from all domains (target-domain and source-domains) and trust relationships to improve recommendation performance in the target-domain. These systems involve two types of information fusion: intra-domain information fusion, which integrates intra-domain trust relationships and ratings within each domain (target-domain or source-domain), and cross-domain information fusion, which fuses information from all domains through inter-domain trust relationships as a bridge. The existing algorithms are limited in that they only address either intra-domain information fusion or cross-domain information fusion, failing to comprehensively utilize both intra-domain and inter-domain trust relationships for integrating information from all domains. To address this limitation, we propose a novel approach that combines inter-domain information fusion and cross-domain information fusion. Based on this approach, we design a matrix factorization and knowledge-guided clustering based trust-aware cross-domain collaborative filtering algorithm (MFKC _ TCCFA). Experimental results on a dataset show that MFKC_TCCFA outperforms existing state-of-the-art algorithms.

关键词： computer science Social networking (online) Fuses Collaborative filtering Soft sensors software algorithms Semantics Clustering algorithms Recommender systems software engineering

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SECURE - An Approach to Recommending Security Design Patterns

SECURE - An Approach to Recommending Security Design Pattern...

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IEEE International conference on software Architecture Workshops (ICSAW)

作者： Alex R. Sabau Dominik Lammers Horst Lichter Research Group Software Construction RWTH Aachen University Aachen Germany RWTH Aachen University Aachen Germany

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Security is an important quality attribute of software systems. Thus, designing secure architectures is a highly important, yet difficult and complex task. Moreover, there is a huge lack of security architecture experts. To overcome these challenges, we aim to make security design knowledge reusable for architects by proposing SECURE, a recommendation approach to secure software design. It lifts design patterns and knowledge engineering concepts to security-related design recommendations for software architectures. This paper presents the central concepts of this approach, the overall recommendation process, and the first results from an initial case study.

关键词： Knowledge engineering software design software architecture computer architecture software systems Security System analysis and design Recommender systems

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25th International conference on Web Information systems engineering, WISE 2024

作者： Tu, Longchuan Meng, Shunmei Liu, Xiao Liu, Guanfeng Beheshti, Amin Zhang, Xuyun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjin China State Key Laboratory for Novel Software Technology Nanjing University Nanjin China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819605699

Recently, graph neural networks(GNNs) have played a key crucial in many recommendation situations. In particular, contrastive learning-based hypergraph neural networks (HGNNs) are gradually becoming a research focus for addressing issues of data sparsity and noise. Despite many studies proving their outstanding performance, there are still some shortcomings: i) Most contrastive learning-based HGNNs primarily rely on cross-view contrastive learning, while neglecting contrastive learning on the interaction graph. ii) Utilizing node embedding for hypergraph structure learning is susceptible to the influence of low-quality representations, thereby constraining the learning capability of HGNNs. To address these issues, we offer a Semantic Similarity-based Graph Contrastive Learning framework (SSGCL), which aims to jointly learn representations with rich semantic information through within-view and cross-view contrastive learning. Specifically, we first introduce consistency contrastive learning, which captures self-supervised signals through semantic similarity between nodes and their neighborhoods. It better captures the unique features of nodes through the connections between nodes and their neighborhoods. Then, we utilize interaction graph learning hypergraph structure, promoting it to extract potential node connections and thus improving its ability to describe homogeneous node relationships. Experimental evaluations on three actual datasets show that SSGCL performs much better than the current baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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SliQSim: A Quantum Circuit Simulator and Solver for Probability and Statistics Queries 31th

SliQSim: A Quantum Circuit Simulator and Solver for Probabil...

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31st International conference on Tools and Algorithms for the Construction and Analysis of systems, TACAS 2025, which was held as part of the International Joint conferences on Theory and Practice of software, ETAPS 2025

作者： Chen, Tian-Fu Jiang, Jie-Hong R. Graduate School of Advanced Technology National Taiwan University Taipei Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan

ISBN: (纸本)9783031906596

SliQSim, originally developed as the first exact quantum circuit simulator, is extended in this paper to provide capabilities for the analysis and verification of quantum states. It provides an interface for users to specify interested quantum states for querying the exact probability or expectation value of a user-defined property. Case studies on three quantum algorithms show the unique capability of SliQSim benefiting exact quantum circuit analysis and verification, beyond the support by other tools. © The Author(s) 2025.

关键词： Quantum electronics

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