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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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Classification of Privacy Preserved Medical Data with Fractional Tuna Sailfish Optimization Based Deep Residual Network in Cloud

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Annals of Data Science 2024年 1-26页

作者： Shikalgar, Shabanam K. Kumar, N. V. S. Pavan Singh, Gavendra Rashid, Faizur Sharad Institute of Technology College of Engineering Yadrav India Koneru Lakshmiah Education Foundation Vijayawada India Department of Software Engineering College of Computing and Informatics Haramaya University Dire Dawa138 Ethiopia College of Computing and Informatics Haramaya University Dire Dawa138 Ethiopia

Nowadays, with the growth of emerging technologies, increased attention has been paid to the classification of privacy-preserved medical data and development of various privacy-preserving models for the promotion of online medical pre-diagnosis systems. Medical data is highly sensitive and it is essential to ensure privacy of medical records from third-party users to increase service quality, satisfy patients and earn trust. The classification of medical preserved data is helpful to build a clinical decision system by classifying patients based on their disease and symptoms. In this article, a hybrid optimization-based deep learning model named Fractional Tuna Sailfish Optimization–Deep Residual Network (FractionalTSFO-DRN) is designed to precisely classify the privacy preserved medical data. A privacy utility coefficient matrix is used to ensure the privacy of medical data by generating a key matrix using Tuna Sailfish Optimization (TSFO) algorithmic technique. The privacy-preserved medical data is allowed for the classification process using DRN and the introduced Fractional TSFO is used to optimize and enhance the classification in DRN. The assessment followed by using heart disease prediction databases proved that the employed classification technique recorded an accuracy of 94.67%, a True Positive Rate of 93.56%, and a True Negative Rate of 89.68% respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Classification (of information)

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JUVDATA: Data Visualization of Juvenile Crime in Malaysia 1

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4th International Conference on Mathematics, Statistics, and computing Technology, ICMSCT 2023

作者： Daud, Nur’Aina Samsuri, Nur Amalina Hussein, Surya Sumarni Department of Computing Sciences College of Computing Informatics and Mathematics UiTM Shah Alam Selangor Shah Alam Malaysia

ISBN: (数字)9789819734504

ISBN: (纸本)9789819734498

Crime is a pervasive issue that afflicts our society. When criminal incidents transpire, there is a prevailing consensus that individuals within the community have suffered unwarranted harm, either through direct involvement or indirectly, with the nature of the harm varying based on the type of offence perpetrated. Juvenile delinquency represents a subset of criminal conduct that exerts an impact on contemporary society. The objective of this project is to develop JUVDATA, a data visualisation dashboard that presents comprehensive information regarding juvenile offenders in Malaysia. The utilisation of data visualisation in this particular context facilitates the identification of previously undisclosed patterns and enhances the understanding of aggregate rates of adolescent criminality. The project employs the Rapid Application Development (RAD) methodology, which is integrated with the CRISP-DM framework. The CRISP-DM framework encompasses six distinct phases, including business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The dashboard can function as a medium for individuals to acquire knowledge and actively participate in the subject matter of juvenile crime through the utilisation of interactive data visualisation and analysis. Developing a more comprehensive understanding of the statistical aspects pertaining to juvenile delinquency can facilitate the exploration of efficacious strategies aimed at mitigating such incidents. This study also addressed future proposals for enhancing JUVDATA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data visualization

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Factors of organizational adoption of enterprise architecture in Malaysian public sector: a multi group analysis

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Journal of Systems and Information Technology 2022年第4期24卷 331-360页

作者： Ahmad, Nor Azizah Mohd Drus, Sulfeeza Kasim, Hairoladenan Department of College of Computing and Informatics UNITEN Putrajaya Malaysia

Purpose: The relevant importance of enterprise architecture (EA) to an organization has gained the attention of the public sector. However, its adoption rate remains slow on the uptake. Thus, this study aims to investigate the various factors associated with adoption intention based on the technological, organizational and environmental framework for federal and state types of public organizations to provide greater predictive accuracy and relevance of EA adoption in Malaysia’s public sector (MPS) context. Design/methodology/approach: An empirical study was conducted through an online survey in Malaysia. Based on 255 samples from public organizations throughout Malaysia, a multigroup analysis with partial least square structural equation modeling was performed to identify the respective influencing factors for adopting EA by federal and state organizations in the Malaysian public sector. Findings: Five organizational factors (clear communication, normative pressure, expected benefit, good governance and organization size) and two environmental factors (coercive pressure and mimetic pressure) were found to be significant factors influencing adoption intention for EA. Additionally, clear communication and coercive pressure were noted to be significant factors influencing adoption for both federal and state organizations. Other significant factors for federal organizations include expected benefit and normative pressure, whereas for state organizations, another significant influencing factor was good governance. The predictive accuracy for federal organizations was slightly higher at 74.6% than that of state organizations, that is, at 65%. Research limitations/implications: These findings imply that federal and state organizations respond to different factors that motivate their intention to adopt EA. These findings guide future intentional practice and ensure greater motivation for EA adoption. Practical implications: The identification of specific factors that matter

关键词： Factor analysis

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Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 849-873页

作者： Saad Alahmari Abdulwhab Alkharashi Department of Computer Science Applied CollegeNorthern Border UniversityArar91431Saudi Arabia Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityRiyadh11673Saudi Arabia

The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital *** the development of IoT devices,huge amounts of information,including users’private data,are *** systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and *** challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for *** address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT *** learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked *** delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal *** study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)*** main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT ***,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial ***,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)*** the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be ***,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI *** simulated outcomes demonstrated that the PEFLID-CSAAI

关键词： Federated learning internet of things artificial intelligence chameleon swarm algorithm intrusion detection system healthcare IoT devices

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Statistical Analysis of CPU Power Consumption for Detecting DGA Botnet Command and Control Communication

Statistical Analysis of CPU Power Consumption for Detecting ...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Ibrahim, Zul-Azri Ismail, Saiful Adli Rahim, Fiza Abdul Abas, Muhammad Hazim bin Khairul Anuar, Muhammad Idris bin Azwan, Aiman Harith Bin Institute of Informatics and Computing in Energy College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia Faculty Artificial Intelligence Universiti Teknologi Malaysia Wilayah Persekutuan Kuala Lumpur Kuala Lumpur Malaysia Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia

ISBN: (纸本)9798331530303

Domain Generation Algorithms (DGA) Botnets represent a significant threat to network security due to their ability to mask Command and Control (C&C) communications with botmaster. Traditional detection methods like domain blacklists have struggle to keep pace with the dynamic nature of these botnets. This research explores an alternative detection approach by investigating the use of CPU power consumption as an indicator for detecting DGA botnet in Internet of Things (IoT) devices. The power consumption data was collected and analyzed from a testbed comprising infected Raspberry Pi devices with DGA-based botnets. Statistical methods that include Analysis of Variance (ANOVA) and post-hoc tests, were used to determine if significant differences in CPU power consumption exist across the botnet phases. The results show that CPU power consumption is significantly higher during both the C&C and attack phases compared to normal operation. Additionally, the attack phase exhibits greater power consumption variance, suggesting unstable resource utilization. These findings highlight the potential of using power consumption data as a key metric for identifying botnet activities in IoT systems. ©2024 IEEE.

关键词： Bot (Internet)

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Distributed intelligence for IoT-based smart cities: a survey

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Neural computing and Applications 2024年第27期36卷 16621-16656页

作者： Hashem, Ibrahim Abaker Siddiqa, Aisha Alaba, Fadele Ayotunde Bilal, Muhammad Alhashmi, Saadat Mehmood Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science Federal College of Education Zaria Kaduna Tudun Wada810282 Nigeria Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32610 United States Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smart city

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Enhanced Blockchain Scalability for IoT-based Smart Devices -A Generic Model Development 13

Enhanced Blockchain Scalability for IoT-based Smart Devices ...

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13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023

作者： Gurunathan, Mathuri Mahmoud, Moamin A. Faisal, Faisal Hadi Universiti Tenaga Nasional College of Graduate Studies Kajang Malaysia College of Computing and Informatics Universiti Tenaga Nasional Institute of Informatics and Computing in Energy Department of Computing Kajang Malaysia

ISBN: (纸本)9798350347319

Blockchain become well known as there is third-party contribution while doing the exchanges or transactions. Blockchain is the innovation for utilizing digital forms of money. It draws in the consideration of specialists and academicians, along with various highlights of Blockchain it is having the significant issue of versatility and scalability which can be classified into throughput, cost, limit, and systems networking. Scalability influences due to some different components like block size and block interval time which likewise may lessen the security. The system may get powerless against various attacks on the off chance that we indiscriminately change the scalability. To address these challenges, this paper con-ducts an intensive analysis of blockchain scalability challenges in the IoT context and subsequently proposes a generic model to enhance scalability. The investigation covers restrictions on different blockchain stages and how system parameters such as throughput, latency, block size, and network influence a peer-topper system's effectiveness, security, and energy consumption. © 2023 IEEE.

关键词： Energy efficiency

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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

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Computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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E-CommerceShield: A Framework for Enhanced Security in E-Commerce with Awareness, DNS Matching, and Blockchain Integration 30

E-CommerceShield: A Framework for Enhanced Security in E-Com...

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30th IEEE International Conference on Telecommunications, ICT 2024

作者： Belghith, Aymen College of Informatics and Computing Saudi Electronic University Computer Science Department Saudi Arabia

ISBN: (纸本)9798350356694

Security attacks on e-commerce platforms are increasingly prevalent, posing significant risks to both users and organizations. These attacks aim to cause financial harm or disrupt e-commerce services, highlighting the critical need for robust security measures. This study investigates key security attacks targeting e-commerce platforms and evaluates the efficacy of various attack detection methods. Through a comprehensive literature review and descriptive analysis, the study identifies financial frauds, phishing, and Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks as the major threats to e-commerce systems. Furthermore, the study examines different detection methods, revealing that blockchain-based security systems emerge as the most effective solution against a range of identified security attacks. Building upon these findings, this paper proposes a novel framework, dubbed e-CommerceShield, which integrates user awareness, DNS matching, and blockchain technology to enhance security in e-commerce environments. The framework prioritizes proactive security measures, empowers users with awareness and education, and leverages advanced technologies to detect and prevent security threats effectively. By adopting the e-CommerceShield framework, e-commerce platforms can fortify their security posture and safeguard the integrity of online transactions, thereby fostering trust and confidence among users in the digital marketplace. © 2024 IEEE.

关键词： Blockchain

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