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检索条件"主题词=Software Algorithms"

共 12322 条记录，以下是101-110 订阅

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Enhancing software Quality with Fault Detection and Prediction based on AI-Driven Model

Enhancing Software Quality with Fault Detection and Predicti...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Ramesh Bishukarma Sumeet Mathur Puneet Matapurkar Sandeep Gupta University of Waikato NZ – Joint Institute at Zhejiang University Hangzhou China Department of Mathematical Sciences & Computer Application Bundelkhand University Jhansi India Department of Computer Science Engineering SATI Vidisha India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Early defect detection and prediction are essential in software engineering to reduce costs and improve quality. This study presents an AI-driven approach for fault detection and prediction employing ML techniques on the CM1 dataset from NASA’s software metrics program. The data preprocessing steps include normalisation, feature selection, and the use of the ADASYN algorithm to enhance minority class detection. Faults are predicted using ML classifiers: Extra Trees (ET), Support Vector Machine (SVM), KNN, RF, MLP, and NB. F1 score, recall, accuracy, and precision are a metrics utilised to assess a models. When it comes to defect prediction, the Extra Trees Classifier (ETC) model outperforms all of the others with an accuracy91.51%, precision91.76%, and recall99%. Although the Naive Bayes model shows lower accuracy (77.61%) and efficiency, it offers a simpler approach for fault detection. Future research in this area should be guided by these findings, which highlight the promise of AI-driven approaches to enhance frameworks for software defect prediction and quality assurance.

关键词： Support vector machines Accuracy Fault detection software algorithms software quality Predictive models Nearest neighbor methods software measurement Classification tree analysis software engineering

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Ethical Challenges and Frameworks in AI-Driven software Development and Testing

Ethical Challenges and Frameworks in AI-Driven Software Deve...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Anujkumarsinh Donvir Gaurav Sharma Application Development Wayne NJ USA AI Test Automation Solution Architect Atlanta GA USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Artificial Intelligence (AI) has revolutionized and transformed the landscape of software development and testing by introducing new efficiencies and capabilities through advancements like Generative AI (GenAI) and Large Language Models (LLMs). While these technologies bring major benefits in terms of productivity, personalization, and innovation, they also raise critical ethical challenges, such as biases, lack of transparency, data privacy concerns, and potential negative societal impacts. This paper examines the ethical considerations involved in developing such advanced AI systems as well using AI systems within software development and testing. It explores existing ethical frameworks and principles provided by leading organizations, emphasizing core concepts like human-centered design, accountability, transparency, fairness, and privacy. Practical strategies for integrating ethical practices throughout the AI development lifecycle are discussed, with a strong emphasis on the need for continuous ethical evaluation. The paper explores the ethical landscape of AI in software development, addressing challenges like algorithmic bias, data security, and broader societal impacts. Real-world case studies presented in the paper demonstrate the consequences of neglecting ethical considerations. Looking forward, the paper suggests future directions, including the development of unified ethical standards, collaborative ethical auditing, regulatory advancements, and higher societal engagement.

关键词： Ethics Technological innovation Data privacy Standards organizations software algorithms Collaboration Stakeholders Artificial intelligence software development management Testing

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HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning software Defect Prediction Framework

HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Software...

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International Conference on Information Networking

作者： Madhusmita Das Biju R. Mohan Ram Mohana Reddy Guddeti Department of Information Technology National Institute of Technology Karnataka Surathkal Mangalore India

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Defect prediction plays a crucial role for any software system across various domains, as its failure may cause unavoidable and undeniable scenarios. For reliable software, defect-free is considered as one of the most important criteria. This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. We strive to do this by designing a swarm-optimized machine learning defect prediction (SoMLDP) model within the HSoMLSDP framework. In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper introduces a hybrid swarm optimization algorithm (SOA) referred to as the gravitational force Lévy flight grasshopper optimization algorithm-artificial bee colony (GFLFGOA-ABC) algorithm. By combining the enhanced exploration feature of the gravitational force Lévy flight grasshopper optimization algorithm (GFLFGOA) with the robust exploitation capacity of the artificial bee colony (ABC), the GFLFGOA-ABC algorithm is proposed. Prior to validating the HSoMLSDP framework, the LFGFGOA-ABC algorithm’s performance is first confirmed by experiments on 6 benchmark functions (BFs) to assess its mean and convergence rate. Following BF verification, the second experiment tunes the hyperparameters of ML models (ANN, GB, XGB) to improve the defect accuracy of the SoMLDP model. As an enhancement of accuracy justifies the correctness of the SoMLDP model, thus validating the HSoMLSDP framework.

关键词： Machine learning algorithms Accuracy software algorithms Machine learning Predictive models Benchmark testing Prediction algorithms software systems Optimization Gravity

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A Comprehensive Evaluation of Machine Learning Techniques for Predicting software Bugs

A Comprehensive Evaluation of Machine Learning Techniques fo...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Fahim Faysal Sakib Md. Sajjadul Islam Juel Zinniya Tafannum Jihad Shahriar Joy Razuan Karim Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

software Bug Prediction (SBP) is essential to control overall software quality. As systems become complex, faults increase, and early prediction is needed to enhance quality and reduce expenses. This paper provides a technical study of thirteen Machine Learning (ML) techniques for predicting software bugs by utilizing NASA's Promise Repository datasets. The study examines the effectiveness of these classification techniques focusing on evaluation metrics such as Precision, Recall, F-measure, Accuracy, Error Rate, FPR, FNR, ROC, and MCC. The results indicate that Random Forest (RF) achieved the highest accuracy on PC1 (94.96%) and CM1 (96.60%), while Multilayer Perceptron (MLP) scored 85.6% on KC1, and Logistic Regression (LR) got 86.25% on KC2. Regarding error rates, RF had the lowest on PC1 (0.050), LR on KC2 (0.137). For FPR, SVM, MLP, LR, RBF, ANN, and AdaBoost excelled on PC1 (0.00). For FNR, Decision Tree (DT) performed best on PC1 (0.611), KC2 (0.518), and LDA on CM1 (0.500).

关键词： Radio frequency Support vector machines Measurement Accuracy Error analysis Computer bugs software algorithms Signal processing algorithms Prediction algorithms Random forests

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Optimum Accuracy of software Defect Prediction using Machine Learning Technique

Optimum Accuracy of Software Defect Prediction using Machine...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Mohini Parashar Arjun Rajput Rajesh Boghey Department of CSE TIT-Excellence Bhopal

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Focus of the paper is on predicting software defects (SD) based on Machine learning (ML) techniques which is a challenging research area because of unbalanced nature of data sets. In general, a set of design metrics is used for fault prediction and for identifying the fault-proneness in software and recent studies shows that ML techniques is being applied for defect prediction. However, some ML techniques cannot produce the needed results when dealing with unbalanced dataset and the results produced are not certainly inferred by the developers by observing these factors, we propose a unique approach for fault prediction which is based on feature selection technique which improves the overall performance by using attribute selection when predicting defects. The ML concentrates on the algorithms entirely centred on statistical methods and data mining techniques for classifying and predicting the defects and these statistical methods followed are quite similar to regression based methods which we used earlier to the ML. When more data is available, ML algorithms behave as dynamic algorithms to improve their performance significantly. The DT ML technique is providing good accuracy compared to other NB and SVM technique. In this model is simulated python language and calculated simulation parameter i.e. precision, recall and accuracy.

关键词： Support vector machines Measurement Accuracy Statistical analysis Heuristic algorithms software algorithms Machine learning Prediction algorithms software Classification algorithms

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Information and software Support for the Multifactor Authentication Process using Biometric Data in Linux Kernel-Based Operating Systems

Information and Software Support for the Multifactor Authent...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Alexander A. Zakharov Andrey M. Shabalin Roman V. Tikhonov Department of Smart City Technologies Information Security Tyumen State University Tyumen Russia Information Security Department Tyumen State University Tyumen Russia Department of information technology Akvatechnik Tyumen Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article substantiates the necessity of of implementing multifactor authentication based on user biometric data to enhance the security level of computer systems. Traditional methods such as password authentication are not always capable of providing the required level of protection for the account and user data. The authors analyze modern approaches to biometric authentication, identify the advantages of using them within multifactor systems, and address current issues related to the secure storage of biometric data. As a result of their research, they have developed a software architecture that uses the proposed mechanisms for integration in Linux kernel-based operating systems.

关键词： Biometrics software architecture Linux software algorithms Authentication Personal voice assistants Protection Open source software Secure storage Testing

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Key Applications and Challenges of Virtual software Simulation Technology in Data Resource Searching Schemes

Key Applications and Challenges of Virtual Software Simulati...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Jiao Yueyue Shandong Institute of Commerce and Technology Jinan Shandong China

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

With the continuous development of big data and complex software systems, virtual software simulation technology plays an increasingly important role in the fields of data resource search and security prevention applications. Inspired by this, this study first analyzes the basic principles of virtual software simulation technology, and proposes a new virtual simulation platform model by combining resource allocation strategy, search algorithm optimization and real-time interactivity, and conducts systematic extensibility research from the perspective of DDos detection. At the practical level, this study introduces a virtual simulation model based on the VERICUT platform to analyze the complex data search process and improve the modeling. The core methods of this research framework include the following three aspects: 1) Proposing a new collaborative management model for automated testing based on data flow; 2) Performing coverage testing of multi-dimensional data input and intermediate state parameterization at the practical level; 3) Proposing a new DDoS attack analysis and detection algorithm as an extension study. From the experimental results, it can be seen that in 50 sets of independent repeated tests, the DDos detection accuracy of the proposed algorithm is stable between 98% and 100%, with a small fluctuation rang. This shows the stability of the proposed model.

关键词： Analytical models Accuracy software algorithms Data models Stability analysis Real-time systems Security Resource management Optimization Testing

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Efficient Load Balancing in software Defined Networks Using Unsupervised Meta-Reinforcement Learning

Efficient Load Balancing in Software Defined Networks Using ...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Prerita Kulkarni Nitika Vats Doohan Department of Computer Science Medicaps University Indore India Department of Computer Science Sage University Indore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

New technologies, such as cloud computing and big data, make traditional network management more difficult, while the network's user base continues to expand. Consequently, changes to the traditional network architecture are required. To address this issue and enhance network management compliance, a new idea called software-defined network (SDN) has been proposed. Due to limited network resources and to satisfy the criteria of quality of service, it is necessary to address numerous difficulties, one of which is load balancing (LB), which distributes data traffic among several resources to maximize their efficiency and reliability. The local information of a network is used to design LB in a typical network. Consequently, it is not very accurate. Still, software-defined network (SDN) controllers can see the network from anywhere in the world and make better LB. We provide a set of unsupervised meta- RL algorithms to build smart optimization architecture for SDN LB.

关键词： Uncertainty software algorithms Computer architecture Telecommunication traffic Load management Inference algorithms Reliability software defined networking Optimization Standards

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Improving software Defects Detection: An In-Depth Analysis of Machine Learning Methods and Static Analysis Tools for Greater Accuracy

Improving Software Defects Detection: An In-Depth Analysis o...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Veer Bobade Chetan Puri Department of Artificial Intelligence and Machine Learning Faculty of Engineering And Technology DMIHER Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering And Technology DMIHER Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Enhancing software quality and reducing testing expenses requires better software fault detection. To guarantee the dependability and usability of software, software Defect Prediction (SDP) uses machine learning approaches to anticipate and address any problems early in the development cycle. Numerous machine learning techniques, such as Support Vector Machines (SVM), Random Forest (RF), Logistic Regression, Naïve Bayes, Multilayer Perceptron (MLP), Decision Stump, J48, Lazy IBK, and ZeroR, are thoroughly examined in this work. With an emphasis on feature selection as a crucial step in increasing prediction accuracy and lower computing costs, these approaches are assessed for their capacity to detect software flaws precisely. This research study demonstrates how these models may improve fault identification and prevention procedures using historical records from sources such as the PROMISE repository. The study highlights the best practices for machine learning-based software quality assurance, lowering expenses while guaranteeing superior results.

关键词： Support vector machines Accuracy Machine learning algorithms software algorithms Machine learning software quality Multilayer perceptrons Feature extraction Prediction algorithms Random forests

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Development of Signal Detection and Recognition Algorithm Using software-Defined Radio

Development of Signal Detection and Recognition Algorithm Us...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Anastasia O. Khodakova Yulia D. Naumova Sergey A. Chechenya Sergey V. Soldatkin Anton Y. Boev Konstantin V. Vorontsov Department of Formation and Processing of Radio Frequency Signals National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article presents an analysis of the existing algorithms used in the implementation of a receiver of multi-position signals based on SDR technology. As a result, a comprehensive approach has been developed to solve the extended task of radio monitoring, which includes not only scanning the spectrum, detecting radio emissions, determining the type of modulation and the location of the source, but also decoding the data contained in the message. To implement the SDR-based algorithm, the GNU Radio program was used, which is convenient because it allows you to build circuits using software modules.

关键词： software algorithms Circuits Signal processing algorithms Receivers software Decoding Synchronization Monitoring Signal detection software radio

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