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检索条件"主题词=software algorithms"

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Optimization Algorithm for Prioritizing software Requirements: A Comparative Study

Optimization Algorithm for Prioritizing Software Requirement...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Moh. Zulfiqar Naufal Maulana Daniel Siahaan Ahmad Saikhu Evi Triandini Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang Indonesia Department of Information System Institut Teknologi dan Bisnis STIKOM Bali Denpasar Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

software requirement prioritization is a critical step in software development, as it directly impacts project efficiency and success. Despite the availability of various prioritization techniques, there remains a gap in directly comparing optimization algorithms for this purpose. This study addresses this gap by evaluating three widely-used metaheuristic-based optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). Using a dataset enriched with attributes such as cost, value, prerequisites, and test cases, the algorithms were analyzed based on accuracy, execution time, and computational complexity. The results show that GA and PSO demonstrate comparable accuracy (82%) and efficiency, with GA exhibiting faster execution time (0.822s) compared to PSO (1.324s). GWO, while slightly less accurate (81%) and slower (3.017s), offers unique advantages in specific optimization contexts. This study provides actionable insights for software engineers in selecting appropriate algorithms to optimize resource allocation and enhance decision-making during requirement prioritization.

关键词： Seminars Accuracy software algorithms software Resource management Particle swarm optimization Time complexity Optimization Genetic algorithms software development management

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Systematic Literature Review of software Effort Estimation : Research Trends, Methods, and Datasets

Systematic Literature Review of Software Effort Estimation :...

引用

International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Hariyanto Aris Marjuni Nova Rijati Zainal Arifin Hasibuan Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

Without sufficient effort, the development of a software project will be hindered and may even significantly fail, thus jeopardizing the quality of the software developers. Therefore, software Effort Estimation (SEE) is a crucial activity in software engineering. One of the most important tasks in software engineering is software effort estimation (SEE). SEE has been the subject of several investigations, which have produced a substantial body of new literature quickly. Determining the primary research topics is essential to get a thorough grasp of the frameworks and trends in software Effort Estimation (SEE) research today. A systematic literature review (SLR) was utilized in this study as an all-encompassing method to identify and examine every significant research issue in this field. Three research topics were identified from 58 journals, including algorithmic techniques, machine learning implementation, and dataset analysis. The algorithmic techniques and machine learning approaches are the most frequently discussed topics.

关键词： Support vector machines Adaptation models Machine learning algorithms Accuracy Systematics software algorithms Estimation Market research software software engineering

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Comparison of Novel Random Forest Algorithm over Gaussian Naïve Bayes for Improving Accuracy in software Bug Prediction System

Comparison of Novel Random Forest Algorithm over Gaussian Na...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Ks Bharath G Anitha Dept of CSE Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research work focuses on analyzing the performance of a proposed random forest (RF) method with that of Gaussian Naive Bayes in predicting software problems. The database utilized in this research was gathered from the Kaggle repository and contained information about software projects and their bug reports. A total of 20 samples were used in this study, with two groups formed and 10 samples randomly selected for each group. Group 1 was used for training and testing the Gaussian Naive Bayes algorithm, while Group 2 was used for training and testing the novel RF algorithm. Python and relevant libraries such as scikit-learn were utilized for implementation. The dataset was preprocessed to remove irrelevant features and perform feature engineering. The algorithms were tested on the training set, and their performance was assessed using accuracy metric on the testing data. A statistical power of 0.8 was set, and alpha and beta qualities of 0.05 and 0.2, respectively, were used with a confidence interval of 95%. The accuracy of the novel RF approach was 78.59% on average, whereas the accuracy of the Gaussian Naive Bayes approach was 75.57% on average. The p-value for the statistically distinct in accuracy among the both approaches was 0.001 ($\mathbf{p}\lt0.05$). The findings suggest that the novel RF approach could be a useful tool for software developers and project managers to predict and prevent potential bugs in their projects.

关键词： Radio frequency Training Accuracy Computer bugs software algorithms software Naive Bayes methods Random forests Testing Python

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Examine of Algorithmic Approaches to software Security

Examine of Algorithmic Approaches to Software Security

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Rajesh Sharma R Akey Sungheetha Prem Kumar M Ellappan. V Rajiv Gandhi K Priyatharsini C Department of CSE Alliance University Bangalore India Department of IT Mahendra Institute of Technology Namakkal Tamil Nadu Department of ECE Mahendra Institute of Technology Namakkal Tamil Nadu Govt. Arts and Science College for Women Parmakkudi India Department of CSE Mahendra Engineering College Namakkal Tamilnadu

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

The complexity of threats in the virtual world is growing, so new solutions are required for software protection. Artificial Intelligence has become a leading factor in strengthening software security against constantly changing threats. This extensive review synthesizes the current progress and trends for algorithmic AI methodologies in context of software protection. Specifically, we discuss requesting machine learning techniques and neural networks used for threat detection, vulnerability assessment, and adaptive defense mechanisms as well as using evolutionary algorithms. From this, we can deduce that security systems that AI enhances are much more flexible and efficient than traditional security. Zero-day vulnerabilities can be detected by deep learning models with high accuracy and reinforcement learning algorithms are effective in real-time threat handling. AI opens up opportunities in the software security domain as a predictor of vulnerability, yet there are shortcomings related to model interpretability and adversarial robustness. More detailed studies are needed to work on the more transparent models of artificial intelligence and to incorporate those into existing security systems.

关键词： Machine learning algorithms software protection software algorithms Signal processing algorithms Reinforcement learning Prediction algorithms Threat assessment Robustness Real-time systems Security

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Use of Generative Artificial Intelligence in the Education of software Verification and Validation

Use of Generative Artificial Intelligence in the Education o...

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Frontiers in Education (FIE) Conference

作者： Ayca Tuzmen Ira A. Fulton Schools of Engineering Arizona State University Tempe Arizona

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

Since the introduction of generative artificial intelligence (GenAI), education in computer science has prompted efforts to incorporate it into the educational curriculum. This innovative practice full paper presents a study into using GenAI to enhance student learning of software engineering. It outlines the initiatives to introduce GenAI into a graduate-level software engineering course in software Verification and Validation (SV&V). The paper presents the educational goals, methodologies and findings of these endeavors in this course. The primary education goal of this course is that students have a solid understanding of principles and practices of software quality assurance and seek to introduce students to diverse techniques employed for SV&V. The study presented in this paper centers on the practical application of GenAI within the domain of testing strategies. The paper introduces the findings of an exercise where GenAI was used to apply testing strategies for unit testing. The exercise consisted of the use of GenAI in the development of unit tests for an algorithm. Rigorous assessments were conducted to gauge the effectiveness of the unit tests developed for validating the accurate implementation of the algorithm. This exploration shed light on the tangible impact of GenAI on the precision and efficiency of unit testing procedures. The findings underscore the significance of encouraging students to actively explore emerging trends and methodologies in the realm of software verification and validation. By incorporating GenAI into the educational framework, students not only gain insights into the capabilities and limitations of this technology but also foster a mindset of continuous learning in software quality assurance. The paper demonstrates that it is not sufficient to use the test cases developed by GenAI for software validation since test cases recommended by GenAI do not cover corner cases which causes gaps in coverage in unit testing. The majority of

关键词： Technological innovation Codes Generative AI Education software algorithms software quality Solids Problem-solving Testing software engineering

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YOLOX Algorithm Driven by software and Hardware Collaboration Domestic Waste Intelligent Identification and Accurate Sorting Robot System Design

YOLOX Algorithm Driven by Software and Hardware Collaboratio...

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Service Robotics (ICoSR), International Conference on

作者： Chunxiang Zhu Yuanbin Mu Hong Shu Yinglian Jin College of Engineering Training Centre China Jiliang University Hangzhou China School of Internet of Things Technology Hangzhou Polytechnic Hangzhou China College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China

ISBN: (数字)9798350367119

ISBN: (纸本)9798350367126

Based on the artificial intelligence computing platform Jetson NANO and YOLOX algorithm, an intelligent and efficient domestic waste sorting system based on YOLOX-NANO is designed. The lightweight YOLOX multi-target detection algorithm is used to achieve accurate identification of four types of domestic waste under occlusion, and based on the CoreXY coordinate transformation algorithm to achieve the best angle judgment and accurate grasping of the mechanical claw; it is implemented using TensorRT optimization technology The lightweight YOLOX multi-target detection algorithm was deployed on the artificial intelligence computing platform Jetson Nano, and the overall construction of an intelligent and efficient sorting system for four types of garbage based on YOLOX-NANO was completed. This system uses a self-made data set for training. The data set includes 4 categories of garbage and a total of 13 subcategories. After testing, it can effectively achieve multi-target detection. The detection mAP0.5:0.95 value is 97.37%, and the target detection speed reaches 30.60FPS, the garbage sorting speed can reach up to 6 pieces/minute. Can be used for clean, environmentally friendly smart garbage cans.

关键词： Training Accuracy Service robots software algorithms software Detection algorithms Artificial intelligence System analysis and design Sorting Testing

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Development and Research of English Defect Reporting software Based on Natural Language Processing

Development and Research of English Defect Reporting Softwar...

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Computers, Information Processing and Advanced Education (CIPAE), 2020 International Conference on

作者： Siyu Wei Xi'an Eurasia University Xi'an Shaanxi China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This paper aims to explore an innovative English defect reporting software that utilizes natural language processing (NLP) techniques, particularly hybrid neural networks and attention mechanisms, to improve the accuracy and readability of English defect reporting. The research focuses on English pronunciation defect recognition and aims to provide software developers with an intelligent defect report writing and review tool by integrating advanced algorithms and simulation techniques. Firstly, this study proposes an English pronunciation defect recognition model based on hybrid neural networks and attention mechanisms. The model uses deep learning algorithms to automatically identify and correct pronunciation errors in reports, while using attention mechanisms to improve the ability to capture key information in the text. This integrated approach effectively improves the language quality of defect reporting and reduces the communication barriers caused by language problems. Secondly, a comprehensive natural language processing system is developed, which not only includes the pronunciation defect recognition module, but also includes grammar checking, vocabulary suggestion and context understanding. Through the organic combination of these functions, the system can provide users with a full range of language support to ensure the professional and clear defect report. Finally, the simulation experiment of the system algorithm is carried out to evaluate its performance in practical application. The experimental results show that the developed system performs well in identifying pronunciation defects and providing language recommendations, significantly improving the quality of defect reporting.

关键词： Vocabulary Attention mechanisms Accuracy software algorithms Neural networks software Natural language processing Grammar software reliability software development management

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Research on software Supply Chain Security Assurance Mechanism System Based on Blockchain Technology

Research on Software Supply Chain Security Assurance Mechani...

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Signal Processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Zhu Lijin Guangzhou Huashang Vocational College Guangzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This research introduces a security assurance mechanism for the software supply chain, leveraging blockchain technology to address existing vulnerabilities within the supply chain. The inherent decentralized and tamper-resistant features of blockchain offer robust safeguards for data exchanges across various stages of the supply chain. The system incorporates an enhanced Byzantine fault-tolerance algorithm to improve the network's resilience, ensuring that supply chain nodes can maintain efficient operation even in complex and challenging environments. For critical security aspects within the supply chain, specialized algorithms and models have been devised, and the system's security and performance are validated through simulation models. The simulation outcomes demonstrate that the system exhibits strong resilience against attacks and potential threats to the software supply chain, while also maintaining high operational efficiency alongside its security measures. Data analysis reveals that the implementation of this security assurance mechanism significantly elevates the overall safety and stability of the software supply chain. The findings of this study provide a theoretical foundation and technical framework for enhancing security assurance capabilities in software supply chains.

关键词： Fault tolerance Supply chains software algorithms Fault tolerant systems Signal processing algorithms software Stability analysis Blockchains Security Resilience

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Development of Algorithmic and software Hardware for the Control System of a Bionic Walking Robot

Development of Algorithmic and Software Hardware for the Con...

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International Russian Automation Conference (RusAutoCon)

作者： Daniyar A. Wolf Rinat R. Galin Yaroslav A. Turovsky Laboratory of Cyber-Physical Systems V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The paper considers the topical issue of the development of bionic walking robots of the Felida class. Information is provided on the research work carried out and the experimental design development of an interactive bionic robot. The purpose of this work is to develop approaches to creating a mobile robotic system that moves in steps, which allows it to move through an unprepared area in advance. The object of the study is a bionic walking robot developed by the authors. The multilevel structure of the walking robot control system is presented in this paper. The analysis of the results of the study of the biological characteristics of felid was carried out and the control elements subject to bionic synthesis were identified. The paper reflects the comparative characteristics of the developed bionic walking robot and its analogue. There is a model of a bionic walking robot and the kinematics of its supports. The software model of the robot consider interaction between control and actuators of the robot on the ROS platform, as part of the links between publisher and subscriber nodes. The hardware architecture organized according to the principle of a two-level control architecture. The first hardware and software level (the control body of the first level) and the second level implements the interface for interaction of application services with the subsystem of the first level. This paper can be useful for both educational and research tasks in the field of developing bionic walking robots.

关键词： Legged locomotion Biological system modeling software algorithms Robot control Computer architecture Open systems Maintenance engineering software Hardware Robots

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Computer-Aided Audit software Based on Fuzzy Clustering Algorithm

Computer-Aided Audit Software Based on Fuzzy Clustering Algo...

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Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Yuan Fen Wuhan Qingchuan University Wuhan City Hubei Province China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

According to the needs of auditing work, efforts are made to develop suitable computer-aided auditing software to gradually change or enhance traditional auditing techniques and methods, which can better play the role of audit supervision. The purpose of this paper is to study the design and optimisation of computer-aided audit software based on fuzzy clustering algorithms. By analysing the technical difficulties of computer-aided auditing, this paper proposes the design concept of the project software. The improved fuzzy C-mean clustering algorithm DFCM and the fast audit steps of the project based on improved fuzzy clustering are specifically introduced, and the data processing methods of the system are described in detail, and the specific application of the project software is also presented. The experimental results show that the computer-aided audit software in this paper can be applied to calculate the audit price of the project to be audited and the software is feasible.

关键词： Image processing software algorithms Clustering algorithms Quality control Data warehouses Data processing software Protection Expert systems Optimization

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