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44th ACM/IEEE international conference on software engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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Multi-Task Chinese Speech Recognition Method Based on the Squeezeformer Model

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第1期52卷 23-31页

作者： Guo, Ying Wang, Li School of Computer Science and software Engineering University of Science and Technology Liaoning Anshan114051 China College of Computer Science and Technology Liaoning Anshan114051 China

End-to-end training has emerged as a prominent trend in speech recognition, with Conformer models effectively integrating Transformer and CNN architectures. However, their complexity and high computational cost pose deployment challenges. To address these issues, we propose a multi-task Chinese speech recognition method based on the Squeezeformer model. We replace the FMCF structure in Conformer with an MF/CF structure, leveraging the convolutional module as a local Multi- Head Attention (MHA) module to enhance efficiency. Multilevel down-sampling and up-sampling using a time-series U-Net further reduce computational costs. By eliminating redundant LayerNorm layers and employing depthwise separable convolutions, we streamline the model, reduce parameters, and lower deployment costs. An Adaptor Layer is integrated into the MHSA module to mitigate the vanishing gradient problem, and a ScaleVar Layer is added to enhance flexibility. Additionally, the RealFormer module is introduced on the decoding side to improve context understanding. Combining Connectionist Temporal Classification (CTC) with attention-based encoding and decoding models for multi-task learning improves performance and accuracy. Experimental results show that the proposed method reduces the parameters on AISHELL-1 dataset by 16% and reduces the character error rate to 5.50%. At the same time, it also shows good performance on AISHELL-2 dataset. © (2025), (international Association of Engineers). All rights reserved.

关键词： Speech recognition

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Enhancing Supply Chain Efficiency with IoT and Data Analytics

Enhancing Supply Chain Efficiency with IoT and Data Analytic...

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2024 IEEE international conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Jayalakshmi, D. Vimal, V.R. Loganayagi, S. Narayanan, Lakshmi Kanthan Hemavathi, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Thandalam Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

This study aims at exploring the use of internet of things technology and data analytics in the field of supply chain management. Using a range of software tools, including Apache Kafka, Apache NiFi, Python libraries such as Pandas, and Scikit-learn, the study accumulated, assimilated, and quantified data from IoT sensors, supply chain systems, and qualitative interviews. The primary data sources are primary supply chain data history and IoT sensor data while secondary data includes academic and industrial journals, and reports. This paper presents a number of basic quantitative measures showing the general performance indicators: the average percentage of order fulfilment as 92.5% and the inventory turnover ratio equalling 6.2. When using linear regression, random forest, and LSTM neural network, the models reveal the mean absolute errors of demand forecasting between 80 and 120 units. The results suggest that IoT and data analysis offer significant opportunities to create value through the integration of SC, cost cutting, and higher customer satisfaction. It has now become essential for organizations to effectively apply data trends to enable competitive gains within the complex business setting to foster operational efficacy and stability. © 2024 IEEE.

关键词： Customer satisfaction

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Designing for Cognitive Diversity: Improving the GitHub Experience for Newcomers 23

Designing for Cognitive Diversity: Improving the GitHub Expe...

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45th IEEE/ACM international conference on software engineering - software engineering in Society (ICSE-SEIS)

作者： Santos, Italo Pimentel, Joao Felipe Wiese, Igor Steinmacher, Igor Sarma, Anita Gerosa, Marco A. No Arizona Univ Flagstaff AZ 86011 USA Fed Univ Technol Campo Mourao PR Brazil Oregon State Univ Corvallis OR USA

ISBN: (纸本)9798350322613

Social coding platforms such as GitHub have become defacto environments for collaborative programming and open source. When these platforms do not support specific cognitive styles, they create barriers to programming for some populations. Research shows that the cognitive styles typically favored by women are often unsupported, creating barriers to entry for woman newcomers. In this paper, we use the GenderMag method to evaluate GitHub to find cognitive style-specific inclusivity bugs. We redesigned the "buggy" GitHub features through a web browser plugin, which we evaluated through a between-subjects experiment (n=75). Our results indicate that the changes to the interface improve users' performance and self-efficacy, mainly for individuals with cognitive styles more common to women. Our results can inspire designers of social coding platforms and software engineering tools to produce more inclusive development environments. General Abstract-Diversity is an important aspect of society. One form of diversity is cognitive diversity-differences in cognitive styles, which helps generate a diversity of thoughts. Unfortunately, software tools often do not support different cognitive styles (e.g., learning styles), disproportionately impacting those whose styles are not supported. These individuals pay a cognitive "tax" each time they use the tools. In this work, we found "inclusivity bugs" in GitHub, a social coding platform. We then redesigned these buggy features and evaluated them with users. Our results show that the redesign makes it easier for the group of individuals whose cognitive styles were unsupported in the original design, with the percentage of completed tasks rising from 67% to 95% for this group.

关键词： open source diversity and inclusion human factors cognitive styles human-computer interaction

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Development of a Low-Cost Smart Embroidery Conversion Kit 11

Development of a Low-Cost Smart Embroidery Conversion Kit

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11th international conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Silvano, Rico Mercolita, Abigail Joy Nillo, John Marlo Panga, Bethany Ann Rellas, Rose Ann Loquias, Rizza T. Cerillo, Jose Eduardo College of Engineering & Architecture Camarines sur Polytechnic Colleges Nabua Philippines

ISBN: (纸本)9798350355314

The increasing demand for embroidered garments confronts MSEs in developing countries, which have limited access due to the high cost of embroidery machines and digitizing software. This paper describes developing a detachable embroidery conversion system utilizing Raspberry Pi and Arduino combo for the control system and a Python-based digitizing software. A CNC plotter-like X-Y table with an embroidery hoop and externally geared Z-axis attachment (32:12 ratio) was designed for embroidery conversion and a darning foot for multi-directional sewing. The embroidery system includes a web-based digitizing software with 3-step Python parsing that converts images to embroidery G-Code. The assessment of the developed embroidery system in terms of accuracy was done on closed squares with varying contour widths. Analysis of all 16 segments per design revealed an accuracy of 89.28%, revealing a negative correlation between contour width and accuracy. Wider contours showed decreased accuracy compared to narrower ones, highlighting the significant influence of contour width on the final embroidered shape. This embroidery system is a low-cost alternative to conventional machines and digitizing software subscription fees. The detachable embroidery system's low cost and expanded functionalities position it as a potential driver for adopting embroidery throughout the textile value chain in domestic fabric and apparel MSEs. © 2024 IEEE.

关键词： Python

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Enhancing software Test Effort Estimation using Ensemble Learning Algorithms 4

Enhancing Software Test Effort Estimation using Ensemble Lea...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Venkatraman, K. Akashvarma, M. Siddharth, S. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350349900

In software engineering, accurate test effort prediction is critical to project schedule and resource efficiency. Conventional approaches, which depend on past performance or expert opinion, frequently fail to provide a reliable estimate of the testing effort. These approaches might offer some insight at first, but as project complexity rises, they become less scalable and more error-prone. This study explores the use of machine learning to improve the estimation of software test effort in response. This work intends to overcome the shortcomings of conventional methods and create more accurate and scalable solutions by utilising the power of machine learning algorithms. The goal of the research is to determine the best machine learning strategy for software test effort estimation through extensive testing and comparative analysis. The results of this study are compared using metrics such as RMSE, MSE, MAE, and R2 score, with Random Forest algorithm demonstrating superior performance over other algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Examining the Role of Guardian Communications software Design for Network Security 15

Examining the Role of Guardian Communications Software Desig...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Chaudhari, Madhuri Harshal Verma, Ashish Dhabliya, Dharmesh Kumar, L.Vinoth Jaison, B. Sunrise University DepartmenT of Computer Science & Engineering Alwar301028 India Dr.D.Y.Patil Science and Computer Science college Department of Computer Science pune Akurdi India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Technology University Department of Information Chennai India R.M.K.Engineering College Department of Computer Science and Engineering Kavarapettai601206 India

ISBN: (纸本)9798350370249

Network protection is a crucial region of generation and PC technology, and numerous equipment and techniques help ensure comfy conversation and records switch. Mum or Dad Communications software program (GCS) is a product suite that gives customers an answer for securing their networks. GCS offers an interactive person interface to facilitate configuration, analysis, and optimization of a community's safety architecture. The suite carries three core components, supplying an excellent and specialized feature. These additives are the mum or dad Firewall, the guardian Host Intrusion Prevention System (HIPS), and the father or mother community protection appliance (NSA). Each component works independently and in concert with each other to provide comprehensive network security. The Guardian Firewall is an application-level firewall that provides a secure edge to the network by controlling all incoming and outgoing traffic and packet communications. It dynamically updates its rules database to recognize and block malicious traffic while allowing access to known secure applications and services. The Guardian HIPS is an internal network monitoring system that can detect and respond to unauthorized intrusions while providing the administrator with real-time alerts and intelligence about the nature of any malicious activity. Finally, the Guardian Network Security Appliance is a hardware-based appliance that can be configured with firewalls and. © 2024 IEEE.

关键词： Communications Dynamically Guardian Security Unauthorized

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Research on Aerodynamic Layout System of Small UAV Based on computer Intelligent Flight Control software 3

Research on Aerodynamic Layout System of Small UAV Based on ...

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3rd IEEE international conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Wei, Dechen Fan, Yukun Zhao, Xiaoyang College of Electromechanical Engineering Binzhou University Shandong Province Binzhou City China

ISBN: (纸本)9798350380989

In this paper, the one-click full-function detection scheme is adopted to realize the comprehensive test method of the flight control system. Firstly, the hardware design of interface board, power board, signal adjusting board and test board is completed based on 586 driver board. Then the layered software architecture is given. The method adopts the modular design idea and completes the task decomposition. Then the communication mode based on ORB is proposed, and the idea of state machine is used to deal with emergencies. The system has the functions of bottom drive, timing control, power-on control, current detection, function detection and so on. It can detect the flight control computer automatically according to the preset timing logic without human intervention. Four different test cases are presented. The results of computer simulation verify that the chosen test route is consistent with the real one. The experiment proves that the scheme can complete the reliability test of the system well. © 2024 IEEE.

关键词： aerodynamic layout Automatic detection autonomous flight drones signal threshold detection

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Proposed Multi-Pathization Method with SDN for Multiple Paths for TCP Throughput 11

Proposed Multi-Pathization Method with SDN for Multiple Path...

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11th international conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Kobayashi, Yuuki Ito, Yoshihiro Fujikami, Ryotaro Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (纸本)9798350355314

In this paper, we propose an innovative method to control the increasing traffic volume caused by the proliferation of IoT devices, utilizing software Defined Networking (SDN). Our method leverages a genetic algorithm, a powerful numerical optimization technique, to determine the optimal forwarding probability that maximizes Quality of Service (QoS) across the network. We experimentally evaluate the TCP throughput of the proposed method under various conditions in a multi-route network topology. The results of these experiments confirm the effectiveness and robustness of our scheme, showing significant improvements in network performance compared to traditional methods. This approach is particularly valuable for future network environments where IoT devices are expected to dominate, providing a scalable solution to manage network congestion and maintain high QoS standards. The potential applications of our method extend to various IoT-driven industries, making it a crucial tool for network administrators in the era of ubiquitous connectivity. © 2024 IEEE.

关键词： Genetic algorithms

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Blessing or Curse? Investigating Test Code Maintenance through Inheritance and Interface 40

Blessing or Curse? Investigating Test Code Maintenance throu...

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40th international conference on software Maintenance and Evolution

作者： Kim, Dong Jae Chen, Tse-Hsun (peter) Depaul Univ Chicago IL 60604 USA Concordia Univ Montreal PQ Canada

ISBN: (纸本)9798350395693;9798350395686

Since the advent of object-oriented programming languages, inheritance and interface have been fundamental concepts in software design principles, facilitating code reuse and extensibility in software systems. Despite their potential benefits, inheritance, and interface remain underexplored in software test code. Currently, there is a limited established standard for how inheritance and interface may impact test reusability, extensibility, and maintainability, nor for understanding the potential design challenges that may arise from improper usage. Addressing these research gaps is crucial for optimizing test maintainability and software quality. In this paper, we address this gap in empirical research by conducting the first comprehensive study on the prevalence and maintenance of inheritance and interface within test code. To accomplish this goal, we use RefactoringMiner's AST differencing API to detect inheritance and interface changes in modified test classes within the software evolution commit history by studying 12 open-source Java systems. Our key findings are as follows: (1) Among the 23,651 commits that modify test classes, 4,429 (18%) involve changes to their inheritance relationships, whereas a significantly smaller subset, 233 (1%), pertain to changes in their interface relationships. (2) 59.5% of test classes already incorporate inheritance when initially created, while 40% of test classes incorporate interfaces. (3) We manually categorized the use of inheritance and interfaces and their impact on test maintainability to provide valuable insights for developers. In summary, this study takes the first step in exploring how the use of inheritance and interfaces in test code affects software reusability and extensibility, offering meaningful insights for both developers and researchers

关键词： Mining software Repository software Evolution software Maintenance Test Maintenance

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