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Predicting software Reusability Using Classification Techniques

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Predicting Software Reusability Using Classification Techniq...

International Conference on Information and Communication Systems (ICICS)

作者： Neda’a Alsalman Raed Shatnawi Ammar Jalamneh Omar AlZoubi Computer Science Jordan University of Science and Technology Irbid Jordan Software Engineering Department Jordan University of Science and Technology College of Arts & Science Applied Science University Bahrain

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

This study predicts software reusability at the class level using machine learning and a dataset of 65 Java applications. The reuse rates were calculated using cohesion, coupling, complexity, inheritance, documentation, and size metrics. The data was segmented by project size (all, small, medium, and big). Several classifiers were tested once the labels were changed to multi-class labels (low: 0, moderate: 1, high: 2). On large projects, the voting model was 93% accurate. The results demonstrate how well machine learning predicts software reusability and provide recommendations for increasing software quality measures.

关键词： Q-factor Machine learning software quality Predictive models Complexity theory Maintenance software measurement

Crowdsourced Requirements engineering Challenges and Solutions:A software Industry Perspective

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computer Systems Science & engineering 2021年第11期39卷 221-236页

作者： Huma Hayat Khan Muhammad Noman Malik Youseef Alotaibi Abdulmajeed Alsufyani Saleh Alghamdi Department of Software Engineering National University of Modern LanguagesIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityPO Box 11099TaifSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software ***,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and *** the requirements are not clear to the development team,it has a significant effect on the quality of the software *** study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)***,solutions to overcome these challenges are also *** data analysis is performed on the interview data collected from software industry ***,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven *** study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.

关键词： software crowdsourced requirements engineering software industry software development survey challenges

Context-Guided Spatio-Temporal Video Grounding

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Context-Guided Spatio-Temporal Video Grounding

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xin Gu Heng Fan Yan Huang Tiejian Luo Libo Zhang University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Institute of Software Beijing China Department of Computer Science and Engineering University of North Texas Denton USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Spatio-temporal video grounding (or STVG) task aims at locating a spatio-temporal tube for a specific instance given a text query. Despite advancements, current methods easily suffer the distractors or heavy object appearance variations in videos due to insufficient object information from the text, leading to degradation. Addressing this, we propose a novel framework, context-guided STVG (CG-STVG), which mines discriminative instance context for object in videos and applies it as a supplementary guidance for target localization. The key of CG-STVG lies in two specially designed modules, including instance context generation (ICG), which focuses on discovering visual context information (in both appearance and motion) of the instance, and instance context refinement (ICR), which aims to improve the instance context from ICG by eliminating irrelevant or even harmful information from the context. During grounding, ICG, together with ICR, are deployed at each decoding stage of a transformer architecture for instance context learning. Particularly, instance context learned from one decoding stage is fed to the next stage, and leveraged as a guidance containing rich and discriminative object feature to enhance the target-awareness in decoding feature, which conversely benefits generating better new instance context to improve localization finally. Compared to existing methods, CG-STVG enjoys object information in text query and guidance from mined instance visual context for more accurate target localization. In experiments on HCSTVG-v1/-v2 and VidSTG, CG-STVG sets new state-of-the-arts in m_tIoU and m_vIoU on all of them, showing efficacy. Code is released at https://***/HengLan/CGSTVG.

关键词： Location awareness Degradation Visualization computer vision Codes Grounding computer architecture

Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

Improving the Transferability of Adversarial Samples by Path-Augmented Method

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arXiv 2023年

作者： Zhang, Jianping Huang, Jen-Tse Wang, Wenxuan Li, Yichen Wu, Weibin Wang, Xiaosen Su, Yuxin Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-sen University China Huawei Singular Security Lab Beijing China

Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon negatively affects their deployment in real-world scenarios, especially security-related ones. To evaluate the robustness of a target model in practice, transfer-based attacks craft adversarial samples with a local model and have attracted increasing attention from researchers due to their high efficiency. The state-of-the-art transfer-based attacks are generally based on data augmentation, which typically augments multiple training images from a linear path when learning adversarial samples. However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples. To overcome the pitfall, we propose the Path-Augmented Method (PAM). Specifically, PAM first constructs a candidate augmentation path pool. It then settles the employed augmentation paths during adversarial sample generation with greedy search. Furthermore, to avoid augmenting semantics-inconsistent images, we train a Semantics Predictor (SP) to constrain the length of the augmentation path. Extensive experiments confirm that PAM can achieve an improvement of over 4.8% on average compared with the state-of-the-art baselines in terms of the attack success rates. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

Analysis of Feature Importance and Interpretation for Malware Classification

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computers, Materials & Continua 2020年第12期65卷 1891-1904页

作者： Dong-Wook Kim Gun-Yoon Shin Myung-Mook Han Department of Computer Engineering Gachon UniversitySungnam-si13120Korea Department of Software Gachon UniversitySungnam-si13120Korea

This study was conducted to enable prompt classification of malware,which was becoming increasingly *** do this,we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were ***,the analysis features were extracted using Cuckoo Sandbox,an open-source malware analysis tool,then the features were divided into five categories using the extracted *** 804 extracted features were reduced by 70%after selecting only the most suitable ones for malware classification using a learning model-based feature selection method called the recursive feature ***,these important features were *** level of contribution from each one was assessed by the Random Forest classifier *** results showed that System call features were mostly *** the end,it was possible to accurately identify the malware type using only 36 to 76 features for each of the four types of malware with the most analysis samples *** were the Trojan,Adware,Downloader,and Backdoor malware.

关键词： Recursive feature elimination model interpretability feature importance malware classification

Keep it Local: Comparing Domain-Specific LLMs in Native and Machine Translated Text using Parallel Corpora on Political Conflict 2

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Keep it Local: Comparing Domain-Specific LLMs in Native and ...

2nd International Conference on Foundation and Large Language Models, FLLM 2024

作者： Osorio, Javier Alsarra, Sultan Converse, Amber Alshammari, Afraa Heintze, Dagmar Khan, Latifur Alatrush, Naif Brandt, Patrick T. D'orazio, Vito Zawad, Niamat Billah, Mahrusa University of Arizona School of Government and Public Policy Tucson United States King Saud University Department of Software Engineering Riyadh Saudi Arabia University of Arizona Department of Linguistics Tucson United States University of Texas - Dallas Department of Computer Science Dallas United States University of Texas - Dallas School of Economic Political and Policy Sciences Dallas United States West Virginia University Department of Political Science Morgantown United States

ISBN: (纸本)9798350354799

The dynamics of political conflict and cooperation require powerful computerized tools capable of effectively tracking security threats and cooperation around the world. This study compares the performance of domain-specific Large Language Models (LLMs) against generically-trained LLMs in binary and multi-class classification using native text in English, Spanish, and Arabic, and their corresponding machine translations. This endeavor yields four key contributions. 1) We present and make available a novel database of annotations using a multi-lingual parallel corpus from the United Nations. 2) Using various metrics, we assess the quality of different machine translation tools. 3) Our results indicate that the ConfliBERT family of LLMs, a set of domain-specific models tailored for political conflict, outperform generically-trained LLMs in English, Spanish, and Arabic in both binary and multi-class tasks. 4) We also disentangle the heterogeneous effects of machine translation on LLM performance in different languages. Overall, results reveal the comparative advantage of native-language domain-specific LLMs specialized on political conflict to understand the dynamics of violence and cooperation worldwide using native text. Our multi-lingual ConfliBERT LLMs provide critical cyber-infrastructure enabling scholars and government agencies use their local languages and information to foster safer, more stable political environments. © 2024 IEEE.

关键词： Machine translation

Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis

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arXiv 2023年

作者： Dong, Yanjie Wang, Luya Chi, Yuanfang Wang, Jia Zhang, Haijun Yu, Fei Richard Leung, Victor C.M. Hu, Xiping The Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China The College of Computer Science and Software Engineering Shenzhen University Shenzhen China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China The Department of Systems and Computer Engineering Carleton University Ottawa Canada The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks. © 2023, CC BY.

关键词： Momentum

BLOCKCHAIN-EMPOWERED FEDERATED LEARNING: BENEFITS, CHALLENGES, AND SOLUTIONS

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arXiv 2024年

作者： Cai, Zeju Chen, Jianguo Fan, Yuting Zheng, Zibin Li, Keqin School of Software Engineering Sun Yat-sen University Guangdong Zhuhai China Department of Computer Science State University of New York New PaltzNY United States

Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability. However, blockchain-empowered FL (BC-FL) systems introduce additional demands on network, computing, and storage resources. This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration. We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration. Additionally, we offer insights on future research directions for the BC-FL system. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain