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Towards Improving the Quality of Requirement and Testing Process in Agile software Development:An Empirical Study

computers, Materials & Continua 2024年第9期80卷 3761-3784页

作者： Irum Ilays Yaser Hafeez Nabil Almashfi Sadia Ali Mamoona Humayun Muhammad Aqib Ghadah Alwakid University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia School of Arts Humanities and Social Sciences University of RoehamptonLondonSW155PJUK Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia

software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing ***,it is difficult to identify all faults in *** requirement changes continuously,it increases the irrelevancy and redundancy during *** to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements *** this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based *** research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change *** compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based ***,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection ***,the proposed model was evaluated by conducting *** results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user *** predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user ***,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier ***,it provides guidelines for practitioners and researchers in the modern *** the future,we will provide the working prototype of this model for proof of concept.

关键词： Requirement prediction software testing agile software development semantic analysis case-based reasoning

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Global Proxy-based Hard Mining for Visual Place Recognition 33

Global Proxy-based Hard Mining for Visual Place Recognition

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Ali-Bey, Amar Chaib-Draa, Brahim Giguère, Philippe Department of Computer Science and Software Engineering Université Laval Québec City Canada

Learning deep representations for visual place recognition is commonly performed using pairwise or triple loss functions that highly depend on the hardness of the examples sampled at each training iteration. Existing techniques address this by using computationally and memory expensive offline hard mining, which consists of identifying, at each iteration, the hardest samples from the training set. In this paper we introduce a new technique that performs global hard mini-batch sampling based on proxies. To do so, we add a new end-to-end trainable branch to the network, which generates efficient place descriptors (one proxy for each place). These proxy representations are thus used to construct a global index that encompasses the similarities between all places in the dataset, allowing for highly informative mini-batch sampling at each training iteration. Our method can be used in combination with all existing pairwise and triplet loss functions with negligible additional memory and computation cost. We run extensive ablation studies and show that our technique brings new state-of-the-art performance on multiple large-scale benchmarks such as Pittsburgh, Mapillary-SLS and SPED. In particular, our method provides more than 100% relative improvement on the challenging Nordland dataset. Our code is available at https://***/amaralibey/GPM. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Iterative methods

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Towards Classifying File Segments in Memory Using Machine-Learning 9

Towards Classifying File Segments in Memory Using Machine-Le...

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9th International Conference on Information Technology Trends, ITT 2023

作者： Al-Saleh, Mohammed I. Alkouz, Akram Alarabeyyat, Abdulsalam Bouchahma, Majed Jordan University of Science and Technology Computer Science Department Jordan Higher Colleges of Technology Computer Information System United Arab Emirates Al-Balqa Applied University Software Engineering Department Jordan-Salt Jordan

ISBN: (纸本)9798350327502

Digital media triage is a main challenge that faces a digital investigator. Knowing what might be useful during crime investigation could greatly save the investigator's time and enhance outcomes. Memory investigation can get potential benefit from the triage process because of its scattered and diverse contents. The Operating System's paging scheme might map the contents of a file into non-consecutive page frames in the physical memory, making the file-type classification and identification process harder. This paper tackles the memory content triage problem at the page level using a machine-learning methodology. © 2023 IEEE.

关键词： Digital storage

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A Novel Rigorous Measurement Model for Big Data Quality Characteristics

A Novel Rigorous Measurement Model for Big Data Quality Char...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Zou, Haochen Xiang, Kun Concordia University Department of Computer Science and Software Engineering MontrealQC Canada Hosei University Department of Science and Engineering Tokyo Japan

ISBN: (纸本)9781665480451

Satisfiable data quality is the basic guarantee for data-based research, decision-making, and service. Today, new trends in the creation, collection, and utilization of data are constantly emerging. With the usage of massive data, the problem of data quality is highlighted. Several studies on the measurement, evaluation, and management of big data quality have been proposed, and the data quality problem in the big data environment has received attention. The big data characteristics Vs model describes the dimensions and attributes information of data sources in detail, which can be implemented in big data quality measurement. In this paper, a novel rigorous big data quality measurement architecture is proposed for automatically and parallelly quantifying the value of six big data Vs, which are Volume, Variety, Velocity, Veracity, Validity, and Vincularity according to the developed algorithms in every big data process step and time phase of the big data pipeline. Thresholds for the six big data Vs are provided correspondingly for analyzing the result values. The hierarchical measurement model is constructed with multiple-based measures, derived measures, and indicators. The model is verified by comparative experiments and experiments results indicate that the designed architecture can improve the outcomes of data source implementation. © 2022 IEEE.

关键词： Big data

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Design and Implementation of a Scheduling Meeting System Using Mobile Agent 8

Design and Implementation of a Scheduling Meeting System Usi...

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8th International Conference on Contemporary Information Technology and Mathematics, ICCITM 2022

作者： Hammo, Asma'A Y. Al-Jawaherry, Marwa Adeeb Altaie, Atica M. University of Mosul College of Computer Science and Mathematics Department of Software Engineering Mosul Iraq Tikrit University College of Computer Science and Mathematics Department of Computer Science Tikrit Iraq

ISBN: (纸本)9798350334869

In recent years, as a result of rapid development, the scheduling of meetings has become costly and time-consuming. Many systems that support the scheduling of meetings have been developed and they are independently finding the appropriate scheduling date and duration of the meeting. In this research, the Multi-Agent Systems (MAS) is used to find out the appropriate time for meeting since agent is able to cooperate and execute autonomy. An algorithm was proposed to deal with the participants' leisure time and to make the appropriate decision to determine the date and duration of the meeting. The scheduling system collects free time from several persons (every person has an agent) and decides the appropriate meeting date, duration, and time for holding the meeting and publishing it to all attenders. The number of attenders in the designed system is between (4-7), with the presence of the manager (The manager Agent) who is responsible for organizing the meeting. The system was developed using multi-agent systems. JADE (Java Agent Development Framework) is used under java language to design the system for its capabilities. The system provides easy and clear interfaces for users. The system is compared with other previous work and shows that its lightness to the network. Also, the proposed system provides an interface with no need to send messages using SMS or any other media. © 2022 IEEE.

关键词： Mobile agents

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A Short Survey on Inductive Biased Graph Neural Networks

A Short Survey on Inductive Biased Graph Neural Networks

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2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Zhang, Yuqi Wang, Nancy Yu, Jian Yongchareon, Sira Nguyen, Mo Auckland University of Technology Department of Computer Science and Software Engineering Auckland New Zealand

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

Many real-world networks including the World Wide Web and the Internet of Things are graphs in their abstract forms. Graph neural networks (GNNs) have emerged as the main solution for deep learning on graphs. Recently, tremendous effort has been made to enhance the performance and expressivity of GNNs. In this paper, we review the state-of-the-art graph neural network models and frameworks with a focus on the latest developments in graph representation learning. We propose a new taxonomy which divides general GNNs into recurrent GNNs, spectral GNNs, spatial GNNs and topology-aware GNNs. We will also discuss the inductive biases behind different categories of GNNs. © 2022 IEEE.

关键词： Graph neural networks

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Modelling and Verification of Context-Aware Intelligent Assistive Formalism

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computers, Materials & Continua 2022年第5期71卷 3355-3373页

作者： Shahid Yousaf Hafiz Mahfooz Ul Haque Abbas Khalid Muhammad Adnan Hashmi Eraj Khan Department of Computer Science&IT The University of LahoreLahore54000Pakistan Department of Software Engineering The University of LahoreLahore54000Pakistan Higher Colleges of Technology Abu Dhabi25026UAE

Recent years have witnessed the expeditious evolution of intelligentsmart devices and autonomous software technologies with the expandeddomains of computing from workplaces to smart computing in everydayroutine life activities. This trend has been rapidly advancing towards the newgeneration of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquirecontextual information from the surrounding environment autonomously,perform reasoning on it, and then adapt their behaviors accordingly. With theproliferation of context-aware systems and smart sensors, real-time monitoring of environmental situations (context) has become quite trivial. However,it is often challenging because the imperfect nature of context can cause theinconsistent behavior of the system. In this paper, we propose a contextaware intelligent decision support formalism to assist cognitively impairedpeople in managing their routine life activities. For this, we present a semanticknowledge-based framework to contextualize the information from the environment using the protégé ontology editor and Semantic Web Rule Language(SWRL) rules. The set of contextualized information and the set of rulesacquired from the ontology can be used to model Context-aware Multi-AgentSystems (CMAS) in order to autonomously plan all activities of the users andnotify users to act accordingly. To illustrate the use of the proposed formalism,we model a case study of Mild Cognitive Impaired (MCI) patients usingColored Petri Nets (CPN) to show the reasoning process on how the contextaware agents collaboratively plan activities on the user’s behalf and validatethe correctness properties of the system.

关键词： Context-awareness multi-agents colored petri net ontology

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Attack-Model-Agnostic Defense Against Model Poisonings in Distributed Learning 19

Attack-Model-Agnostic Defense Against Model Poisonings in Di...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xu, Hairuo Shu, Tao Auburn University Department of Computer Science and Software Engineering AuburnAL United States

ISBN: (纸本)9798350346558

The distributed nature of distributed learning renders the learning process susceptible to model poisoning attacks. Most existing countermeasures are designed based on a presumed attack model, and can only perform under the presumed attack model. However, in reality a distributed learning system typically does not have the luxury of knowing the attack model it is going to be actually facing in its operation when the learning system is deployed, thus constituting a zero-day vulnerability of the system that has been largely overlooked so far. In this paper, we study the attack-model-agnostic defense mechanisms for distributed learning, which are capable of countering a wide-spectrum of model poisoning attacks without relying on assumptions of the specific attack model, and hence alleviating the zero-day vulnerability of the system. Extensive experiments are performed to verify the effectiveness of the proposed defense. © 2022 IEEE.

关键词： Zero-day attack

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Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5681-5698页

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C. M. Chongqing University School of Big Data & Software Engineering Chongqing400000 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. © 2002-2012 IEEE.

关键词： Federated learning

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Mask guided diverse face image synthesis

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Frontiers of computer science 2022年第3期16卷 67-75页

作者： Song SUN Bo ZHAO Muhammad MATEEN Xin CHEN Junhao WEN School of Big Data&Software Engineering Chongqing UniversityChongqing 401331China Department of Computer Science The University of British ColumbiaVancouve BC V6T 1Z4Canada Department of Computer Science Air University Multan CampusMultan 60000Pakistan

Recent studies have shown remarkable success in face image generation ***,existing approaches have limited diversity,quality and controllability in generating *** address these issues,we propose a novel end-to-end learning framework to generate diverse,realistic and controllable face images guided by face *** face mask provides a good geometric constraint for a face by specifying the size and location of different components of the face,such as eyes,nose and *** framework consists of four components:style encoder,style decoder,generator and *** style encoder generates a style code which represents the style of the result face;the generator translate the input face mask into a real face based on the style code;the style decoder learns to reconstruct the style code from the generated face image;and the discriminator classifies an input face image as real or *** the style code,the proposed model can generate different face images matching the input face mask,and by manipulating the face mask,we can finely control the generated face *** empirically demonstrate the effectiveness of our approach on mask guided face image synthesis task.

关键词： face image generation image translation generative adversarial networks

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