Framework for Effective Utilization of Distributed Scrum in Software Projects

There is an emerging interest in using agile methodologies in Global Software Development(GSD)to get the mutual benefits of both methods.Scrum is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated teams.At the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural distances.Due to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in GSD.We conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the challenges.To validate our reviewfindings,we conducted an industrial survey of 305 practitioners.The results of our study are consolidated into a research framework.The framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed Scrum.Results of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.

Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Monitoring Surface Deformation Using Distributed Scatterers InSAR

In the past two decades,extensive and in-depth research has been conducted on Time Series InSAR technology with the advancement of high-performance SAR satellites and the accumulation of big SAR data.The introduction of distributed scatterers in Distributed Scatterers InSAR(DS-InSAR)has significantly expanded the application scenarios of InSAR geodetic measurement by increasing the number of measurement points.This study traces the history of DS-InSAR,presents the definition and characteristics of distributed scatterers,and focuses on exploring the relationships and distinctions among proposed algorithms in two crucial steps:statistically homogeneous pixel selection and phase optimization.Additionally,the latest research progress in this field is tracked and the possible development direction in the future is discussed.Through simulation experiments and two real InSAR case studies,the proposed algorithms are compared and verified,and the advantages of DS-InSAR in deformation measurement practice are demonstrated.This work not only offers insights into current trends and focal points for theoretical research on DS-InSAR but also provides practical cases and guidance for applied research.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Open-Source Software Defined Networking Controllers:State-of-the-Art,Challenges and Solutions for Future Network Providers

Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN technology.Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them.There are several SDN controllers available in the open market besides a large number of commercial controllers;some are developed tomeet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System(ONOS).This paper presents a comparative study between open source SDN controllers,which are known as Network Controller Platform(NOX),Python-based Network Controller(POX),component-based SDN framework(Ryu),Java-based OpenFlow controller(Floodlight),OpenDayLight(ODL)and ONOS.The discussion is further extended into ONOS architecture,as well as,the evolution of ONOS controllers.This article will review use cases based on ONOS controllers in several application deployments.Moreover,the opportunities and challenges of open source SDN controllers will be discussed,exploring carriergrade ONOS for future real-world deployments,ONOS unique features and identifying the suitable choice of SDN controller for service providers.In addition,we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in,interoperability,and standards compliance,Similarly,real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers.Furthermore,challenges faced by open-source projects,and considerations when choosing an open-source SDN controller are underscored.Then the role of Artificial Intelligence(AI)and Machine Learning(ML)in the evolution of open-source SDN controllers in light of recent research is indicated.In addition,the challenges and limitations associated with deploying open-source SDN controllers in production networks,how can they be mitigated,and finally how opensource SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented.Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

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

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing process.Therefore,it is difficult to identify all faults in software.As requirement changes continuously,it increases the irrelevancy and redundancy during testing.Due to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements specification.In this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment.The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis.Then compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based requirements.Afterward,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 rate.Furthermore,the proposed model was evaluated by conducting experiments.The 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 satisfaction.The predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user satisfaction.Therefore,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 stage.Hence,it provides guidelines for practitioners and researchers in the modern era.In the future,we will provide the working prototype of this model for proof of concept.

HV Process Model of Software Development

Software Development Life Cycle (SDLC) is one of the major ingredients for the development of efficient software systems within a time frame and low-cost involvement. From the literature, it is evident that there are various kinds of process models that are used by the software industries for the development of small, medium and long-term software projects, but many of them do not cover risk management. It is quite obvious that the improper selection of the software development process model leads to failure of the software products as it is time bound activity. In the present work, a new software development process model is proposed which covers the risks at any stage of the development of the software product. The model is named a Hemant-Vipin (HV) process model and may be helpful for the software industries for development of the efficient software products and timely delivery at the end of the client. The efficiency of the HV process model is observed by considering various kinds of factors like requirement clarity, user feedback, change agility, predictability, risk identification, practical implementation, customer satisfaction, incremental development, use of ready-made components, quick design, resource organization and many more and found through a case study that the presented approach covers many of parameters in comparison of the existing process models. .

How Software Engineering Transforms Organizations: An Open and Qualitative Study on the Organizational Objectives and Motivations in Agile Transformations

Agile Transformations are challenging processes for organizations that look to extend the benefits of Agile philosophy and methods beyond software engineering.Despite the impact of these transformations on orga-nizations,they have not been extensively studied in academia.We conducted a study grounded in workshops and interviews with 99 participants from 30 organizations,including organizations undergoing transformations(“final organizations”)and companies supporting these processes(“consultants”).The study aims to understand the motivations,objectives,and factors driving and challenging these transformations.Over 700 responses were collected to the question and categorized into 32 objectives.The findings show that organizations primarily aim to achieve customer centricity and adaptability,both with 8%of the mentions.Other primary important objectives,with above 4%of mentions,include alignment of goals,lean delivery,sustainable processes,and a flatter,more team-based organizational structure.We also detect discrepancies in perspectives between the objectives identified by the two kinds of organizations and the existing agile literature and models.This misalignment highlights the need for practitioners to understand with the practical realities the organizations face.

A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

Development and Evaluation of 3D Delivery Animation Software Designed to Improve the Mother’s and Spouse’s Satisfaction with Delivery

Purpose: To clarify the effectiveness of 3-D delivery animation software for the mother’s and husband’s satisfaction with delivery. Subjects and Method: We independently developed a software application used to display the pelvic region and explain the labor process. The study involved a collaboration with hospital staff who recruited 18 primiparous and 18 multiparous mothers who were hospitalized for delivery at Facility A. The midwife explained the process of delivery using the “Delivery Animation Software”. A self-administered, anonymous questionnaire was distributed and analyzed separately for primiparous and multiparous mothers and their husbands. Results: 1) For both primiparous and multiparous couples, both mothers and their husbands gained a significantly higher level of understanding after delivery than during pregnancy. 2) The Self-Evaluation Scale for Experience of Delivery results were as follows: “I did my best for the baby even if it was painful” was selected more often for “birth coping skills”;“reliable medical staff” was selected more often for “physiological birth process”;“the birth progressed as I expected” was selected frequently by primiparous mothers;and “the birth progressed smoothly” was selected often by multiparous mothers. 3) In terms of husbands’ satisfaction with the delivery, “I was satisfied with the delivery”, “I was given an easy-to-understand explanation”, and “They explained the process to me” was selected of primiparous and multiparous fathers. 4) All primiparous and multiparous mothers positively evaluated whether the delivery animation was helpful in understanding the process of delivery. Conclusion: The delivery animation was effective in improving the understanding and satisfaction of both the mothers and their husbands.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

A Proposed Approach for Measuring Maturity Level of Software Delivery

Software delivery is vital for modern organizations, driving innovation and competitiveness. Measuring an organization’s maturity in software delivery is crucial for efficiency and quality. The Capability Maturity Model (CMM) framework provides a roadmap for improvement but assessing an organization’s CMM Level is challenging. This paper offers a quantitative approach tailored to the CMM framework, using Goal-Question-Metric (GQM) frame-works for each key process area (KPA). These frameworks include metrics and questions to compute maturity scores effectively. The study also refines practices into questions for a thorough assessment. The result is an Analysis Matrix that calculates weighted scores and an overall maturity score. This approach helps organizations assess and enhance their software delivery processes systematically, aiming for improved practices and growth.

Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

The Internet of Things(IoT)has characteristics such as node mobility,node heterogeneity,link heterogeneity,and topology heterogeneity.In the face of the IoT characteristics and the explosive growth of IoT nodes,which brings about large-scale data processing requirements,edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions.However,the defense mechanism of Edge Computing-enabled IoT Nodes(ECIoTNs)is still weak due to their limited resources,so that they are susceptible to malicious software spread,which can compromise data confidentiality and network service availability.Facing this situation,we put forward an epidemiology-based susceptible-curb-infectious-removed-dead(SCIRD)model.Then,we analyze the dynamics of ECIoTNs with different infection levels under different initial conditions to obtain the dynamic differential equations.Additionally,we establish the presence of equilibrium states in the SCIRD model.Furthermore,we conduct an analysis of the model’s stability and examine the conditions under which malicious software will either spread or disappear within Edge Computing-enabled IoT(ECIoT)networks.Lastly,we validate the efficacy and superiority of the SCIRD model through MATLAB simulations.These research findings offer a theoretical foundation for suppressing the propagation of malicious software in ECIoT networks.The experimental results indicate that the theoretical SCIRD model has instructive significance,deeply revealing the principles of malicious software propagation in ECIoT networks.This study solves a challenging security problem of ECIoT networks by determining the malicious software propagation threshold,which lays the foundation for buildingmore secure and reliable ECIoT networks.

Distributed fiber optic sensors for tunnel monitoring:A state-of-the-art review

Distributed fiber optic sensors(DFOSs)possess the capability to measure strain and temperature variations over long distances,demonstrating outstanding potential for monitoring underground infrastructure.This study presents a state-of-the-art review of the DFOS applications for monitoring and assessing the deformation behavior of typical tunnel infrastructure,including bored tunnels,conventional tunnels,as well as immersed and cut-and-cover tunnels.DFOS systems based on Brillouin and Rayleigh scattering principles are both considered.When implementing DFOS monitoring,the fiber optic cable can be primarily installed along transverse and longitudinal directions to(1)measure distributed strains by continuously adhering the fiber to the structure’s surface or embedding it in the lining,or(2)measure point displacements by spot-anchoring it on the lining surface.There are four critical aspects of DFOS monitoring,including proper selection of the sensing fiber,selection of the measuring principle for the specific application,design of an effective sensor layout,and establishment of robust field sensor instrumentation.These four issues are comprehensively discussed,and practical suggestions are provided for the implementation of DFOS in tunnel infrastructure monitoring.