Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as ...Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.展开更多
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 ...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.展开更多
Some types of software systems,like event‐based and non‐deterministic ones,are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules.However,when the fuzz...Some types of software systems,like event‐based and non‐deterministic ones,are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules.However,when the fuzzy rules are used for the specification of non‐deterministic behaviour and they contain a large number of variables,they constitute a complex form that is difficult to understand and infer.A solution is to visualise the system specification with the capability of automatic rule inference.In this study,by representing a high‐level system specification,the authors visualise rule representation and firing using fuzzy coloured Petri‐nets.Already,several fuzzy Petri‐nets‐based methods have been presented,but they either do not support a large number of rules and variables or do not consider significant cases like(a)the weight of the premise's propositions in the occurrence of the rule conclusion,(b)the weight of conclusion's proposition,(c)threshold values for premise and conclusion's propositions of the rule,and(d)the certainty factor(CF)for the rule or the conclusion's proposition.By considering cases(a)-(d),a wider variety of fuzzy rules are supported.The authors applied their model to the analysis of attacks against a part of a real secure water treatment system.In another real experiment,the authors applied the model to the two scenarios from their previous work and analysed the results.展开更多
Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple ...Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which completes the validation.展开更多
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...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.展开更多
The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO client.Although a lot of research on software outsourcing is going on,most...The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO client.Although a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development only.Several frameworks have been developed focusing on guiding software systemmanagers concerning offshore software outsourcing.However,none of these studies delivered comprehensive guidelines for managing the whole process of OSMO.There is a considerable lack of research working on managing OSMO from a vendor’s perspective.Therefore,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s perspective.This study validated the preliminary OSMO process model via a case study research approach.The results showed that the OSMO process model is applicable in an industrial setting with few changes.The industrial data collected during the case study enabled this paper to extend the preliminary OSMO process model.The refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.展开更多
Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the client.Clients can benefit from offshore software maintenance outsourcing(OSMO)in different w...Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the client.Clients can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and value.One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’projects.The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients.The projects belong to OSMO vendors,having offices in developing countries while providing services to developed countries.In the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model.The proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden layers.The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies.The current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.展开更多
This paper presents new theoretical aspects of software engineering which oriented on product lines for building applied systems and software product families from readymade reusable components in conditions of progra...This paper presents new theoretical aspects of software engineering which oriented on product lines for building applied systems and software product families from readymade reusable components in conditions of program factories. These aspects are the new disciplines such as the theory of component programming;models variability and interoperability of system;theory for building systems and product families from components. Principles and methods of implementing these theories were realized in the instrumental and technological complex by lines of component development: assembling program factories using lines, e-learning to new theories and technologies in textbook of “Software Engineering” by the universities students.展开更多
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).Beca...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 industry.However,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained.If the requirements are not clear to the development team,it has a significant effect on the quality of the software product.This study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)process.Moreover,solutions to overcome these challenges are also identified.Qualitative data analysis is performed on the interview data collected from software industry professionals.Consequently,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven categories.This 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.展开更多
According to the abstract and practical characteristics of introduction to software engineering,the mixed flipped classroom teaching method is used in the teaching process.It can stimulate students’interest in learni...According to the abstract and practical characteristics of introduction to software engineering,the mixed flipped classroom teaching method is used in the teaching process.It can stimulate students’interest in learning.Taking the SPOC course“Introduction to software engineering”offered by Chongqing University as an example,this study uses the blended flipped classroom teaching method of“learning before teaching”.Online teaching resources design,teaching process design and assessment design were devised and practiced.Through the practice of blended flipped classroom teaching based on SPOC,the students’autonomous learning ability is improved.The effective combination of online teaching and offline classroom is realized.The teaching effect of this course has improved.展开更多
Nowadays software is taking a very important role in almost all aspects of our daily lives which gave great importance to the study field of Software Engineering. However, most of the current Software Engineering grad...Nowadays software is taking a very important role in almost all aspects of our daily lives which gave great importance to the study field of Software Engineering. However, most of the current Software Engineering graduates in Jordan lack the required knowledge and skills to join software industry because of many reasons. This research investigates these reasons by firstly analyzing more than 1000 software job listings in Jordanian and Gulf area e-recruitment services in order to discover the skills and knowledge areas that are mostly required by software industry in Jordan and the Gulf area, and secondly comparing these knowledge areas and skills with those provided by the Software Engineering curricula at the Jordanian Universities. The awareness of the Software Engineering students and academic staff of the concluded mostly required knowledge areas and skills is measured using two questionnaires. Recommendations to decrease the gap between Software Engineering academia and industry had also been taken from a sample of software companies’ manager using a third questionnaire. The results of this research revealed that many important skills such as Web applications development are very poorly covered by Software engineering curricula and that many Software engineering students and academic staffs are not aware about many of the mostly needed skills to join industry.展开更多
Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’...Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.展开更多
In software engineering, software measures are often proposed without precise identification of the measurable concepts they attempt to quantify: consequently, the numbers obtained are challenging to reproduce in diff...In software engineering, software measures are often proposed without precise identification of the measurable concepts they attempt to quantify: consequently, the numbers obtained are challenging to reproduce in different measurement contexts and to interpret, either as base measures or in combination as derived measures. The lack of consistency when using base measures in data collection can affect both data preparation and data analysis. This paper analyzes the similarities and differences across three different views of measurement methods (ISO International Vocabulary on Metrology, ISO 15939, and ISO 25021), and uses a process proposed for the design of software measurement methods to analyze two examples of such methods selected from the literature.展开更多
The Internet of Things plays a predominant role in automating all real-time applications.One such application is the Internet of Vehicles which monitors the roadside traffic for automating traffic rules.As vehicles ar...The Internet of Things plays a predominant role in automating all real-time applications.One such application is the Internet of Vehicles which monitors the roadside traffic for automating traffic rules.As vehicles are connected to the internet through wireless communication technologies,the Internet of Vehicles network infrastructure is susceptible to flooding attacks.Reconfiguring the network infrastructure is difficult as network customization is not possible.As Software Defined Network provide a flexible programming environment for network customization,detecting flooding attacks on the Internet of Vehicles is integrated on top of it.The basic methodology used is crypto-fuzzy rules,in which cryptographic standard is incorporated in the traditional fuzzy rules.In this research work,an intelligent framework for secure transportation is proposed with the basic ideas of security attacks on the Internet of Vehicles integrated with software-defined networking.The intelligent framework is proposed to apply for the smart city application.The proposed cognitive framework is integrated with traditional fuzzy,cryptofuzzy and Restricted Boltzmann Machine algorithm to detect malicious traffic flows in Software-Defined-Internet of Vehicles.It is inferred from the result interpretations that an intelligent framework for secure transportation system achieves better attack detection accuracy with less delay and also prevents buffer overflow attacks.The proposed intelligent framework for secure transportation system is not compared with existing methods;instead,it is tested with crypto and machine learning algorithms.展开更多
The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles...The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.展开更多
Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of ...Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of images.Since the work is done offshore,these agreements or images must be shared through the Internet or stored over the cloud.The breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of danger.This article proposes a novel algorithm for securing the NDAs in the form of images.As an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the Internet.As the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of time.The confusion effects have been realized through an XOR operation between the confused image,and chaotic data.Besides,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it has.The machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.展开更多
Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting...Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus,there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.展开更多
The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide.This problem is addressed by the software development industry by softwar...The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide.This problem is addressed by the software development industry by software product line(SPL)practices that employ feature models.However,optimal feature selection based on user requirements is a challenging task.Thus,there is a requirement to resolve the challenges of software development,to increase satisfaction and maintain high product quality,for massive customer needs within limited resources.In this work,we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and prior experiences of similar developers and clients.The proposed system recommends features with their estimated cost concerning new software requirements,from all over the globe according to similar developers’and clients’needs and preferences.The system guides and facilitates the development team by suggesting a list of features,code snippets,libraries,cheat sheets of programming languages,and coding references from a cloud-based knowledge management repository.Similarly,a list of features is suggested to the client according to their needs and preferences.The experimental results revealed that the proposed recommender system is feasible and effective,providing better recommendations to developers and clients.It provides proper and reasonably well-estimated costs to perform development tasks effectively as well as increase the client’s satisfaction level.The results indicate that there is an increase in productivity,performance,and quality of products and a reduction in effort,complexity,and system failure.Therefore,our proposed system facilitates developers and clients during development by providing better recommendations in terms of solutions and anticipated costs.Thus,the increase in productivity and satisfaction level maximizes the benefits and usability of SPL in the modern era of technology.展开更多
文摘Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.
基金the R&D&I,Spain grants PID2020-119478GB-I00 and,PID2020-115832GB-I00 funded by MCIN/AEI/10.13039/501100011033.N.Rodríguez-Barroso was supported by the grant FPU18/04475 funded by MCIN/AEI/10.13039/501100011033 and by“ESF Investing in your future”Spain.J.Moyano was supported by a postdoctoral Juan de la Cierva Formación grant FJC2020-043823-I funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR.J.Del Ser acknowledges funding support from the Spanish Centro para el Desarrollo Tecnológico Industrial(CDTI)through the AI4ES projectthe Department of Education of the Basque Government(consolidated research group MATHMODE,IT1456-22)。
文摘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.
基金supported by University of Kashan,(Grant/Award Number:'234333').
文摘Some types of software systems,like event‐based and non‐deterministic ones,are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules.However,when the fuzzy rules are used for the specification of non‐deterministic behaviour and they contain a large number of variables,they constitute a complex form that is difficult to understand and infer.A solution is to visualise the system specification with the capability of automatic rule inference.In this study,by representing a high‐level system specification,the authors visualise rule representation and firing using fuzzy coloured Petri‐nets.Already,several fuzzy Petri‐nets‐based methods have been presented,but they either do not support a large number of rules and variables or do not consider significant cases like(a)the weight of the premise's propositions in the occurrence of the rule conclusion,(b)the weight of conclusion's proposition,(c)threshold values for premise and conclusion's propositions of the rule,and(d)the certainty factor(CF)for the rule or the conclusion's proposition.By considering cases(a)-(d),a wider variety of fuzzy rules are supported.The authors applied their model to the analysis of attacks against a part of a real secure water treatment system.In another real experiment,the authors applied the model to the two scenarios from their previous work and analysed the results.
基金[King Abdulaziz University][Deanship of Scientific Research]Grant Number[KEP-PHD-20-611-42].
文摘Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which completes the validation.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no.RG-1441-490.
文摘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.
基金This research is fully funded byUniversiti Malaysia Terengganu under the research Grant(PGRG).
文摘The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO client.Although a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development only.Several frameworks have been developed focusing on guiding software systemmanagers concerning offshore software outsourcing.However,none of these studies delivered comprehensive guidelines for managing the whole process of OSMO.There is a considerable lack of research working on managing OSMO from a vendor’s perspective.Therefore,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s perspective.This study validated the preliminary OSMO process model via a case study research approach.The results showed that the OSMO process model is applicable in an industrial setting with few changes.The industrial data collected during the case study enabled this paper to extend the preliminary OSMO process model.The refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.
基金fully funded by Universiti Teknologi Malaysia under the UTM Fundamental Research Grant(UTMFR)with Cost Center No Q.K130000.2556.21H14.
文摘Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the client.Clients can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and value.One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’projects.The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients.The projects belong to OSMO vendors,having offices in developing countries while providing services to developed countries.In the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model.The proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden layers.The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies.The current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.
文摘This paper presents new theoretical aspects of software engineering which oriented on product lines for building applied systems and software product families from readymade reusable components in conditions of program factories. These aspects are the new disciplines such as the theory of component programming;models variability and interoperability of system;theory for building systems and product families from components. Principles and methods of implementing these theories were realized in the instrumental and technological complex by lines of component development: assembling program factories using lines, e-learning to new theories and technologies in textbook of “Software Engineering” by the universities students.
基金‘This research is funded by Taif University,TURSP-2020/115’.
文摘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 industry.However,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained.If the requirements are not clear to the development team,it has a significant effect on the quality of the software product.This study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)process.Moreover,solutions to overcome these challenges are also identified.Qualitative data analysis is performed on the interview data collected from software industry professionals.Consequently,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven categories.This 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.
基金supported by the Teaching Reform Research Project of Chongqing University“Research on the Continuous Improvement Mechanism of Software Engineering for Engineering Education Professional Certification”(Grant No.2021Y12),“Big Data Engineering Training and Teaching Exploration in the Context of Industry Education Integration”(Grant No.2021Y13)the Teaching Reform Research Project of Chongqing City“Research and Practice on the Training System of Data Science and Big Data Professionals”(Grant No.203200)the Research Project of Chongqing Postgraduate Education and Teaching Reform“Exploration and Practice of the Cultivation of the Experimental Ability of the Software Engineering Academic Master under the Cooperation of Science and Education with Industry and Education”(Grant No.yjg213020).
文摘According to the abstract and practical characteristics of introduction to software engineering,the mixed flipped classroom teaching method is used in the teaching process.It can stimulate students’interest in learning.Taking the SPOC course“Introduction to software engineering”offered by Chongqing University as an example,this study uses the blended flipped classroom teaching method of“learning before teaching”.Online teaching resources design,teaching process design and assessment design were devised and practiced.Through the practice of blended flipped classroom teaching based on SPOC,the students’autonomous learning ability is improved.The effective combination of online teaching and offline classroom is realized.The teaching effect of this course has improved.
文摘Nowadays software is taking a very important role in almost all aspects of our daily lives which gave great importance to the study field of Software Engineering. However, most of the current Software Engineering graduates in Jordan lack the required knowledge and skills to join software industry because of many reasons. This research investigates these reasons by firstly analyzing more than 1000 software job listings in Jordanian and Gulf area e-recruitment services in order to discover the skills and knowledge areas that are mostly required by software industry in Jordan and the Gulf area, and secondly comparing these knowledge areas and skills with those provided by the Software Engineering curricula at the Jordanian Universities. The awareness of the Software Engineering students and academic staff of the concluded mostly required knowledge areas and skills is measured using two questionnaires. Recommendations to decrease the gap between Software Engineering academia and industry had also been taken from a sample of software companies’ manager using a third questionnaire. The results of this research revealed that many important skills such as Web applications development are very poorly covered by Software engineering curricula and that many Software engineering students and academic staffs are not aware about many of the mostly needed skills to join industry.
文摘Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.
文摘In software engineering, software measures are often proposed without precise identification of the measurable concepts they attempt to quantify: consequently, the numbers obtained are challenging to reproduce in different measurement contexts and to interpret, either as base measures or in combination as derived measures. The lack of consistency when using base measures in data collection can affect both data preparation and data analysis. This paper analyzes the similarities and differences across three different views of measurement methods (ISO International Vocabulary on Metrology, ISO 15939, and ISO 25021), and uses a process proposed for the design of software measurement methods to analyze two examples of such methods selected from the literature.
文摘The Internet of Things plays a predominant role in automating all real-time applications.One such application is the Internet of Vehicles which monitors the roadside traffic for automating traffic rules.As vehicles are connected to the internet through wireless communication technologies,the Internet of Vehicles network infrastructure is susceptible to flooding attacks.Reconfiguring the network infrastructure is difficult as network customization is not possible.As Software Defined Network provide a flexible programming environment for network customization,detecting flooding attacks on the Internet of Vehicles is integrated on top of it.The basic methodology used is crypto-fuzzy rules,in which cryptographic standard is incorporated in the traditional fuzzy rules.In this research work,an intelligent framework for secure transportation is proposed with the basic ideas of security attacks on the Internet of Vehicles integrated with software-defined networking.The intelligent framework is proposed to apply for the smart city application.The proposed cognitive framework is integrated with traditional fuzzy,cryptofuzzy and Restricted Boltzmann Machine algorithm to detect malicious traffic flows in Software-Defined-Internet of Vehicles.It is inferred from the result interpretations that an intelligent framework for secure transportation system achieves better attack detection accuracy with less delay and also prevents buffer overflow attacks.The proposed intelligent framework for secure transportation system is not compared with existing methods;instead,it is tested with crypto and machine learning algorithms.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.
基金This research is fully funded by Universiti Teknologi Malaysia under the UTM Fundamental Research Grant(UTMFR)with Cost Center No Q.K130000.2556.21H14.
文摘Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of images.Since the work is done offshore,these agreements or images must be shared through the Internet or stored over the cloud.The breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of danger.This article proposes a novel algorithm for securing the NDAs in the form of images.As an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the Internet.As the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of time.The confusion effects have been realized through an XOR operation between the confused image,and chaotic data.Besides,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it has.The machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.
基金This research was financially supported in part by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program.(Project No.P0016038)in part by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2016-0-00312)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).
文摘Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus,there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.
基金supported by the National Natural Science Foundation of China(Grant Number:61672080,Sponsored Authors:Yang S.,Sponsors’Websites:http://www.nsfc.gov.cn/english/site_1/index.html).
文摘The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide.This problem is addressed by the software development industry by software product line(SPL)practices that employ feature models.However,optimal feature selection based on user requirements is a challenging task.Thus,there is a requirement to resolve the challenges of software development,to increase satisfaction and maintain high product quality,for massive customer needs within limited resources.In this work,we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and prior experiences of similar developers and clients.The proposed system recommends features with their estimated cost concerning new software requirements,from all over the globe according to similar developers’and clients’needs and preferences.The system guides and facilitates the development team by suggesting a list of features,code snippets,libraries,cheat sheets of programming languages,and coding references from a cloud-based knowledge management repository.Similarly,a list of features is suggested to the client according to their needs and preferences.The experimental results revealed that the proposed recommender system is feasible and effective,providing better recommendations to developers and clients.It provides proper and reasonably well-estimated costs to perform development tasks effectively as well as increase the client’s satisfaction level.The results indicate that there is an increase in productivity,performance,and quality of products and a reduction in effort,complexity,and system failure.Therefore,our proposed system facilitates developers and clients during development by providing better recommendations in terms of solutions and anticipated costs.Thus,the increase in productivity and satisfaction level maximizes the benefits and usability of SPL in the modern era of technology.
