Maintaining software reliability is the key idea for conducting quality research.This can be done by having less complex applications.While developers and other experts have made signicant efforts in this context,the ...Maintaining software reliability is the key idea for conducting quality research.This can be done by having less complex applications.While developers and other experts have made signicant efforts in this context,the level of reliability is not the same as it should be.Therefore,further research into the most detailed mechanisms for evaluating and increasing software reliability is essential.A signicant aspect of growing the degree of reliable applications is the quantitative assessment of reliability.There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software.However,none of these mechanisms are useful for all kinds of failure datasets and applications.Hence nding the most optimal model for reliability prediction is an important concern.This paper suggests a novel method to substantially pick the best model of reliability prediction.This method is the combination of analytic hierarchy method(AHP),hesitant fuzzy(HF)sets and technique for order of preference by similarity to ideal solution(TOPSIS).In addition,using the different iterations of the process,procedural sensitivity was also performed to validate the ndings.The ndings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.展开更多
A novel kernel learning method for object-oriented （00） software fault prediction is proposed in this paper. With this method, each set of classes that has inheritance relation named class hierarchy, is treated as a...A novel kernel learning method for object-oriented （00） software fault prediction is proposed in this paper. With this method, each set of classes that has inheritance relation named class hierarchy, is treated as an elemental software model. A layered kernel is introduced to handle the tree data structure corresponding to the class hierarchy models. This method was vali- dated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experi- ments showed that our approach is very effective in learning structured data and outperforms the traditional support vector learning methods in accurately and correctly predicting the fault-prone class hierarchy model in real-life OO software.展开更多
The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a ve...The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.展开更多
An object-oriented approach is taken to the problem of formulating portable, easy-to-modify PDE solvers for realistic problems in three space dimensions. The resulting software library, Cogito, contains tools for writ...An object-oriented approach is taken to the problem of formulating portable, easy-to-modify PDE solvers for realistic problems in three space dimensions. The resulting software library, Cogito, contains tools for writing programs to be executed on MIMD computers with distributed memory. Difference methods on composite, structured grids are supported. Most of the Cogito classes have been implemented in Fortran 77, in such a way that the object-oriented design is visible. With respect to parallel performance, these tools yield code that is comparable to parallel solvers written in plain Fortran 77. The resulting programs are can be executed without modification on a large number of multicomputer platforms, and also on serial computers. The uppermost level of abstraction in Cogito concerns the problem of decoupling the numerical method from the PDE problem. The validity of these tools has been preliminarily demonstrated with a C++ implementation for one-dimensional problems.展开更多
Most current object-oriented programming courses offered by domestic colleges and universities generally focus on the object-oriented programming language itself,i.e.,the programming grammar of the language,but ignore...Most current object-oriented programming courses offered by domestic colleges and universities generally focus on the object-oriented programming language itself,i.e.,the programming grammar of the language,but ignore the design pattern.However,design patterns are essential to software engineering because they can solve common problems in software design and improve code reuse,readability,extensibility,and reliability.Our Object-oriented Software Construction Course is creative since it aims at cultivating students’object-oriented thinking as well as basic abilities required to construct high-quality,object-oriented software.Specifically,we exploit the 5E teaching principle during the education of this course,and present the whole pipeline in the paper.We also provide one case of the factory pattern to further demonstrate the implementation of the 5E teaching principle in the course.The effect of the 5E teaching principle has also been demonstrated.展开更多
The increasing use of digital video everyday in a multitude of electronic devices, including mobile phones, tablets and laptops, poses the need for quick development of cross-platform video software. However current a...The increasing use of digital video everyday in a multitude of electronic devices, including mobile phones, tablets and laptops, poses the need for quick development of cross-platform video software. However current approaches to this direction usually require a long learning curve, and their development lacks standardization. This results in software components that are difficult to reuse, and hard to maintain or extend. In order to overcome such issues, we propose a novel object-oriented framework for efficient development of software systems for video analysis. It consists of a set of four abstract components, suitable for the implementation of independent plug-in modules for video acquisition, preprocessing, analysis and output handling. The extensibility of each module can be facilitated by sub-modules specifying additional functionalities. This architecture enables quick responses to changes and re-configurability;thus conforming to the requirements of agile software development practices. Considering the need for platform independency, the proposed Java Video Analysis (JVA) framework is implemented in Java. It is publicly available through the web as open-access software, supported by a growing collection of implemented modules. Its efficiency is empirically validated for the development of a representative video analysis system.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
In order to investigate the mechanical behavior of systems with complex architecture and a large number of contacting bodies,a finite element software,named LiToTac,has been developed by using the object-oriented prog...In order to investigate the mechanical behavior of systems with complex architecture and a large number of contacting bodies,a finite element software,named LiToTac,has been developed by using the object-oriented programming technique.This software,with an interactive graphical user interface,is able to handle highly non-linear problems including multiple contacts and large deformation.More importantly,the contact detection based on a hybrid three-stages methodology can be performed automatically,which is more efficient than the common strategies of pre-defining contact zones in commercial FEM software like ANSYS,ABAQUS,etc.In addition,the contact solver in LiToTac is portable between dynamic and quasi-static codes and can accurately solve contact coupled with friction in a reduced system.Several numerical examples are carried out to illustrate the functionality and capacity of the software package.展开更多
Based on the new algorithm for GIS image pixel topographic factors in remote sensing monitoring ofsoil losses, a software was developed for microcomputer to carry out computation at a medium river basin(county). This ...Based on the new algorithm for GIS image pixel topographic factors in remote sensing monitoring ofsoil losses, a software was developed for microcomputer to carry out computation at a medium river basin(county). This paper lays its emphasis on algorithmic skills and programming techniques as well as applicationof the software.展开更多
Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of ob...Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.展开更多
An user-oriented computer software consisting of three modeling codes, named DRAD, DRAA and FDPAT, is introduced. It can be used to design three types of Cassegrain system: classical, with shaped subreflector and with...An user-oriented computer software consisting of three modeling codes, named DRAD, DRAA and FDPAT, is introduced. It can be used to design three types of Cassegrain system: classical, with shaped subreflector and with dual shaped reflectors, and to analyse radiation patterns for the antennas. Several mathematical models and numerical techniques are presented.展开更多
There has not been life cycle assessment (LCA) software applicable to China's products. To faciliate the development of LCA software system in China, we built an object-oriented LCA software system plantform based...There has not been life cycle assessment (LCA) software applicable to China's products. To faciliate the development of LCA software system in China, we built an object-oriented LCA software system plantform based on LCA international standards ISO 14040 to ISO 14043 and the commonness of diverse products. The system includes six modules: disassembly module, data collection module, arithmetic module, drawing module, database module and control module. It evaluates all economic and environmental impacts during the whole life cycle of a product. The integrated life cycle inventory database system allows dynamic expansion, which makes the effort required for data collection reduce with the increase of the system's application. With the improvement of human environmental-awareness, the demand of environment-friendly products is increasing, and ecological design of products has become a critical part in products development. Consummation of the LCA software system will provide a powerful tool for designing and developing Chinese ecological products.展开更多
Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages ot...Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.展开更多
Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into requirements.This phase is cost-and time-intensive,and...Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into requirements.This phase is cost-and time-intensive,and a project may fail if there are excessive costs and schedule overruns.COVID-19 has affected the software industry by reducing interactions between developers and customers.Such a lack of interaction is a key reason for the failure of software projects.Projects can also fail when customers do not know precisely what they want.Furthermore,selecting the unsuitable elicitation technique can also cause project failure.The present study,therefore,aimed to identify which requirements elicitation technique is the most cost-effective for large-scale projects when time to market is a critical issue or when the customer is not available.To that end,we conducted a systematic literature review on requirements elicitation techniques.Most primary studies identified introspection as the best technique,followed by survey and brainstorming.This finding suggests that introspection should be the first choice of elicitation technique,especially when the customer is not available or the project has strict time and cost constraints.Moreover,introspection should also be used as the starting point in the elicitation process of a large-scale project,and all known requirements should be elicited using this technique.展开更多
In this paper, we identify a set of factors that may be used to forecast software productivity and software development time. Software productivity was measured in function points per person hours, and software develo...In this paper, we identify a set of factors that may be used to forecast software productivity and software development time. Software productivity was measured in function points per person hours, and software development time was measured in number of elapsed days. Using field data on over 130 field software projects from various industries, we empirically test the impact of team size, integrated computer aided software engineering (ICASE) tools, software development type, software development platform, and programming language type on the software development productivity and development time. Our results indicate that team size, software development type, software development platform, and programming language type significantly impact software development productivity. However, only team size significantly impacts software development time. Our results indicate that effective management of software development teams, and using different management strategies for different software development type environments may improve software development productivity.展开更多
The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w...The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.展开更多
基金funded by Grant No.12-INF2970-10 from the National Science,Technology and Innovation Plan(MAARIFAH)the King Abdul-Aziz City for Science and Technology(KACST)Kingdom of Saudi Arabia.
文摘Maintaining software reliability is the key idea for conducting quality research.This can be done by having less complex applications.While developers and other experts have made signicant efforts in this context,the level of reliability is not the same as it should be.Therefore,further research into the most detailed mechanisms for evaluating and increasing software reliability is essential.A signicant aspect of growing the degree of reliable applications is the quantitative assessment of reliability.There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software.However,none of these mechanisms are useful for all kinds of failure datasets and applications.Hence nding the most optimal model for reliability prediction is an important concern.This paper suggests a novel method to substantially pick the best model of reliability prediction.This method is the combination of analytic hierarchy method(AHP),hesitant fuzzy(HF)sets and technique for order of preference by similarity to ideal solution(TOPSIS).In addition,using the different iterations of the process,procedural sensitivity was also performed to validate the ndings.The ndings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.
文摘A novel kernel learning method for object-oriented （00） software fault prediction is proposed in this paper. With this method, each set of classes that has inheritance relation named class hierarchy, is treated as an elemental software model. A layered kernel is introduced to handle the tree data structure corresponding to the class hierarchy models. This method was vali- dated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experi- ments showed that our approach is very effective in learning structured data and outperforms the traditional support vector learning methods in accurately and correctly predicting the fault-prone class hierarchy model in real-life OO software.
文摘The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.
文摘An object-oriented approach is taken to the problem of formulating portable, easy-to-modify PDE solvers for realistic problems in three space dimensions. The resulting software library, Cogito, contains tools for writing programs to be executed on MIMD computers with distributed memory. Difference methods on composite, structured grids are supported. Most of the Cogito classes have been implemented in Fortran 77, in such a way that the object-oriented design is visible. With respect to parallel performance, these tools yield code that is comparable to parallel solvers written in plain Fortran 77. The resulting programs are can be executed without modification on a large number of multicomputer platforms, and also on serial computers. The uppermost level of abstraction in Cogito concerns the problem of decoupling the numerical method from the PDE problem. The validity of these tools has been preliminarily demonstrated with a C++ implementation for one-dimensional problems.
基金supported by Guangdong Hardware and System Teaching and Research Office(Quality Engineeringproject No.HITSZERP22002)+2 种基金Guangdong Province Education Science Planning Project(Higher Education Project,Project No.2022GXJK431)Harbin Institute of Technology(Shenzhen)Course Ideological and Political Project(Project No.HITSZIP21003)Construction Project of Teachers College of Harbin Institute of Technology(Shenzhen)(Project No.HITSZSFXY202201)。
文摘Most current object-oriented programming courses offered by domestic colleges and universities generally focus on the object-oriented programming language itself,i.e.,the programming grammar of the language,but ignore the design pattern.However,design patterns are essential to software engineering because they can solve common problems in software design and improve code reuse,readability,extensibility,and reliability.Our Object-oriented Software Construction Course is creative since it aims at cultivating students’object-oriented thinking as well as basic abilities required to construct high-quality,object-oriented software.Specifically,we exploit the 5E teaching principle during the education of this course,and present the whole pipeline in the paper.We also provide one case of the factory pattern to further demonstrate the implementation of the 5E teaching principle in the course.The effect of the 5E teaching principle has also been demonstrated.
文摘The increasing use of digital video everyday in a multitude of electronic devices, including mobile phones, tablets and laptops, poses the need for quick development of cross-platform video software. However current approaches to this direction usually require a long learning curve, and their development lacks standardization. This results in software components that are difficult to reuse, and hard to maintain or extend. In order to overcome such issues, we propose a novel object-oriented framework for efficient development of software systems for video analysis. It consists of a set of four abstract components, suitable for the implementation of independent plug-in modules for video acquisition, preprocessing, analysis and output handling. The extensibility of each module can be facilitated by sub-modules specifying additional functionalities. This architecture enables quick responses to changes and re-configurability;thus conforming to the requirements of agile software development practices. Considering the need for platform independency, the proposed Java Video Analysis (JVA) framework is implemented in Java. It is publicly available through the web as open-access software, supported by a growing collection of implemented modules. Its efficiency is empirically validated for the development of a representative video analysis system.
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We gratefully acknowledge the financial support of the National Key R&D Program of China(Grant No.2017YFB0703200)the National Natural Science Foundation of China(Grant No.11772274).
文摘In order to investigate the mechanical behavior of systems with complex architecture and a large number of contacting bodies,a finite element software,named LiToTac,has been developed by using the object-oriented programming technique.This software,with an interactive graphical user interface,is able to handle highly non-linear problems including multiple contacts and large deformation.More importantly,the contact detection based on a hybrid three-stages methodology can be performed automatically,which is more efficient than the common strategies of pre-defining contact zones in commercial FEM software like ANSYS,ABAQUS,etc.In addition,the contact solver in LiToTac is portable between dynamic and quasi-static codes and can accurately solve contact coupled with friction in a reduced system.Several numerical examples are carried out to illustrate the functionality and capacity of the software package.
文摘Based on the new algorithm for GIS image pixel topographic factors in remote sensing monitoring ofsoil losses, a software was developed for microcomputer to carry out computation at a medium river basin(county). This paper lays its emphasis on algorithmic skills and programming techniques as well as applicationof the software.
文摘Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.
文摘An user-oriented computer software consisting of three modeling codes, named DRAD, DRAA and FDPAT, is introduced. It can be used to design three types of Cassegrain system: classical, with shaped subreflector and with dual shaped reflectors, and to analyse radiation patterns for the antennas. Several mathematical models and numerical techniques are presented.
文摘There has not been life cycle assessment (LCA) software applicable to China's products. To faciliate the development of LCA software system in China, we built an object-oriented LCA software system plantform based on LCA international standards ISO 14040 to ISO 14043 and the commonness of diverse products. The system includes six modules: disassembly module, data collection module, arithmetic module, drawing module, database module and control module. It evaluates all economic and environmental impacts during the whole life cycle of a product. The integrated life cycle inventory database system allows dynamic expansion, which makes the effort required for data collection reduce with the increase of the system's application. With the improvement of human environmental-awareness, the demand of environment-friendly products is increasing, and ecological design of products has become a critical part in products development. Consummation of the LCA software system will provide a powerful tool for designing and developing Chinese ecological products.
基金Researchers supporting Project Number(RSPD2024R576),King Saud University,Riyadh,Saudi Arabia.
文摘Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.
基金funding this work through research group no.RG-1441-490.
文摘Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into requirements.This phase is cost-and time-intensive,and a project may fail if there are excessive costs and schedule overruns.COVID-19 has affected the software industry by reducing interactions between developers and customers.Such a lack of interaction is a key reason for the failure of software projects.Projects can also fail when customers do not know precisely what they want.Furthermore,selecting the unsuitable elicitation technique can also cause project failure.The present study,therefore,aimed to identify which requirements elicitation technique is the most cost-effective for large-scale projects when time to market is a critical issue or when the customer is not available.To that end,we conducted a systematic literature review on requirements elicitation techniques.Most primary studies identified introspection as the best technique,followed by survey and brainstorming.This finding suggests that introspection should be the first choice of elicitation technique,especially when the customer is not available or the project has strict time and cost constraints.Moreover,introspection should also be used as the starting point in the elicitation process of a large-scale project,and all known requirements should be elicited using this technique.
文摘In this paper, we identify a set of factors that may be used to forecast software productivity and software development time. Software productivity was measured in function points per person hours, and software development time was measured in number of elapsed days. Using field data on over 130 field software projects from various industries, we empirically test the impact of team size, integrated computer aided software engineering (ICASE) tools, software development type, software development platform, and programming language type on the software development productivity and development time. Our results indicate that team size, software development type, software development platform, and programming language type significantly impact software development productivity. However, only team size significantly impacts software development time. Our results indicate that effective management of software development teams, and using different management strategies for different software development type environments may improve software development productivity.
文摘The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.
