The Experts below are selected from a list of 57 Experts worldwide ranked by ideXlab platform
Ashish Sureka - One of the best experts on this subject based on the ideXlab platform.
-
using structured text source code metrics and artificial neural networks to predict change proneness at code tab and program Organization level
India Software Engineering Conference, 2017Co-Authors: Lov Kumar, Ashish SurekaAbstract:Structured Text (ST) is a high-level text-based programming language which is part of the IEC 61131-3 standard. ST is widely used in the domain of industrial automation engineering to create Programmable Logic Controller (PLC) programs. ST is a Domain Specific Language (DSL) which is specialized to the Automation Engineering (AE) application domain. ST has specialized features and programming constructs which are different than general purpose programming languages. We define, develop a tool and compute 10 source code metrics and their correlation with each-other at the Code Tab (CT) and Program Organization Unit (POU) level for two real-world industrial projects at a leading automation engineering company. We study the correlation between the 10 ST source code metrics and their relationship with change proneness at the CT and POU level by creating experimental dataset consisting of different versions of the system. We build predictive models using Artificial Neural Network (ANN) based techniques to predict change proneness of the software. We conduct a series of experiments using various training algorithms and measure the performance of our approach using accuracy and F-measure metrics. We also apply two feature selection techniques to select optimal features aiming to improve the overall accuracy of the classifier.
-
ISEC - Using Structured Text Source Code Metrics and Artificial Neural Networks to Predict Change Proneness at Code Tab and Program Organization Level
Proceedings of the 10th Innovations in Software Engineering Conference on - ISEC '17, 2017Co-Authors: Lov Kumar, Ashish SurekaAbstract:Structured Text (ST) is a high-level text-based programming language which is part of the IEC 61131-3 standard. ST is widely used in the domain of industrial automation engineering to create Programmable Logic Controller (PLC) programs. ST is a Domain Specific Language (DSL) which is specialized to the Automation Engineering (AE) application domain. ST has specialized features and programming constructs which are different than general purpose programming languages. We define, develop a tool and compute 10 source code metrics and their correlation with each-other at the Code Tab (CT) and Program Organization Unit (POU) level for two real-world industrial projects at a leading automation engineering company. We study the correlation between the 10 ST source code metrics and their relationship with change proneness at the CT and POU level by creating experimental dataset consisting of different versions of the system. We build predictive models using Artificial Neural Network (ANN) based techniques to predict change proneness of the software. We conduct a series of experiments using various training algorithms and measure the performance of our approach using accuracy and F-measure metrics. We also apply two feature selection techniques to select optimal features aiming to improve the overall accuracy of the classifier.
Lov Kumar - One of the best experts on this subject based on the ideXlab platform.
-
using structured text source code metrics and artificial neural networks to predict change proneness at code tab and program Organization level
India Software Engineering Conference, 2017Co-Authors: Lov Kumar, Ashish SurekaAbstract:Structured Text (ST) is a high-level text-based programming language which is part of the IEC 61131-3 standard. ST is widely used in the domain of industrial automation engineering to create Programmable Logic Controller (PLC) programs. ST is a Domain Specific Language (DSL) which is specialized to the Automation Engineering (AE) application domain. ST has specialized features and programming constructs which are different than general purpose programming languages. We define, develop a tool and compute 10 source code metrics and their correlation with each-other at the Code Tab (CT) and Program Organization Unit (POU) level for two real-world industrial projects at a leading automation engineering company. We study the correlation between the 10 ST source code metrics and their relationship with change proneness at the CT and POU level by creating experimental dataset consisting of different versions of the system. We build predictive models using Artificial Neural Network (ANN) based techniques to predict change proneness of the software. We conduct a series of experiments using various training algorithms and measure the performance of our approach using accuracy and F-measure metrics. We also apply two feature selection techniques to select optimal features aiming to improve the overall accuracy of the classifier.
-
ISEC - Using Structured Text Source Code Metrics and Artificial Neural Networks to Predict Change Proneness at Code Tab and Program Organization Level
Proceedings of the 10th Innovations in Software Engineering Conference on - ISEC '17, 2017Co-Authors: Lov Kumar, Ashish SurekaAbstract:Structured Text (ST) is a high-level text-based programming language which is part of the IEC 61131-3 standard. ST is widely used in the domain of industrial automation engineering to create Programmable Logic Controller (PLC) programs. ST is a Domain Specific Language (DSL) which is specialized to the Automation Engineering (AE) application domain. ST has specialized features and programming constructs which are different than general purpose programming languages. We define, develop a tool and compute 10 source code metrics and their correlation with each-other at the Code Tab (CT) and Program Organization Unit (POU) level for two real-world industrial projects at a leading automation engineering company. We study the correlation between the 10 ST source code metrics and their relationship with change proneness at the CT and POU level by creating experimental dataset consisting of different versions of the system. We build predictive models using Artificial Neural Network (ANN) based techniques to predict change proneness of the software. We conduct a series of experiments using various training algorithms and measure the performance of our approach using accuracy and F-measure metrics. We also apply two feature selection techniques to select optimal features aiming to improve the overall accuracy of the classifier.
Xu Guo-hua - One of the best experts on this subject based on the ideXlab platform.
-
The mode of network Organization design based on the optimization Organization Unit
Journal of Xidian University, 2006Co-Authors: Xu Guo-huaAbstract:From the trade of the advanced manufacturing mode and the change of Organization,the conception of optimization Organization Unit is defined,which applies Socio-Technical System,modern Organization management and Organizational economics synthetically.The mathematical programming model of optimization Organization Unit is built by quantifying the cost of technique,manpower and Organization.This model is subject to the interaction between the technical system and social system.Then the mode of network Organization design based on the optimization Organization Unit is given.The mode improves the methods of team and network Organization design.It can achieve the coordinated optimization of socio-technical factors in Organization,and make the Organization have both dynamic reconstruction and stability.
Yongping Xie - One of the best experts on this subject based on the ideXlab platform.
-
The mode of the integrated network Organization design based on optimal Organization Unit
2011 2nd International Conference on Artificial Intelligence Management Science and Electronic Commerce (AIMSEC), 2011Co-Authors: Anmin Wang, Yongping XieAbstract:With the trends to Unit, integration and network of advanced manufacturing mode changes, a design mode of integrated network Organization based on optimal Organization Unit and its analysis framework are presented by synthesizing the achievement of Organization theoretical and practical innovation. The design mode provides the Organizational form for several advanced manufacturing modes to match their properties, solves the problem of coordination paralysis in large-scale work team and the incompatibility problem of dynamics and stability in traditional Organizations, helps to accomplish the rapid reconstruction of Organizational system with low cost and low risk.
Anmin Wang - One of the best experts on this subject based on the ideXlab platform.
-
The mode of the integrated network Organization design based on optimal Organization Unit
2011 2nd International Conference on Artificial Intelligence Management Science and Electronic Commerce (AIMSEC), 2011Co-Authors: Anmin Wang, Yongping XieAbstract:With the trends to Unit, integration and network of advanced manufacturing mode changes, a design mode of integrated network Organization based on optimal Organization Unit and its analysis framework are presented by synthesizing the achievement of Organization theoretical and practical innovation. The design mode provides the Organizational form for several advanced manufacturing modes to match their properties, solves the problem of coordination paralysis in large-scale work team and the incompatibility problem of dynamics and stability in traditional Organizations, helps to accomplish the rapid reconstruction of Organizational system with low cost and low risk.