The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Perolof Bengtsson - One of the best experts on this subject based on the ideXlab platform.
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assessing optimal software architecture Maintainability
Conference on Software Maintenance and Reengineering, 2001Co-Authors: Jan Bosch, Perolof BengtssonAbstract:Over the last decade, several authors have studied the Maintainability of software architectures. In particular, the assessment of Maintainability has received attention. However, even when one has a quantitative assessment of the Maintainability of a software architecture, one still does not have any indication of the optimality of the software architecture with respect to this quality attribute. Typically, the software architect is supposed to judge the assessment result based on his or her personal experience. In this paper, we propose a technique for analysing the optimal Maintainability of a software architecture based on a specified scenario profile. This technique allows software architects to analyse the Maintainability of their software architecture with respect to the optimal Maintainability. The technique is illustrated and evaluated using industrial cases.
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CSMR - Assessing optimal software architecture Maintainability
Proceedings Fifth European Conference on Software Maintenance and Reengineering, 1Co-Authors: Jan Bosch, Perolof BengtssonAbstract:Over the last decade, several authors have studied the Maintainability of software architectures. In particular, the assessment of Maintainability has received attention. However, even when one has a quantitative assessment of the Maintainability of a software architecture, one still does not have any indication of the optimality of the software architecture with respect to this quality attribute. Typically, the software architect is supposed to judge the assessment result based on his or her personal experience. In this paper, we propose a technique for analysing the optimal Maintainability of a software architecture based on a specified scenario profile. This technique allows software architects to analyse the Maintainability of their software architecture with respect to the optimal Maintainability. The technique is illustrated and evaluated using industrial cases.
Jan Bosch - One of the best experts on this subject based on the ideXlab platform.
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assessing optimal software architecture Maintainability
Conference on Software Maintenance and Reengineering, 2001Co-Authors: Jan Bosch, Perolof BengtssonAbstract:Over the last decade, several authors have studied the Maintainability of software architectures. In particular, the assessment of Maintainability has received attention. However, even when one has a quantitative assessment of the Maintainability of a software architecture, one still does not have any indication of the optimality of the software architecture with respect to this quality attribute. Typically, the software architect is supposed to judge the assessment result based on his or her personal experience. In this paper, we propose a technique for analysing the optimal Maintainability of a software architecture based on a specified scenario profile. This technique allows software architects to analyse the Maintainability of their software architecture with respect to the optimal Maintainability. The technique is illustrated and evaluated using industrial cases.
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CSMR - Assessing optimal software architecture Maintainability
Proceedings Fifth European Conference on Software Maintenance and Reengineering, 1Co-Authors: Jan Bosch, Perolof BengtssonAbstract:Over the last decade, several authors have studied the Maintainability of software architectures. In particular, the assessment of Maintainability has received attention. However, even when one has a quantitative assessment of the Maintainability of a software architecture, one still does not have any indication of the optimality of the software architecture with respect to this quality attribute. Typically, the software architect is supposed to judge the assessment result based on his or her personal experience. In this paper, we propose a technique for analysing the optimal Maintainability of a software architecture based on a specified scenario profile. This technique allows software architects to analyse the Maintainability of their software architecture with respect to the optimal Maintainability. The technique is illustrated and evaluated using industrial cases.
Ashish Sureka - One of the best experts on this subject based on the ideXlab platform.
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using source code metrics and multivariate adaptive regression splines to predict Maintainability of service oriented software
High-Assurance Systems Engineering, 2017Co-Authors: Lov Kumar, Santanu Kumar Rath, Ashish SurekaAbstract:Prediction of Maintainability parameter for Object-Oriented Software using source code metrics is an area that hasattracted the attention of several researchers in academia andindustry. However, Maintainability prediction of Service-Orientedsoftware is a relatively unexplored area. In this work, we conductan empirical analysis on Maintainability prediction of eBay webservices using several source code metrics. We consider elevendifferent types of source code metrics as input for developinga Maintainability prediction model using Multivariate AdaptiveRegression Splines (MARS) method. We compare and evaluatethe performance of the Maintainability prediction model withMultivariate Linear Regression (MLR) approach and SupportVector Machine (SVM). Eight different types of feature selectiontechniques have been implemented to reduce dimension andremove irrelevant features. The experiment results reveals thatthe Maintainability prediction model developed using MARSmethod achieved better performance as compared to MLR andSVM methods. Experimental results also demonstrate that themodel developed by considering a selected set of source codemetrics by feature selection technique as input achieves betterresults as compared to the approach which considers all sourcecode metrics.
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HASE - Using Source Code Metrics and Multivariate Adaptive Regression Splines to Predict Maintainability of Service Oriented Software
2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE), 2017Co-Authors: Lov Kumar, Santanu Kumar Rath, Ashish SurekaAbstract:Prediction of Maintainability parameter for Object-Oriented Software using source code metrics is an area that hasattracted the attention of several researchers in academia andindustry. However, Maintainability prediction of Service-Orientedsoftware is a relatively unexplored area. In this work, we conductan empirical analysis on Maintainability prediction of eBay webservices using several source code metrics. We consider elevendifferent types of source code metrics as input for developinga Maintainability prediction model using Multivariate AdaptiveRegression Splines (MARS) method. We compare and evaluatethe performance of the Maintainability prediction model withMultivariate Linear Regression (MLR) approach and SupportVector Machine (SVM). Eight different types of feature selectiontechniques have been implemented to reduce dimension andremove irrelevant features. The experiment results reveals thatthe Maintainability prediction model developed using MARSmethod achieved better performance as compared to MLR andSVM methods. Experimental results also demonstrate that themodel developed by considering a selected set of source codemetrics by feature selection technique as input achieves betterresults as compared to the approach which considers all sourcecode metrics.
Zhang Zhongneng - One of the best experts on this subject based on the ideXlab platform.
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software design optimization and implementation for system Maintainability improvement
Computer Engineering, 2004Co-Authors: Zhang ZhongnengAbstract:Maintenance is an important phase of software lifecycle. Software Maintainability is a key aspect of quality to software, which directly impact how much effort and cost will be spent on related systems or productions. Currently, it has become an essential point to evaluate whether a software system or productions is successful. People pay more and more sight on the software Maintainability and give consistent analysis and research and try to figure out how to improve the software system Maintainability. Answer can be extracted from each phase which composes the whole software lifecycle. This article focuses on the impact to Maintainability from software design sight, and gives out a solution on improvement and optimizes on the software Maintainability.
Lov Kumar - One of the best experts on this subject based on the ideXlab platform.
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using source code metrics and multivariate adaptive regression splines to predict Maintainability of service oriented software
High-Assurance Systems Engineering, 2017Co-Authors: Lov Kumar, Santanu Kumar Rath, Ashish SurekaAbstract:Prediction of Maintainability parameter for Object-Oriented Software using source code metrics is an area that hasattracted the attention of several researchers in academia andindustry. However, Maintainability prediction of Service-Orientedsoftware is a relatively unexplored area. In this work, we conductan empirical analysis on Maintainability prediction of eBay webservices using several source code metrics. We consider elevendifferent types of source code metrics as input for developinga Maintainability prediction model using Multivariate AdaptiveRegression Splines (MARS) method. We compare and evaluatethe performance of the Maintainability prediction model withMultivariate Linear Regression (MLR) approach and SupportVector Machine (SVM). Eight different types of feature selectiontechniques have been implemented to reduce dimension andremove irrelevant features. The experiment results reveals thatthe Maintainability prediction model developed using MARSmethod achieved better performance as compared to MLR andSVM methods. Experimental results also demonstrate that themodel developed by considering a selected set of source codemetrics by feature selection technique as input achieves betterresults as compared to the approach which considers all sourcecode metrics.
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HASE - Using Source Code Metrics and Multivariate Adaptive Regression Splines to Predict Maintainability of Service Oriented Software
2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE), 2017Co-Authors: Lov Kumar, Santanu Kumar Rath, Ashish SurekaAbstract:Prediction of Maintainability parameter for Object-Oriented Software using source code metrics is an area that hasattracted the attention of several researchers in academia andindustry. However, Maintainability prediction of Service-Orientedsoftware is a relatively unexplored area. In this work, we conductan empirical analysis on Maintainability prediction of eBay webservices using several source code metrics. We consider elevendifferent types of source code metrics as input for developinga Maintainability prediction model using Multivariate AdaptiveRegression Splines (MARS) method. We compare and evaluatethe performance of the Maintainability prediction model withMultivariate Linear Regression (MLR) approach and SupportVector Machine (SVM). Eight different types of feature selectiontechniques have been implemented to reduce dimension andremove irrelevant features. The experiment results reveals thatthe Maintainability prediction model developed using MARSmethod achieved better performance as compared to MLR andSVM methods. Experimental results also demonstrate that themodel developed by considering a selected set of source codemetrics by feature selection technique as input achieves betterresults as compared to the approach which considers all sourcecode metrics.
