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Suresh Chandra Satapathy - One of the best experts on this subject based on the ideXlab platform.
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Cost-effective and fault-resilient Reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications
Cluster Computing, 2019Co-Authors: Neelamadhab Padhy, R. P. Singh, Suresh Chandra SatapathyAbstract:The exponential rise in software technologies and its significances has demanded academia-industries to ensure low cost software solution with assured service quality and reliability. A low cost and fault-resilient software design is must, where to achieve low cost design the developers or programmers prefer exploiting source or function reuse. However, excessive Reusability makes software vulnerable to get faulty due to increased complexity and aging proneness. Non-deniably assessing Reusability of a class of function in software can enable avoiding any unexpected fault or failure. To achieve it developing a robust and efficient Reusability estimation or prediction model is of utmost significance. On the other hand, the aftermath consequences of excess Reusability caused faults might lead significant losses. Hence assessing cost effectiveness and efficacy of a Reusability prediction model is must for software design optimization. In this paper, we have examined different Reusability prediction models for their cost effectiveness and prediction efficiency over object-oriented software design. At first to examine the Reusability of a class, three key object oriented software metrics (OO-SM); cohesion, coupling and complexity of the software components are used. Furthermore, our proposed cost-efficient Reusability prediction model incorporates Min–Max normalization, outlier detection, Reusability threshold estimation; T test analysis based feature selection and various classification algorithms. Different classifiers including decision tree (DT), Naïve Bayes (NB), artificial neural network (ANN) algorithms, extreme learning machine (ELM), regression algorithms, multivariate adaptive regression spline (MARS) and adaptive genetic algorithm (AGA) based ANN are used for Reusability prediction. Additionally, the cost effectiveness of each Reusability prediction model is estimated, where the overall results have revealed that AGA based ANN as classifier in conjunction with OO-SM, normalization, T test analysis based feature selection outperforms other state-of-art techniques in terms of both accuracy as well as cost-effectiveness.
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Enhanced evolutionary computing based artificial intelligence model for web-solutions software Reusability estimation
Cluster Computing, 2019Co-Authors: Neelamdhab Padhy, R. P. Singh, Suresh Chandra SatapathyAbstract:Ensuring the aging resilient software design can be of paramount significance to enable faultless software system. Particularly assessing Reusability extent of the software components can enable efficient software design. The probability of aging proneness can be characterized based on key OO-SM like cohesion, coupling and complexity of a software component. In this paper, aging resilient software Reusability prediction model is proposed for object oriented design based Web of Service (WoS) software systems. This work introduces multilevel optimization to accomplish a novel Reusability prediction model. Considering coupling, cohesion and complexity as the software characteristics to signify aging proneness, six CK metrics; WMC, CBO, DIT, LCOM, NOC, and RFC are obtained from 100 WoS software. The extracted CK metrics are processed for min–max normalization that alleviates data-unbalancing and hence avoids saturation during learning. The 10-fold Cross-validation followed by outlier detection is considered to enrich data quality for further feature extraction. To reduce computational overheads RSA algorithm is applied. SoftAudit tool is applied to estimate Reusability of each class, while binary ULR estimates calculates (reuse proneness) threshold. Applying different classification algorithms such as LM, ANN algorithms, ELM, and evolutionary computing enriched ANN reuse-proneness prediction has been done. The performance assessment affirms that AGA based ANN model outperforms other techniques and hence can be used for earlier aging-resilient Reusability optimization for WoS software design.
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identifying the reusable components from component based system proposed metrics and model
2019Co-Authors: Neelamadhab Padhy, Rasmita Panigrahi, Suresh Chandra SatapathyAbstract:Reusability is the key component from the software development prospective. This job describes a measurement which is popularly called as reprocess measurement to discover and investigate the static activities of the module. This paper proposed a set of Reusability metrics especially partly adaptable, completely changeable and moderately capable modules. The process of Reusability can be measured to degree of module in component-based system. This paper provides the novel model as well as the proposed metrics. We propose Reusability-metric for all categories of components including partially modifiable, fully modifiable as well as for off-the-shelf components. Using Reusability-metric, we draw a Reusability-matrix containing the Reusability ratios of all the different classes of components. This paper introduces selection criteria for components by using the Reusability features of component-based software.
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software Reusability metrics estimation algorithms models and optimization techniques
Computers & Electrical Engineering, 2017Co-Authors: Neelamadhab Padhy, R. P. Singh, Suresh Chandra SatapathyAbstract:Abstract Objective In this paper, the proposed model is intended to employ a novel evolutionary computing-based artificial intelligence or machine learning scheme for regression tests to be used for Reusability estimation. Such enhancement can lead to accurate Reusability pattern estimation, which can be effective for optimal software design purposes. This model is popularly called an aging-resilient software Reusability forecast representation. The proposed system employs predominant object-oriented software metrics, such as Chidamber and Kemerer's metrics to examine Reusability. Here, cumulative metrics, object-oriented metrics, McCabe's metrics, cohesion and a coupling-based Reusability assessment model have been proposed which could be of paramount significance in software design optimization. In this paper, software metrics algorithms and their primary constructions have been developed for estimating the metrics from the UML/class diagrams. It is feasible to derive an efficient and robust Reusability prediction model for web-service products using object-oriented metrics. Here, it was also found that OO-CK metrics, particularly complexity, cohesion and coupling-related metrics can be helpful in predicting Reusability in web-service software products. Considering the above-mentioned key contributions, it can be stated that the proposed research could be of paramount significance in next-generation software computation systems, primarily for software component Reusability, reliability, survivability, aging prediction and stability, and for software excellence assurance purposes.
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utility of an object oriented Reusability metrics and estimation complexity
Indian journal of science and technology, 2017Co-Authors: Neelamadhab Padhy, Suresh Chandra Satapathy, R. P. SinghAbstract:Background/Objectives: In this 21st century, Reusability imparts powerful tools in the software industry. More and less 80% code is reused in the new project. Evaluation of metrics form the software code is now a challenging task as well as how much percentage of code is used from the existing one. This can be achieved by using CK (Chidambaram and Kemmerer Metrics). Methods/Statistical Analysis: There are numerous metrics are defined which distinguish the actual object. The proposed new metrics which is the combinations of one of the CK metrics suite and which calculates the reusable codes in the object oriented programme. Findings: In the inheritance if we take maximum depth of class in the hierarchy then found more chance for Reusability of the inherited metrics. So DIT (Depth of Inheritance) has positive sign on the Reusability of the class. If reasonable value for number of children then more scope of reuse in the class. If we have more number of methods in the class then more impact will be more on the children class and restrictive the possible of reuse. Conclusion: The OOS (Object Oriented System) using the parameterized constructor in C++ programs is more reusable up to some extent. When we will get the larger ethics (values) of proposed Metrics-2 and - 3 then definitely it gives the negative collision on the Reusability. So the constructor having parameters (parameterized Constructor) gives the negative impact on the Reusability of the classes.
Matthew Patrick - One of the best experts on this subject based on the ideXlab platform.
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exploring software Reusability metrics with q a forum data
Journal of Systems and Software, 2020Co-Authors: Matthew PatrickAbstract:Abstract Question and answer (QA it can be used to explore the relationship between software Reusability metrics and difficulties encountered by users, as well as predict the number of difficulties users will face in the future. Q&A forum data can help improve understanding of software reuse, and may be harnessed as an additional resource to evaluate software Reusability metrics.
R. P. Singh - One of the best experts on this subject based on the ideXlab platform.
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Cost-effective and fault-resilient Reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications
Cluster Computing, 2019Co-Authors: Neelamadhab Padhy, R. P. Singh, Suresh Chandra SatapathyAbstract:The exponential rise in software technologies and its significances has demanded academia-industries to ensure low cost software solution with assured service quality and reliability. A low cost and fault-resilient software design is must, where to achieve low cost design the developers or programmers prefer exploiting source or function reuse. However, excessive Reusability makes software vulnerable to get faulty due to increased complexity and aging proneness. Non-deniably assessing Reusability of a class of function in software can enable avoiding any unexpected fault or failure. To achieve it developing a robust and efficient Reusability estimation or prediction model is of utmost significance. On the other hand, the aftermath consequences of excess Reusability caused faults might lead significant losses. Hence assessing cost effectiveness and efficacy of a Reusability prediction model is must for software design optimization. In this paper, we have examined different Reusability prediction models for their cost effectiveness and prediction efficiency over object-oriented software design. At first to examine the Reusability of a class, three key object oriented software metrics (OO-SM); cohesion, coupling and complexity of the software components are used. Furthermore, our proposed cost-efficient Reusability prediction model incorporates Min–Max normalization, outlier detection, Reusability threshold estimation; T test analysis based feature selection and various classification algorithms. Different classifiers including decision tree (DT), Naïve Bayes (NB), artificial neural network (ANN) algorithms, extreme learning machine (ELM), regression algorithms, multivariate adaptive regression spline (MARS) and adaptive genetic algorithm (AGA) based ANN are used for Reusability prediction. Additionally, the cost effectiveness of each Reusability prediction model is estimated, where the overall results have revealed that AGA based ANN as classifier in conjunction with OO-SM, normalization, T test analysis based feature selection outperforms other state-of-art techniques in terms of both accuracy as well as cost-effectiveness.
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Enhanced evolutionary computing based artificial intelligence model for web-solutions software Reusability estimation
Cluster Computing, 2019Co-Authors: Neelamdhab Padhy, R. P. Singh, Suresh Chandra SatapathyAbstract:Ensuring the aging resilient software design can be of paramount significance to enable faultless software system. Particularly assessing Reusability extent of the software components can enable efficient software design. The probability of aging proneness can be characterized based on key OO-SM like cohesion, coupling and complexity of a software component. In this paper, aging resilient software Reusability prediction model is proposed for object oriented design based Web of Service (WoS) software systems. This work introduces multilevel optimization to accomplish a novel Reusability prediction model. Considering coupling, cohesion and complexity as the software characteristics to signify aging proneness, six CK metrics; WMC, CBO, DIT, LCOM, NOC, and RFC are obtained from 100 WoS software. The extracted CK metrics are processed for min–max normalization that alleviates data-unbalancing and hence avoids saturation during learning. The 10-fold Cross-validation followed by outlier detection is considered to enrich data quality for further feature extraction. To reduce computational overheads RSA algorithm is applied. SoftAudit tool is applied to estimate Reusability of each class, while binary ULR estimates calculates (reuse proneness) threshold. Applying different classification algorithms such as LM, ANN algorithms, ELM, and evolutionary computing enriched ANN reuse-proneness prediction has been done. The performance assessment affirms that AGA based ANN model outperforms other techniques and hence can be used for earlier aging-resilient Reusability optimization for WoS software design.
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software Reusability metrics estimation algorithms models and optimization techniques
Computers & Electrical Engineering, 2017Co-Authors: Neelamadhab Padhy, R. P. Singh, Suresh Chandra SatapathyAbstract:Abstract Objective In this paper, the proposed model is intended to employ a novel evolutionary computing-based artificial intelligence or machine learning scheme for regression tests to be used for Reusability estimation. Such enhancement can lead to accurate Reusability pattern estimation, which can be effective for optimal software design purposes. This model is popularly called an aging-resilient software Reusability forecast representation. The proposed system employs predominant object-oriented software metrics, such as Chidamber and Kemerer's metrics to examine Reusability. Here, cumulative metrics, object-oriented metrics, McCabe's metrics, cohesion and a coupling-based Reusability assessment model have been proposed which could be of paramount significance in software design optimization. In this paper, software metrics algorithms and their primary constructions have been developed for estimating the metrics from the UML/class diagrams. It is feasible to derive an efficient and robust Reusability prediction model for web-service products using object-oriented metrics. Here, it was also found that OO-CK metrics, particularly complexity, cohesion and coupling-related metrics can be helpful in predicting Reusability in web-service software products. Considering the above-mentioned key contributions, it can be stated that the proposed research could be of paramount significance in next-generation software computation systems, primarily for software component Reusability, reliability, survivability, aging prediction and stability, and for software excellence assurance purposes.
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utility of an object oriented Reusability metrics and estimation complexity
Indian journal of science and technology, 2017Co-Authors: Neelamadhab Padhy, Suresh Chandra Satapathy, R. P. SinghAbstract:Background/Objectives: In this 21st century, Reusability imparts powerful tools in the software industry. More and less 80% code is reused in the new project. Evaluation of metrics form the software code is now a challenging task as well as how much percentage of code is used from the existing one. This can be achieved by using CK (Chidambaram and Kemmerer Metrics). Methods/Statistical Analysis: There are numerous metrics are defined which distinguish the actual object. The proposed new metrics which is the combinations of one of the CK metrics suite and which calculates the reusable codes in the object oriented programme. Findings: In the inheritance if we take maximum depth of class in the hierarchy then found more chance for Reusability of the inherited metrics. So DIT (Depth of Inheritance) has positive sign on the Reusability of the class. If reasonable value for number of children then more scope of reuse in the class. If we have more number of methods in the class then more impact will be more on the children class and restrictive the possible of reuse. Conclusion: The OOS (Object Oriented System) using the parameterized constructor in C++ programs is more reusable up to some extent. When we will get the larger ethics (values) of proposed Metrics-2 and - 3 then definitely it gives the negative collision on the Reusability. So the constructor having parameters (parameterized Constructor) gives the negative impact on the Reusability of the classes.
Neelamadhab Padhy - One of the best experts on this subject based on the ideXlab platform.
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Cost-effective and fault-resilient Reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications
Cluster Computing, 2019Co-Authors: Neelamadhab Padhy, R. P. Singh, Suresh Chandra SatapathyAbstract:The exponential rise in software technologies and its significances has demanded academia-industries to ensure low cost software solution with assured service quality and reliability. A low cost and fault-resilient software design is must, where to achieve low cost design the developers or programmers prefer exploiting source or function reuse. However, excessive Reusability makes software vulnerable to get faulty due to increased complexity and aging proneness. Non-deniably assessing Reusability of a class of function in software can enable avoiding any unexpected fault or failure. To achieve it developing a robust and efficient Reusability estimation or prediction model is of utmost significance. On the other hand, the aftermath consequences of excess Reusability caused faults might lead significant losses. Hence assessing cost effectiveness and efficacy of a Reusability prediction model is must for software design optimization. In this paper, we have examined different Reusability prediction models for their cost effectiveness and prediction efficiency over object-oriented software design. At first to examine the Reusability of a class, three key object oriented software metrics (OO-SM); cohesion, coupling and complexity of the software components are used. Furthermore, our proposed cost-efficient Reusability prediction model incorporates Min–Max normalization, outlier detection, Reusability threshold estimation; T test analysis based feature selection and various classification algorithms. Different classifiers including decision tree (DT), Naïve Bayes (NB), artificial neural network (ANN) algorithms, extreme learning machine (ELM), regression algorithms, multivariate adaptive regression spline (MARS) and adaptive genetic algorithm (AGA) based ANN are used for Reusability prediction. Additionally, the cost effectiveness of each Reusability prediction model is estimated, where the overall results have revealed that AGA based ANN as classifier in conjunction with OO-SM, normalization, T test analysis based feature selection outperforms other state-of-art techniques in terms of both accuracy as well as cost-effectiveness.
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identifying the reusable components from component based system proposed metrics and model
2019Co-Authors: Neelamadhab Padhy, Rasmita Panigrahi, Suresh Chandra SatapathyAbstract:Reusability is the key component from the software development prospective. This job describes a measurement which is popularly called as reprocess measurement to discover and investigate the static activities of the module. This paper proposed a set of Reusability metrics especially partly adaptable, completely changeable and moderately capable modules. The process of Reusability can be measured to degree of module in component-based system. This paper provides the novel model as well as the proposed metrics. We propose Reusability-metric for all categories of components including partially modifiable, fully modifiable as well as for off-the-shelf components. Using Reusability-metric, we draw a Reusability-matrix containing the Reusability ratios of all the different classes of components. This paper introduces selection criteria for components by using the Reusability features of component-based software.
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software Reusability metrics estimation algorithms models and optimization techniques
Computers & Electrical Engineering, 2017Co-Authors: Neelamadhab Padhy, R. P. Singh, Suresh Chandra SatapathyAbstract:Abstract Objective In this paper, the proposed model is intended to employ a novel evolutionary computing-based artificial intelligence or machine learning scheme for regression tests to be used for Reusability estimation. Such enhancement can lead to accurate Reusability pattern estimation, which can be effective for optimal software design purposes. This model is popularly called an aging-resilient software Reusability forecast representation. The proposed system employs predominant object-oriented software metrics, such as Chidamber and Kemerer's metrics to examine Reusability. Here, cumulative metrics, object-oriented metrics, McCabe's metrics, cohesion and a coupling-based Reusability assessment model have been proposed which could be of paramount significance in software design optimization. In this paper, software metrics algorithms and their primary constructions have been developed for estimating the metrics from the UML/class diagrams. It is feasible to derive an efficient and robust Reusability prediction model for web-service products using object-oriented metrics. Here, it was also found that OO-CK metrics, particularly complexity, cohesion and coupling-related metrics can be helpful in predicting Reusability in web-service software products. Considering the above-mentioned key contributions, it can be stated that the proposed research could be of paramount significance in next-generation software computation systems, primarily for software component Reusability, reliability, survivability, aging prediction and stability, and for software excellence assurance purposes.
-
utility of an object oriented Reusability metrics and estimation complexity
Indian journal of science and technology, 2017Co-Authors: Neelamadhab Padhy, Suresh Chandra Satapathy, R. P. SinghAbstract:Background/Objectives: In this 21st century, Reusability imparts powerful tools in the software industry. More and less 80% code is reused in the new project. Evaluation of metrics form the software code is now a challenging task as well as how much percentage of code is used from the existing one. This can be achieved by using CK (Chidambaram and Kemmerer Metrics). Methods/Statistical Analysis: There are numerous metrics are defined which distinguish the actual object. The proposed new metrics which is the combinations of one of the CK metrics suite and which calculates the reusable codes in the object oriented programme. Findings: In the inheritance if we take maximum depth of class in the hierarchy then found more chance for Reusability of the inherited metrics. So DIT (Depth of Inheritance) has positive sign on the Reusability of the class. If reasonable value for number of children then more scope of reuse in the class. If we have more number of methods in the class then more impact will be more on the children class and restrictive the possible of reuse. Conclusion: The OOS (Object Oriented System) using the parameterized constructor in C++ programs is more reusable up to some extent. When we will get the larger ethics (values) of proposed Metrics-2 and - 3 then definitely it gives the negative collision on the Reusability. So the constructor having parameters (parameterized Constructor) gives the negative impact on the Reusability of the classes.
Natasha Boskic - One of the best experts on this subject based on the ideXlab platform.
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learning objects design what do educators think about the quality and Reusability of learning objects
International Conference on Advanced Learning Technologies, 2003Co-Authors: Natasha BoskicAbstract:We present the theoretical framework for the research study conducted on faculty assessment of quality and Reusability of learning objects. It reviews a) four critical challenges, educators face when selecting learning objects: the learner's cognitive process, levels of interaction, evaluation, and instructional design; b) learning object repositories and learning object evaluation; and c) Reusability of learning objects.
