Estimation of software reusability for component based system using soft computing techniques

Singh, Charu; Pratap, Amrendra; Singhal, Abhishek

doi:10.1109/confluence.2014.6949307

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…In [7], the author did estimation of software reusability in case of component-based system. They made used of several soft computing techniques in their research.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized Component based Selection using LSTM Model by Integrating Hybrid MVO-PSO Soft Computing Technique

Banga¹,

Bhatia²

2021

Adv. sci. technol. eng. syst. j.

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Research focused on training and testing of dataset afterOptimizing Software Component with the help of deep neural network mechanism. Optimized components are selected for training and testing to improve the accuracy at the time of software selection. Selected components are required to be attuned and accommodating as per requirement. Soft computing mechanism such as PSO and MVO will be used for optimization. Deep Neural-Network mechanism is performing training and testing to get the confusion metrics of true positive/negative and false positive/negative. The accuracy, precision, recall value and fscore are computed to assure accuracy of proposed work. The proposed mechanism is making use of LSTM layer for more accurate output. Proposed research is exploring inadequacy of existing research and extent of incorporation of previous mechanism to soft computing mechanism in CBSE.

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“…In [7], the author did estimation of software reusability in case of component-based system. They made used of several soft computing techniques in their research.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized Component based Selection using LSTM Model by Integrating Hybrid MVO-PSO Soft Computing Technique

Banga¹,

Bhatia²

2021

Adv. sci. technol. eng. syst. j.

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“…Classification of software components based on their technical description is considered in [9]. Application of NNs for solving the problem of estimating reusable components is considered in [10] based on the indicators of modularity, interface complexity, supportability, flexibility, and adaptability.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…However, the solutions discussed in [6][7][8][9][10][11][12] require conversion of textual descriptions of software components, their templates and web services into sets of the numerical data necessary for the NN functioning. In addition, in the course of application of the results described in [11,12], a necessity of reverse conversion of the numerical result of the NN operation to a web-service description arises in a number of cases.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…It should be noted that the initial data for the neural nets include their formal descriptions in the course of solving problems of classification, identification and search for reusable web services or software components. Such descriptions can be represented in the following ways: a) a special model based on quantitative metrics for estimating the reuse of software components [10]; b) representation of a component or service as a set of formal descriptions of their functions or interfaces (including application of WSDL and similar formal languages) [9,11]; c) a formal component description based on the diagrams of the UML class [6].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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The ADALINE neuron modification for solving the problem on searching for the reusable functions of the information system

Muhamed

Mohammed

Evlanov

et al. 2018

EEJET

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“…Some simulators also visualize the physical structure of the neural network. Neural network model also used to represent the complex non-linear associations (input-output relationship) and functions [2]. …”

Section: Neural Networkmentioning

confidence: 99%

Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

Sharma

Gandhi

2017

IJET

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There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate estimation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.

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Estimation of software reusability for component based system using soft computing techniques

Cited by 10 publications

References 12 publications

Optimized Component based Selection using LSTM Model by Integrating Hybrid MVO-PSO Soft Computing Technique

Optimized Component based Selection using LSTM Model by Integrating Hybrid MVO-PSO Soft Computing Technique

The ADALINE neuron modification for solving the problem on searching for the reusable functions of the information system

Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

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