Software Effort Estimation Using Modified Fuzzy C Means Clustering and Hybrid ABC-MCS Optimization in Neural Network

In a software development process, effective cost estimation is the most challenging activity. Software effort estimation is a crucial part of cost estimation. Management cautiously considers the efforts and benefits of software before committing the required resources to that project or order for a...

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Published in:	Journal of intelligent systems Vol. 29; no. 1; pp. 251 - 263
Main Authors:	Azath, Hussain, Mohanapriya, Marimuthu, Rajalakshmi, Somasundaram
Format:	Journal Article
Language:	English
Published:	Berlin De Gruyter 01.01.2020 Walter de Gruyter GmbH
Subjects:	Clustering Cost estimation Datasets Estimation fuzzy Fuzzy logic neural network Neural networks Optimization Optimization algorithms Search algorithms Soft computing Software development Swarm intelligence
ISSN:	0334-1860, 2191-026X
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Abstract	In a software development process, effective cost estimation is the most challenging activity. Software effort estimation is a crucial part of cost estimation. Management cautiously considers the efforts and benefits of software before committing the required resources to that project or order for a contract. Unfortunately, it is difficult to measure such preliminary estimation, as it has only little information about the project at an early stage. In this paper, a new approach is proposed; this is based on reasoning by the soft computing approach to calculate the effort estimation of the software. In this approach, rules are generated based on the input dataset. These rules are then clustered for better estimation. In our proposed method, we use modified fuzzy C means for clustering the dataset. Once the clustering is done, various rules are obtained and these rules are given as the input to the neural network. Here, we modify the neural network by incorporating optimization algorithms. The optimization algorithms employed here are the artificial bee colony (ABC), modified cuckoo search (MCS), and hybrid ABC-MCS algorithms. Hence, we obtain three optimized sets of rules that are used for the effort estimation process. The performance of our proposed method is investigated using parameters such as the mean absolute relative error and mean magnitude of relative error.
AbstractList	In a software development process, effective cost estimation is the most challenging activity. Software effort estimation is a crucial part of cost estimation. Management cautiously considers the efforts and benefits of software before committing the required resources to that project or order for a contract. Unfortunately, it is difficult to measure such preliminary estimation, as it has only little information about the project at an early stage. In this paper, a new approach is proposed; this is based on reasoning by the soft computing approach to calculate the effort estimation of the software. In this approach, rules are generated based on the input dataset. These rules are then clustered for better estimation. In our proposed method, we use modified fuzzy C means for clustering the dataset. Once the clustering is done, various rules are obtained and these rules are given as the input to the neural network. Here, we modify the neural network by incorporating optimization algorithms. The optimization algorithms employed here are the artificial bee colony (ABC), modified cuckoo search (MCS), and hybrid ABC-MCS algorithms. Hence, we obtain three optimized sets of rules that are used for the effort estimation process. The performance of our proposed method is investigated using parameters such as the mean absolute relative error and mean magnitude of relative error.
Author	Mohanapriya, Marimuthu Rajalakshmi, Somasundaram Azath, Hussain
Author_xml	– sequence: 1 givenname: Hussain surname: Azath fullname: Azath, Hussain email: writetoazath@yahoo.com organization: Assistant Professor, CSE, KGiSL Institute of Technology, Coimbatore, India – sequence: 2 givenname: Marimuthu surname: Mohanapriya fullname: Mohanapriya, Marimuthu organization: Associate Professor/Department of CSE, CIT, Coimbatore, India – sequence: 3 givenname: Somasundaram surname: Rajalakshmi fullname: Rajalakshmi, Somasundaram organization: Professor & Head, Department of CSE, Cheran College of Engineering, Karur, India
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SubjectTerms	Clustering Cost estimation Datasets Estimation fuzzy Fuzzy logic neural network Neural networks Optimization Optimization algorithms Search algorithms Soft computing Software development Swarm intelligence
Title	Software Effort Estimation Using Modified Fuzzy C Means Clustering and Hybrid ABC-MCS Optimization in Neural Network
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