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29 November 2016

Attention to Authors

The Volume 1, Number 2, November 2016 issue of IJARCE has been published and hardcopies have been sent to authors. Notice that, arrival process of the hardcopies, to your addresses, will takes between 12 to 30 days depending on your country of origin.

 

10 October 2016

Attention to Authors

The Volume 1, Number 1, September 2016 issue of IJARCE has been published and hardcopies have been sent to authors. Notice that, arrival process of the hardcopies, to your addresses, will takes between 5 to 30 days depending on your country of origin.

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A New Model for Software Cost Estimation Using Bat Algorithm

Artical 7 ; volume1 ; number1 ; Page: 53-60 ; PDF Download

Authors

Muhammad Ibrahim,

Department of Computer Science, COMSATS Institute of Information Technology, Wah Campus, Pakistan.

Abstract

Several software projects are developed and produced in software companies annually. Rapid environmental and technological changes, cost constraints, mismanagement, lack of skills of the project managers and inaccurate estimation cause many of these projects to fail in practice. In large software projects, software cost estimation has always been one of the main problems for the project manager and project-development team. The main SCE criterion is to determine the effort required to complete the project. The effort needed is usually determined according to people / month. There is a direct relationship between the amount of effort and the time required to complete a project. And the time can be used to estimate costs. Therefore, the main step in SCE is determining the amount of effort required to complete projects. COCOMO model is the algorithmic model for SCE. In COCOMO model, the emphasis is on the effort coefficients for better performance. In order to measure the relationship between the effort and time, i.e., the relation between the number of code lines and effort coefficients, COCOMO uses some linear formula that utilizes data from NASA software projects. In this paper, we have used bat algorithm to accurately estimate the effort coefficients and to reduce MMRE. Evaluation has been conducted on NASA60 dataset; the results show that BA has much less error compared with COCOMO model.

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