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29 November 2016

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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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Ant Colony based Meta-Heuristic Algorithms for Software Cost Estimation

Artical 1 ; volume1 ; number1 ; Page: 05 - 15 ; PDF Download

Authors

Isa Maleki , Laya Ebrahimi , Mitra Khanjari Japelaghi

Young Researchers and Elite Club, Urmia Branch, Islamic Azad University, Urmia, Iran.

Young Researchers and Elite Club, Urmia Branch, Islamic Azad University, Urmia, Iran.

Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

In software development and software project management, Software Cost Estimation (SCE) will be considered a major step in the start of projects. SCE is one of the main activities at the decisions of software's time and expense management which has a special status in a software project. SCE in software development is considered as a key parameter in software project management. Therefore, to achieve the basic goals requires accurate and reliable cost estimate. Actual estimate in software development is based on effective factors that its accurate value should be recognized using algorithmic models and Artificial Intelligence (AI). Boehm used COCOMO model for SCE which is an algorithmic model in 1981. Algorithmic models such as COCOMO are based on criteria such as the number of lines of code or the Function Point (FP). In COCOMO model, project development and then the cost is calculated by such units. Therefore, the lower accuracy and unreliability of the algorithmic models creates a substantial risk in software projects, so, regularly estimating the cost throughout the project is necessary and it should be compared with other techniques. In the meantime, meta-heuristic algorithms in recent years have made good progress in the area of software and it has been used widely in SCE. Among meta-heuristic algorithms, Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO) used to optimize the issues based on population and they have good effects in optimizing estimation factors. In this paper, a hybrid model DE-ACO, PSO-ACO and ABC-ACO based on ACO algorithm have been proposed for optimization based on effective factors in COCOMO model. Test results show that hybrid models have less magnitude of relative error (MRE) and Mean MRE (MMRE) in estimating software project cost in comparison with COCOMO model.

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