Chaos-based Modified Morphological Genetic Algorithm for Software Development Cost Estimation

2018 ◽  
pp. 31-40 ◽  
Author(s):  
Saurabh Bilgaiyan ◽  
Kunwar Aditya ◽  
Samaresh Mishra ◽  
Madhabananda Das
Keyword(s):  
Genetic Algorithm ◽  
Software Development ◽  
Cost Estimation ◽  
Development Cost

An evolutionary morphological approach for software development cost estimation

2012 ◽  
Vol 32 ◽  
pp. 285-291 ◽  
Author(s):  
Ricardo de A. Araújo ◽  
Adriano L.I. Oliveira ◽  
Sergio Soares ◽  
Silvio Meira
Keyword(s):  
Software Development ◽  
Cost Estimation ◽  
Development Cost ◽  
Morphological Approach

Software development cost estimation using wavelet neural networks

2008 ◽  
Vol 81 (11) ◽  
pp. 1853-1867 ◽  
Author(s):  
K. Vinay Kumar ◽  
V. Ravi ◽  
Mahil Carr ◽  
N. Raj Kiran
Keyword(s):  
Neural Networks ◽  
Software Development ◽  
Cost Estimation ◽  
Development Cost ◽  
Wavelet Neural Networks

scholarly journals A Study of Software Development Cost Estimation Techniques and Models

2020 ◽  
Vol 39 (2) ◽  
pp. 413-431 ◽  
Author(s):  
Junaid Rashid ◽  
Muhammad Wasif Nisar ◽  
Toqeer Mahmood ◽  
Amjad Rehman ◽  
Yasser Arafat Syed
Keyword(s):  
Software Development ◽  
Cost Estimation ◽  
Hybrid Approach ◽  
Parametric Models ◽  
Development Effort ◽  
Development Cost ◽  
Software Development Effort ◽  
Percent Accuracy ◽  
Single Technique ◽  
Estimation Models

SDCE (Software Development Cost Estimation) has always been an interesting and budding field in Software Engineering. This study supports the SDCE by exploring its techniques and models and collecting them in one place. This contribution in the literature will assist future researchers to get maximum knowledge about SDCE techniques and models from one paper and to save their time. In this paper, we review numerous software development effort and cost estimation models and techniques, which are divided into different categories. These categories are parametric models, expertise-based techniques, learning-oriented techniques, dynamicsbased models, regression-based techniques, fuzzy logic-based methods, size-based estimation models, and composite techniques. Some other techniques which directly do not lie in any specific category are also briefly explained. We have concluded that no single technique is best for all situations; rather they are applicable in different nature of projects. All techniques have their own pros and cons and they are challenged by the rapidly changing software industry. Since no single technique gives a hundred percent accuracy, that is why one technique and model should not be preferred over all others. We recommend a hybrid approach for SDCE because in this way the limitations of one model and technique are complemented by the merits of the other model/technique. We also recommend a model calibration to obtain accurate results because if a model was developed in a different environment, we cannot expect reliable estimates from it in a completely new environment.

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