Paper Info
Title
Improved software cost estimation models: A new perspective based on evolution in Dynamic Environment.
Abstract
Software cost estimation is the process of predicting the most realistic and valid amount of effort necessary for the development of any software. The cost estimation of any software is a difficult assignment due to the involvement of many factors that anyhow affect the estimation process. In literature, many cost estimation models have been developed for more than a decade to maintain accuracy in estimation of the cost of software projects. But, it is found that these models are inefficient to estimate the exact cost of software development because of uncertainties and lack of accuracy associated with them. In this paper, Alla F. Sheta models have been taken for optimization, which are the modified versions of the very famous Boehm's COCOMO model. Parameters of the Sheta models have been tuned enough by the proposed method to estimate and minimize the consequences of different factors that affect the overall software development cost. Experimental work has been carried out in MATLAB environment and analysis of results is performed on the basis of Magnitude of Relative Error (MRE), Prediction (PRED) at 0.25, Value Accounted For (VAF) and Mean Magnitude of Relative Error (MMRE). Estimation accuracy of the proposed work is tested on NASA software project dataset. It is found that the proposed method shows good estimation capabilities over other state-of-the-art cost estimation models.
Year
DOI
Venue
2018
10.3233/JIFS-169707
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Software cost estimation,EAMD,COCOMO model,NASA dataset,natural phenomena
Cost estimation models,Software,Artificial intelligence,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
35
2
1064-1246
Citations 
PageRank 
References 
0
0.34
14
Authors
4
Name
Order
Citations
PageRank
Ashish Tripathi172.51
K. K. Mishra233.45
Shailesh Tiwari3146.95
Naveen Kumar413.09