Software Cost Estimation using Hybrid Algorithm

Software Cost Estimation using Hybrid Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-37 Number-2
Year of Publication : 2016
Authors : Shivani Sharma, Aman Kaushik, Abhishek Tomar
DOI :  10.14445/22315381/IJETT-V37P212

Citation 

Shivani Sharma, Aman Kaushik, Abhishek Tomar"Software Cost Estimation using Hybrid Algorithm", International Journal of Engineering Trends and Technology (IJETT), V37(2),62-71 July 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Software cost estimation is the vital step to start any project. It gives us the outline of effort, resources and time required for a project. Accomplishment of software enhancement depends on cost estimation. It is really tough to meet approximate cost with actual cost. Such as Software size has remained highest significant factor in software which is increasing day by day, due to which we have realized a huge amount of increase in complexity as well as in size of software. A project will be enabled a success if all the necessities can be fulfilled, the cost is not excessive, did not pass through the strategy that has been planned. There are variousbudget assessment techniques to compute cost of the development and Function point analysis (FPA) is the technique of calculating the dimension of software.The benefit is that it can avoid source code error when selecting dissimilar programming languages.The key objectives of this study we are computing budget of project based on Top down method in which we will compute function points of each module. The whole process will be done by Ant colony optimization algorithm. To compare and evaluate the outcomes of the proposed algorithm with K Modes algorithm and RF model and it has been noticed that when we have compared with K modes and RF model then proposed work gives better results.

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Keywords
Software cost estimation, Cost estimation methods, Ant colony optimization, K modes, and RF model.