Application of Hybrid Techniques to Forecasting Accurate Software Cost Estimation
V. Venkataiah1, Ramakanta Mohanty2, M. Nagaratna3

1V. Venkataiah, CMR College of Engineering & Technology, Medchal, Hyderabad (Telangana), India.
2Ramakanta Mohanty, Keshav Memorial Institute of Technology, Hyderabad (Telangana), India.
3M. Nagaratna, JNTUH College of Engineering, Kukatpally, Hyderabad (Telangana), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 04 April 2019 | Manuscript Published on 18 April 2019 | PP: 408-412 | Volume-7 Issue-6S March 2019 | Retrieval Number: F02800376S19/2019©BEIESP
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Abstract: In a competitive business world, software development is a challenging task at the primary stage of the life cycle, due to in-complete raw material and dynamically changing environment of technology in the development of the software industry. As a result inferiority software product delivered to the customer. Hence, facing a lot of problems, and wasting of time. In fact, the software quality, the budget, effort and timeliness of the development of the software product are often crucial forms of an organization to achieve success. Moreover, the interaction between vendor and development process is important. A considerable amount of models has been proposed over the most recent 3 decades. One of them the common model is the Construct Cost Mod-el in this area, and it is quite straight forward method to estimate of effort at an initial stage of software development. Shockingly, the COCOMO strategy neglected to manage the certain nonlinearity and the connection between the attributes of the project effort. In this article, we propose the application of hybrid methodology for tuning parameters of the COCOMO model which give an accurate estimated cost for project development. The COCOMO 81, IBMDPS, COCOMO NASA 2 and DESHARNAIS are used to test the performance of the proposed model.
Keywords: Particle Swarm Optimization (PSO), Construct Cost Model (COCOMO), K-Means Algorithm, Software Cost Estimation (SCE).
Scope of the Article: Software Engineering Methodologies