Software Quality Assesment using COCOMO-II Metrics with ABC and NN
Naveen Malik1, Sandip Kumar Goyal2, Vinisha Malik3
1Naveen Malik*, PhD Research Scholar, Department of Computer Science & Engineering, Maharishi Markandeshwar (Deemed To Be University) Mullana, Ambala , India.
2Sandip Kumar Goyal, Professor, Department of Computer Science & Engineering, Maharishi Markandeshwar (Deemed To Be University) Mullana, Ambala, India.
3Vinisha Malik, PhD Research Scholar, Department of Computer Science & Engineering, Maharishi Markandeshwar (Deemed To Be University) Mullana, Ambala , India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2982-2988 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5633029320/2020©BEIESP | DOI: 10.35940/ijeat.C5633.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: : Time, cost and quality predictions are the key aspects of any software development system. Loses that result due to wrong estimations may lead to irresistible damage. It is observed that a badly estimated project always results into a bad quality output as the efforts are put in the wrong direction. In the present study, author proposed ABC-COCOMO-II as a new model and tried to enhance the extent of accuracy in effort quality assessment through effort estimation. In the proposed model author combined the strengths of COCOMO-II (Constructive Cost Model) with the Artificial Bee Colony (ABC) and Neural Network (NN). In the present work, ABC algorithm is used to select the best solution, NN is used for the classification purpose to improve the quality estimation using COCOMO-II. The results are compared and evaluated with the pre-existing effort estimation models. The simulation results had shown that the proposed combination outperformed in terms of quality estimation with small variation of 5-10% in comparison to the actual effort, which further leads to betterment of the quality. More than 90% projects results into high quality output for the proposed algorithmic architecture.
Keywords: Effort estimation, Artificial Bee Colony (ABC), Neural Network (NN), Constructive Cost Model (COCOMO-II)