Open AccessSoftware Quality Assesment using COCOMO II Metrics with ABC and NN
Author(s) -
Naveen Malik,
Sandip Kumar Goyal,
Vinisha Malik
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c5633.029320
Subject(s) - cocomo , computer science , quality (philosophy) , constructive , software , estimation , key (lock) , artificial neural network , data mining , artificial intelligence , reliability engineering , machine learning , operations research , software development , engineering , systems engineering , process (computing) , software construction , philosophy , computer security , epistemology , programming language , operating system
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.