Facebook pixel tracking

The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

Contact Info
Email info@thesai.org
Website thesai.org
Follow Us
Contact Info
Email info@thesai.org
Follow Us
Research Article | Open Access |

Level of Confidence in Software Effort Estimation by an Intelligent Fuzzy – Neuro - Genetic Approach

Author 1: Poonam Rijwani Author 2: Sonal Jain
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 9, No. 9 · Published 2018

DOI: https://doi.org/10.14569/IJACSA.2018.090972

Download PDF Cite Call for Papers

Abstract

Organizations are struggling to deliver the expected software functionality and quality in scheduled time and prescribed budget. Despite availability of numerous advanced effort estimation techniques overestimation and underestimation occur on a vast scale and results in project failures and significant loss to the organization. The paper proposes machine learning based approach to calculate the optimized effort and level of confidence. Genetically trained neural network evaluates the optimum effort for given COCOMO II variables. The level of confidence is evaluated by fuzzy logic and indicates the percentage that the predicted effort will not exceed the limits.

Keywords

COCOMO II artificial neural networks genetic algorithm fuzzy logic

How to Cite this Article

Rijwani, P., & Jain, S. (2018). Level of Confidence in Software Effort Estimation by an Intelligent Fuzzy – Neuro - Genetic Approach. International Journal of Advanced Computer Science and Applications, 9(9). https://doi.org/10.14569/IJACSA.2018.090972

Rijwani, Poonam, and Sonal Jain. "Level of Confidence in Software Effort Estimation by an Intelligent Fuzzy – Neuro - Genetic Approach." International Journal of Advanced Computer Science and Applications, vol. 9, no. 9, 2018, https://doi.org/10.14569/IJACSA.2018.090972.

@article{Rijwani2018,
  title     = {Level of Confidence in Software Effort Estimation by an Intelligent Fuzzy – Neuro - Genetic Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {9},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Poonam Rijwani and Sonal Jain},
  doi       = {10.14569/IJACSA.2018.090972},
  url       = {https://doi.org/10.14569/IJACSA.2018.090972}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

Back to Issue
IJACSA Journal Cover

IJACSA