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.

Poonam Rijwani 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(IJACSA), 9(9), 2018. http://dx.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},
doi = {10.14569/IJACSA.2018.090972},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090972},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {9},
author = {Poonam Rijwani and Sonal Jain}
}