Abstract
Software cost estimation SCE is directly related to quality of software. The paper presents a hybrid approach that is an amalgamation of algorithmic (parametric models) and non-algorithmic (expert estimation) models. Algorithmic model uses COCOMO II while non algorithmic utilizes Neuro-Fuzzy technique that can be further used to estimate accuracy in irregular functions. For generalization of the model, Neuro-fuzzy membership functions have been used and simulated using mathematical tool MATLAB. Also, the proposed model has been validated with traditional COCOMO model (COCOMO 81) by using NASA software project data. The experimental results suggest that the proposed model gives better SCE as compared to its traditional counterpart.
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Appendix A
Appendix A
Costar cost drivers
Personnel factors | COCOMO II post architecture | COCOMO 81, COCOMO 85 | |
|---|---|---|---|
ACAP | Analyst capability | Yes | Yes |
APEX | Applications experience | Yes | Yes |
AEXP | |||
PCAP | Programmer capability | Yes | Yes |
LEXP | Programming language experience | Yes | |
VEXP | Virtual machine experience | Yes | |
PERS | Personnel capability | ||
LTEX | Language and tool experience | Yes | |
Product factors | |||
RELY | Required software reliability | Yes | Yes |
DATA | Database size | Yes | Yes |
CPLX | Software product complexity | Yes | Yes |
RUSE | Required reusability | Yes | |
Platform factors | |||
TIME | Execution time constraint | Yes | Yes |
STOR | Main storage constraint | Yes | Yes |
VIRT | Virtual machine volatility | Yes | |
PVOL | Platform volatility | Yes | |
PDIF | Platform difficulty | ||
Project factors | |||
TOOL | Use of software tools | Yes | Yes |
FCIL | Facilities | ||
RVOL | Requirements volatility | ||
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Kaur, I., Narula, G.S., Wason, R. et al. Neuro fuzzy—COCOMO II model for software cost estimation. Int. j. inf. tecnol. 10, 181–187 (2018). https://doi.org/10.1007/s41870-018-0083-6
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DOI: https://doi.org/10.1007/s41870-018-0083-6