Hybrid Genetic-Environmental Adaptation Algorithm to Improve Parameters of COCOMO for Software Cost Estimation

The software cost estimation (SCE) problem is one of the major challenges in software engineering. Inaccurate cost and time estimation in a software project may lead to devastating damage to software companies. To deal with this issue, software researchers have made significant efforts during recent...

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Veröffentlicht in:2022 Second International Conference on Distributed Computing and High Performance Computing (DCHPC) S. 82 - 85
Hauptverfasser: Gandomani, Taghi Javdani, Dashti, Maedeh, Nafchi, Mina Zaiei
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 02.03.2022
Schlagworte:
Adaptation models
Computational modeling
Constructive Cost Model (COCOMO)
Costs
environment adaptation (EA) algorithm
Estimation
genetic algorithm (GA)
Predictive models
Software
Software algorithms
software cost estimation (SCE)
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Zusammenfassung:The software cost estimation (SCE) problem is one of the major challenges in software engineering. Inaccurate cost and time estimation in a software project may lead to devastating damage to software companies. To deal with this issue, software researchers have made significant efforts during recent years to improve and modify the available SCE models, one widely-used model of which is the Constructive Cost Model (COCOMO). This research aims to optimize the coefficients of a standard COCOMO model for SCE by combining genetic algorithm (GA) and environmental adaptation (EA) methods. The results indicate that the EA algorithm can solve the divergence issue of the genetic algorithm and optimize the coefficients of the COCOMO model as well. Moreover, the accuracy of the SCE in the case of combining GA and EA algorithms is 8% higher than when these algorithms are separately adopted.
DOI:10.1109/DCHPC55044.2022.9732107