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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	2022 Second International Conference on Distributed Computing and High Performance Computing (DCHPC) s. 82 - 85
Hlavní autoři:	Gandomani, Taghi Javdani, Dashti, Maedeh, Nafchi, Mina Zaiei
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 02.03.2022
Témata:	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)
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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