TY - GEN
T1 - Optimizing COCOMO II parameters using artificial bee colony method
AU - Pratama, Rayandra Yala
AU - Sarno, Riyanarto
AU - Sholiq,
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/19
Y1 - 2018/1/19
N2 - Cost estimation is a crucial and essential process in software industry. The more accurate cost estimated, the more efficient the project became. This cost estimation become a challenge for software industry to bring accurate result. There are many methods to solve this problem. Constructive Cost Model is usual method that is used to estimate software cost. This model was proposed in 1981 by using regression analysis with 63 types of project data. In 2000, COCOMO II was introduced. This new model of COCOMO use cost drivers, scale factors, and project size that measured by line of code. COCOMO II has 4 parameters A, B, C and D. However, using this parameters are not guarantee accurate result. This paper proposed Bee Colony Optimization to calibrate the COCOMO II model parameter to be more accurate for effort estimation. This Bee Colony Optimization is applied on Nasa93 dataset that consisted of 93 projects which each project has 22 cost drivers, project's size, effort, and development time. This proposed method gives MMRE result 50.584% on effort and 14.192% on development time.
AB - Cost estimation is a crucial and essential process in software industry. The more accurate cost estimated, the more efficient the project became. This cost estimation become a challenge for software industry to bring accurate result. There are many methods to solve this problem. Constructive Cost Model is usual method that is used to estimate software cost. This model was proposed in 1981 by using regression analysis with 63 types of project data. In 2000, COCOMO II was introduced. This new model of COCOMO use cost drivers, scale factors, and project size that measured by line of code. COCOMO II has 4 parameters A, B, C and D. However, using this parameters are not guarantee accurate result. This paper proposed Bee Colony Optimization to calibrate the COCOMO II model parameter to be more accurate for effort estimation. This Bee Colony Optimization is applied on Nasa93 dataset that consisted of 93 projects which each project has 22 cost drivers, project's size, effort, and development time. This proposed method gives MMRE result 50.584% on effort and 14.192% on development time.
KW - Bee Algorithm
KW - Bee Colony Optimization
KW - COCOMO
KW - MRE
KW - Software Cost Estimation
UR - http://www.scopus.com/inward/record.url?scp=85050526195&partnerID=8YFLogxK
U2 - 10.1109/ICTS.2017.8265657
DO - 10.1109/ICTS.2017.8265657
M3 - Conference contribution
AN - SCOPUS:85050526195
T3 - Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
SP - 125
EP - 129
BT - Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Information and Communication Technology and System, ICTS 2017
Y2 - 31 October 2017 through 31 October 2017
ER -