Application of DEA to measure the efficiency of Open Source software projects
محورهای موضوعی : Data Envelopment Analysis
Ehsan Zanboori
1
(
Department of Mathematics, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran
)
فاطمه رستمی
2
(
Masters Student, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran
)
سعید قبادی
3
(
دانشگاه آزاد اسلامی واحد خمینی شهر اصفهان
)
کلید واژه: DEA, Ranking, Computer software, OSS projects,
چکیده مقاله :
This paper evaluates the relative performance of open source software projects by evaluating multiple project inputs and multiple project outputs by using data envelopment analysis (DEA) model. The DEA model produces an efficiency score for each project based on project inputs and outputs. One of the important issues in data envelopment analysis is ranking DMUs. In this paper, open source software projects (OSS) are considered as decision making units which consume inputs to generate outputs. In this article, three standard Data Envelopment Analysis (DEA) models are used to evaluate the open source software projects. Also, super-efficiency model are used for ranking. Due to the inability of the models to rank projects, the AP-super efficiency model (the most important and popular method for ranking units) has been used for ranking OSS projects.The result of this research is a practical model that can be used by OSS project developers in order to evaluate the relative performance of their projects and make decision for their sources. Also, OSS projects can now be adequately ranked and evaluated according to project performance.
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261-1264.
Banker, R.D., Charnes, A., & Cooper, W. W. (1984). Models for the estimation of technical and scale efficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Crowston, K., & Shamshurin I. (2017). Core-periphery communication and the success of free/libre open source software projects, Journal of Internet Services and Applications, 8(10), 1-11.
Fitzgerald, B., (2018). Crowdsourcing software development. Software engineering und software management 2018. Bonn: Gesellschaft für Informatik. (S. 23-24).
Flitman, A. (2003). Towards meaningful benchmarking of software development team productivity, Benchmarking: An International Journal, 10(4), 382-99.
Gao N., Richard S. S., Zichen Z., & Zhijian W., (2021). A Survey of open source statistical software (OSSS) and their data processing functionalities, International Journal of Open Source Software and Processes, 12(1), 1-20.
Hosseinzadeh Lotfi, F., Jahanshahloo, G. R., Khodabakhshi, M., Rostami-Malkhalifeh, M., Moghaddas, Z., & Vaez-Ghasemi, M. (2013). A review of ranking models in data envelopment analysis, Journal of Applied Mathematics, 2013, 1-20.
Jorgensen, N. (2001). Putting it all in the trunk: incremental software development in the FreeBSD open source project, Information Systems Journal, 11(4), 321-336.
Kalina, I. & Czyzycki, A. (2005). The ins and outs of open source, Consulting to Management, 16(3), 41-7.
Lacy, S. (2005). Open source: now it’s an ecosystem, Business Week Online, October, p. 3.
Monteiro, N. S., Fontinele, A. C., Campelo, D., & Soares Mookhey A. (2020). Provision of adaptive guard band in elastic optical networks", Journal of Internet Services and Applications , 11(5), 1-19.
Paradi, J.C., Reese, D.N., & Rosen, D. (1997). Applications of DEA to measure the efficiency of software production at two large Canadian banks, Annals of Operations Research, 73, 91-115.
Payne, C. (2002). On the security of open source software, Information Systems Journal, 12(1), 61-78.
Pereira, J. (2021). Leveraging final degree projects for open source software contributions, Electronics, 10(10), 1-16.
Heidary, S., Zanboori, E., & Parvin, H. (2018). A Hybrid model based on neural network and data envelopment analysis model for evaluation of unit performance”, Iranian Journal of Optimization, 10(2): 2, 101-112.
Stensrud, E. & Myrtveit, I. (2003). Identifying high performance ERP projects, IEEE Transactions on Software Engineering, 29(5), 398-415.
Yang, Z. & paradi, J.C. (2004). DEA evaluation of Y2K software retrofit program, IEEE Transactions on Engineering Management, 51(3), 279-287.
Zanboori, E., Hosseinzadeh Lotfi, F., Rostamy-Malkhalifeh M. & Jahanshahloo, G. R. (2014). Calculating Super Efficiency of DMUs for Ranking Units in Data Envelopment Analysis Based on SBM Model. The Scientific World Journal, 2014, 1-7.
