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Blockchain-based Software Effort Estimation: An Empirical Study

Authors
Ahmed, MansoorIqbal, NaeemHussain, FarazKhan, Murad-AliHelfert, MarkusImran,Kim, Jungsuk
Issue Date
Oct-2022
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Keywords
Analogy-based estimation; Blockchain; Blockchain-based software engineering; Blockchains; Error analysis; Estimation; Estimation error; Group-based estimation; Neural networks; Schedules; Software effort estimation; Software engineering; Software engineering; Software engineering; Software measurement
Citation
IEEE Access, v.10, pp.120412 - 120425
Journal Title
IEEE Access
Volume
10
Start Page
120412
End Page
120425
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86238
DOI
10.1109/access.2022.3216840
ISSN
2169-3536
Abstract
Context: The success or failure of any software development project significantly depends on the accuracy of its effort estimates. Software development effort estimation is the foundation for project bidding, budgeting, planning, and cost control. Problem: The literature shows that a lot of work has been done on software effort estimation. But still, there is a need for improvement in effort estimation by introducing new methodologies. The structured group-based and analogy-based effort estimations are the widely used estimation methods. Nevertheless, there are several shortcomings of using these methods such as lack of experts, lack of historical data, and biasness in expert opinion, which negatively affect the estimation results. Motivation: With the advancement of technologies, such limitations could be overcome. Such as leveraging the applicability of blockchain in several domains such as improvement in software development process and network security. Method: In this article, we have proposed a Blockchain-Based Software Effort Estimation (BBSEE) methodology to improve the software effort estimation. We employ the proposed method using Web and blockchain technologies. Moreover, we also proposed an evaluation criteria to assess the efficacy of the proposed method in terms of Mean Magnitude of Relative Error (MMRE), Mean Absolute Error (MAE), and percentage of successful predictions falling (PRED (25)). Result: We performed several case studies and analyses expert opinion of 52 organizations to present the efficacy of the proposed method. Conclusion: We observe that BBSEE method outperforms than expert judgment and analogy-based effort estimation methodologies in terms of software effort estimation. Author
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