Blockchain-based Software Effort Estimation: An Empirical Study
- Authors
- Ahmed, Mansoor; Iqbal, Naeem; Hussain, Faraz; Khan, Murad-Ali; Helfert, Markus; Imran,; 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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