Blockchain-Based Software Effort Estimation: An Empirical Study

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.

Software Cost and Effort Estimation: Current Approaches and Future Trends

“…While widely used approaches have been examined, there is still a limitation to this SLR that emerging technologies like blockchain and bio-inspired feature selection algorithms have not been examined in depth. In future work, we intend to perform an SLR on blockchain-based software effort estimation methods and bio-inspired feature selection algorithms that have been used recently in a few studies [29], [40] to address the software cost and effort estimation problems.…”

“…In the recent past, ML-based methods have been used more extensively in the literature [29], [30], [31]. This comprises techniques such as regression [32], [33], [34], ANN [13], [35], [36], [37], Ensemble [31], [38], [39], and Blockchain [40]. The specific focus of research in all these methods has been on the optimization of errors [41], [42], [43], [44], [45].…”

“…This is an emerging field with research potential. Ahmed et al [40] conducted an SLR using a Blockchain-Based Software Effort Estimation methodology on the expert opinions of 52 organizations and the results of the case study conclude that a lack of historical data, experts, and biases in a group prevents the organizations to perform estimation activities more effectively. It also suggests that the blockchain method estimation is more efficient as compared to traditional methods.…”

Particle Swarm Optimization-based Linear Regression for Software Effort Estimation

“…The Evaluation section uses calculations such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) for each model (Ahmed et al, 2022;Shukla & Kumar, 2021). MAE is the average of the absolute values of the error between the prediction and the actual value.…”