Collaborative Software Engineering Model Dependent on Deep Recursive Least Squares
Authors: 
Jiao Chen, Zhi Gong Academic Editor: Hye-jin KimAuthors Info & Claims
Abstract
With the continuous development and maturity of information technology, the open methods and development results of software are enriched constantly. There are uncertain factors that may affect the cost of software development. The control of demand, the determination of the logical framework, and the vagueness and inaccuracy of business logic can all affect the development cycle of software. In this paper, the bit error rate and complexity are calculated by using the related algorithm based on the deep recursive least squares algorithm through the establishment of a collaborative engineering tool evaluation system. Under the same preconditions, the estimation performance of the deep recursive least squares algorithm is compared with that of the ideal channel based on the same foundation, and the deep recursive least squares algorithm is simulated through the simulation curve. In addition, the performance of the algorithm is analyzed. The evaluation criteria of four indexes for perception, synchronization, product, and coordination were put forward. The results of the study indicate that the proposed algorithm is effective and can support the engineering modeling of collaborative software.
References
[1]
A. Fichtner, D. P. Vanherwaarden, M. Afanasiev, and S. Simute, “The collaborative seismic earth model: generation 1,” Geophysical Research Letters, vol. 3, no. 2, pp. 1–9, 2018.
[2]
L. Liang, Y. Wei, C. Li, J. Tang, and X. Cao, “Inference with collaborative model for interactive tumor segmentation in medical image sequences,” IEEE Transactions on Cybernetics, vol. 46, no. 12, pp. 2796–2809, 2017.
[3]
J. Kong, Y. Ding, M. Jiang, and S. Li, “Collaborative model tracking with robust occlusion handling,” IET Image Processing, vol. 14, no. 9, pp. 1701–1709, 2020.
[4]
D. A. Quimby, J. E. Kawatu, K. M. Saul, and L. A. Schamus, “Implementation of a learning collaborative model increases Chlamydia screening at 37 family planning clinics: lessons learned from 3 cohorts,” Sexually Transmitted Diseases, vol. 48, no. 2, pp. 102–109, 2021.
[5]
R. Li, Z. Jiang, C. Ji, and A. Li, S. Yu, “An improved risk-benefit collaborative grey target decision model and its application in the decision making of load adjustment schemes,” Energy, vol. 156, no. 4, pp. 387–400, 2018.
[6]
D. Riehle, M. Capraro, D. Kips, and L. Horn, “Inner source in platform-based product engineering,” IEEE Transactions on Software Engineering, vol. 4, no. 3, pp. 1162–1177, 2016.
[7]
V. A. Merchan, E. Esche, S. Fillinger, and G. Tolksdorf, “Computer‐aided process and plant development. A review of common software tools and methods and comparison against an integrated collaborative approach,” Chemie Ingenieur Technik, vol. 88, no. 2, pp. 50–69, 2016.
[8]
C. L. Craney, T. Lau, W. T. Nelson, and A. Ghomeshi, “Collaborative middle school science outreach project using the Japanese lesson study model,” Journal of Chemical Education, vol. 97, no. 5, pp. 89–96, 2020.
[9]
P. T. Nguyen-Ha, D. Howrie, K. Crowley, and C. G. Vetterly, “A quality assessment of a collaborative model of a pediatric antimicrobial stewardship program,” Pediatrics, vol. 137, no. 5, pp. 316–321, 2016.
[10]
W. Mu, F. Bénaben, and H. Pingaud, “Collaborative process cartography deduction based on collaborative ontology and model transformation,” Information ences, vol. 334, pp. 83–102, 2016.
[11]
M. Henry, B. Jan, and V. D. H. Andre, “Collaborative modeling in software engineering,” IEEE Software, vol. 35, no. 6, pp. 20–24, 2018.
[12]
P. Nicolaescu, M. Rosenstengel, M. Derntl, R. Klamma, and M. Jarke, “Near real-time collaborative modeling for view-based Web information systems engineering,” Information Systems, vol. 74, no. 5, pp. 23–39, 2018.
[13]
V. Basili, L. Briand, D. Bianculli, S. Nejati, F. Pastore, and M. Sabetzadeh, “Software engineering research and industry: a symbiotic relationship to foster impact,” IEEE Software, vol. 4, no. 4, pp. 1–10, 2018.
[14]
H. Sharp, Y. Dittrich, and C. Souza, “The role of ethnographic studies in empirical software engineering,” IEEE Transactions on Software Engineering, vol. 42, no. 8, pp. 786–804, 2016.
[15]
X. Wang, X. Li, S. Pack, Z. Han, and V. C. M. Leung, “STCS: spatial-temporal collaborative sampling in flow-aware software defined networks,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 99, pp. 999–1013, 2020.
Recommendations
Least Squares Sign-Solvability
Let $Ax=b$ be a linear system. We study the relationship between the sign pattern of the least squares solution to $Ax=b$ and the sign patterns of $A$ and $b$. The system $Ax=b$ is least squares sign-solvable if the signs of the entries in its ...
Least squares parameter estimation and multi-innovation least squares methods for linear fitting problems from noisy data
AbstractLeast squares is an important method for solving linear fitting problems and quadratic optimization problems. This paper explores the properties of the least squares methods and the multi-innovation least squares methods. It ...
Least absolute value and chebychev estimation utilizing least squares results
In exploratory data analysis and curve fitting in particular, it is often desirable to observe residual values obtained with different estimation criteria. The goal with most linear model curve-fitting procedures is to minimize, in some sense, the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2022 Jiao Chen and Zhi Gong.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
IOS Press
Netherlands
Publication History
Published: 01 January 2022
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 11 Jan 2025