Defining a Standard Classification in Activity Model Confirmation, Approval and Adjustment

Authors

  • Prasanna Kumar M., Kiran P., Bhavani Shankar K., Dhanraj

Keywords:

Standard classification , activity model , adjustment & software development.

Abstract

Defining a standard classification in activity model confirmation, approval, and adjustment for software development is crucial to navigating the complexities of the software development lifecycle effectively. This classification framework provides a structured approach to managing various activities, ensuring consistency, transparency, and quality throughout the process. The framework addresses the challenges posed by diverse stakeholders, the evolving nature of technology, and the need for efficient resource allocation. It balances structured processes with the flexibility to adapt to changing requirements, promoting collaboration and communication among teams. By establishing clear stages of confirmation, approval, and adjustment, the framework enhances decision-making, risk management, and project visibility. It facilitates efficient resource allocation, reduces bottlenecks, and fosters a culture of continuous improvement. In conclusion, the standard classification framework empowers organizations to streamline software development, optimize resource utilization, and adapt to industry shifts. It serves as a guiding beacon, ensuring that each activity progresses through well-defined stages, leading to successful software outcomes in an ever-changing landscape.

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References

M. R. Wigan and R. Clarke, "Big Data's Big Unintended Consequences," in Computer, vol. 46, no. 6, pp. 46-53, June 2013. doi: 10.1109/MC.2013.195

keywords: {Information management; Data handling; Data storage systems; Government policies; Databases; Business; Legal aspects; Data privacy; policy; privacy; data; social impact; big data; private data commons},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=6527234

Broggi et al., "PROUD—Public Road Urban Driverless-Car Test," in IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 6, pp. 3508-3519, Dec. 2015.

doi: 10.1109/TITS.2015.2477556

keywords: {Autonomous automobiles; Intelligent systems; Image processing; Data integration; Systems architecture; Urban areas; Autonomous vehicles; intelligent systems; image processing; data fusion; system architecture; Autonomous vehicles; intelligent systems; image processing; data fusion; system architecture},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=7330243

L. Li, W. -L. Huang, Y. Liu, N. -N. Zheng and F. -Y. Wang, "Intelligence Testing for Autonomous Vehicles: A New Approach," in IEEE Transactions on Intelligent Vehicles, vol. 1, no. 2, pp. 158-166, June 2016.

doi: 10.1109/TIV.2016.2608003

keywords: {Autonomous automobiles; Vehicles; Testing; Intelligent vehicles; Semantics; Roads; Prototypes; Autonomous vehicles; intelligence testing},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=7769266

L. Li and D. Wen, "Parallel Systems for Traffic Control: A Rethinking," in IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 4, pp. 1179-1182, April 2016.

doi: 10.1109/TITS.2015.2494625

keywords: {Transportation; Optimal control; Uncertainty; Computational modeling; Traffic control; Predictive control; Traffic control; parallel systems; parallel traffic control; Traffic control; parallel systems; parallel traffic control},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=7442200

L. Li, Y. Lin, N. Zheng and F. -Y. Wang, "Parallel learning: a perspective and a framework," in IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 3, pp. 389-395, 2017.

doi: 10.1109/JAS.2017.7510493

keywords: {Learning systems; Complex systems; Control systems; Aerospace electronics; Games; Automation; Data models},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=7974885

W. B. Langdon, S. Yoo and M. Harman, "Inferring Automatic Test Oracles," 2017 IEEE/ACM 10th International Workshop on Search-Based Software Testing (SBST), Buenos Aires, Argentina, 2017, pp. 5-6.

doi: 10.1109/SBST.2017.1

keywords: {Software; Artificial intelligence; Software engineering; Software testing; Programming; Neural networks; SBSE; Multiplicity computing; deep testing; Search Based Automatic Oracles},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=7967900

Podgurski et al., "Automated support for classifying software failure reports," 25th International Conference on Software Engineering, 2003. Proceedings., Portland, OR, USA, 2003, pp. 465-475.

doi: 10.1109/ICSE.2003.1201224

keywords: {Computer crashes; Frequency estimation; Terminology; Visualization; Humans; Estimation error; Instruments},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=27042

P. Francis, D. Leon, M. Minch and A. Podgurski, "Tree-based methods for classifying software failures," 15th International Symposium on Software Reliability Engineering, Saint-Malo, France, 2004, pp. 451-462.

doi: 10.1109/ISSRE.2004.43

keywords: {Classification tree analysis; Clustering algorithms; Pattern classification; Iterative algorithms; Computer science; Software testing; Data analysis; Data mining; Failure analysis; Information analysis},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=30138

[9]. T. Y. Chen, Jianqiang Feng and T. H. Tse, "Metamorphic testing of programs on partial differential equations: a case study," Proceedings 26th Annual International Computer Software and Applications, Oxford, UK, 2002, pp. 327-333.

doi: 10.1109/CMPSAC.2002.1045022

keywords: {Partial differential equations; Computer aided software engineering; Application software; Software libraries; Boundary conditions; Software testing; Software standards; Biomedical engineering; Mission critical systems; Packaging},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=22390

J. Mayer and R. Guderlei, "On Random Testing of Image Processing Applications," 2006 Sixth International Conference on Quality Software (QSIC'06), Beijing, China, 2006, pp. 85-92.

doi: 10.1109/QSIC.2006.45

keywords: {Image processing; Automatic testing; Pixel; Image analysis; Digital images; Gray-scale; Euclidean distance; Genetic mutations; Software testing; Software quality; Metamorphic Testing; Random Testing; test data selection; test evaluation; testing oracle},

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=

&isnumber=4032251

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Published

01.07.2024

How to Cite

Prasanna Kumar M. (2024). Defining a Standard Classification in Activity Model Confirmation, Approval and Adjustment. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 4591 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6343

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Section

Research Article