A framework of automated project Management automation using artificial intelligence

  • nabila latif lgu
Keywords: AI – Artificial Intelligence, BPR - Business Process Reengineering, BPM – Business Process Management, SE - Software Engineering, SEM – Software Engineering Management, SPM – Software Process Management, SPI – Software Process Improvement

Abstract

This paper points an analytical study on the software development organization to understand automation technologies and their use in Software Engineering Management (SEM) processes. Software Project Management (SPM) deals with planning, controlling, executing and monitoring. SPM approaches are more centering towards the basic necessity for the achievement of software project management. It has been trying to oversee software development utilizing existing project management procedures driven by software development organization and this is one of the problem area for this study. This paper is a systematic examination for the prerequisites and consideration of BPR in SPM, investigates to spot and emphasis the significant success factors for the execution of a BPR utilizing advantages of Artificial Intelligence (AI) in software development organization. BPR is organizational technique that improves capacity to react to difficulties of qualitative outcome by change and improvement in software engineering procedures, profitability, product quality and competitive advantages. AI will be the best methodology and extent of automation SEM processes for software development organization. This paper also highlights an applied perspective on software engineering model move for upgrades in ability of project managers to deal with agile thinking and problem solving for advancement of SPM utilizing Artificial Intelligence.

References

[1] C. S. Joshi and P. Dangwal, "Management of business process reengineering projects: a case study," Journal of Project, Program & Portfolio Management, vol. 3, no. 1, pp. 78 to 89-78 to 89, 2012.
[2] L. Robert, "Glass, IT Failure Rates--70% or 10-15%," IEEE Software, vol. 22, no. 3, pp. 112-111, 2005.
[3] F. Caeldries, "Reengineering the corporation: A manifesto for business revolution," ed: JSTOR, 1994.
[4] S. Dwivedi, "Software Development Life Cycle Models-A Comparative analysis," International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 2, pp. 232-233, 2016.
[5] G. B. O. o. G. Commerce, Managing successful projects with PRINCE2. The Stationery Office, 2002.
[6] J. K. Pinto and S. J. Mantel, "The causes of project failure," IEEE transactions on engineering management, vol. 37, no. 4, pp. 269-276, 1990.
[7] J. Pinto and D. Slevin, "Critical success factors across the project life cycle," Project Control, Newtown, p. 91, 1999.
[8] O. Räihä, "A survey on search-based software design," Computer Science Review, vol. 4, no. 4, pp. 203-249, 2010.
[9] A. J. Shrnhur, O. Levy, and D. Dvir, "Mapping the dimensions of project success," Project management journal, vol. 28, no. 2, pp. 5-13, 1997.
[10] A. De Wit, "Measurement of project success," International journal of project management, vol. 6, no. 3, pp. 164-170, 1988.
Published
2020-08-03
How to Cite
[1]
nabila latif, “A framework of automated project Management automation using artificial intelligence”, IJCBS, vol. 1, no. 3, Aug. 2020.