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PORM: Predictive Optimization of Risk Management to control Uncertainty Problems in Software Engineering

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 8 No. 6 (2018)

DOI
10.11591/ijece.v8i6.pp4735-4744
ISSN
2088-8708, 2088-8708
Published
2018-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Irrespective of different research-based approaches toward risk management, developing a precise model towards risk management is found to be a computationally challenging task owing to critical and vague definition of the origination of the problems. This research work introduces a model called as PROM i.e. Predictive Optimization of Risk Management with the perspective of software engineering. The significant contribution of PORM is to offer a reliable computation of risk analysis by considering generalized practical scenario of software development practices in Information Technology (IT) industry. The proposed PORM system is also designed and equipped with better risk factor assessment with an aid of machine learning approach without having more involvement of iteration. The study outcome shows that PORM system offers computationally cost effective analysis of risk factor as assessed with respect to different quality standards of object oriented system involved in every software projects.

Collaboration network of the authors

4 authors · 4 co-authorship links · 1 across different organisations

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  • Bharathiar University
  • University of Sri Jayewardenepura
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Surendran Swapna KumarAffiliation unknown1
R.G.N. MeegamaUniversity of Sri Jayewardenepura1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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