Authors :
Ravikanth Kowdeed
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/yjuhsu4t
Scribd :
https://tinyurl.com/32mhnypu
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR2132
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The Organizations have been investing more
in Technology and Infrastructure spends like software
upgrades, software renewals, software replacements,
platform migrations etc., apart from investment in
Business, People, and Processes. In this context, it is not
an easy task for stakeholders to decide whether to go for
a software upgrade or to replace it with another
software.
There is no unified approach or solution to
consolidate data and relationships of Information
Technology Assets, Software Upgrades, Software costs,
Software defects, Software Performance Metrics,
Security issues, IT system versions, service level
objectives etc. Due to this, the decision making of
software upgrades and software decommissioning is a
tedious process and takes more time and effort.
There is a need to build a solution that can integrate
and validate the information like software assets,
software upgrade success and failure likelihoods, cost
benefit analysis of Cloud Computing, software metrics
for fault prediction, software maintainability prediction
results, Digital Transformation readiness and other
related factors.
There is an opportunity to apply Machine Learning
techniques in defining and deriving the success
likelihoods on the following data: Systems and data
integration, software assets compatibility, operational
service level agreement breaches, quality assurance
metrics, security issues, number of open defects, number
of defect fixes, number of priority incidents, mean time
to resolve critical incidents, expected cost increase in
software maintenance, potential cost reduction with the
software or hardware replacement etc.
This Research Proposal outlines the above
mentioned to build a recommendation system aka
decision tree namely Software Upgrades or
Decommissions Life Cycle.
The Organizations have been investing more
in Technology and Infrastructure spends like software
upgrades, software renewals, software replacements,
platform migrations etc., apart from investment in
Business, People, and Processes. In this context, it is not
an easy task for stakeholders to decide whether to go for
a software upgrade or to replace it with another
software.
There is no unified approach or solution to
consolidate data and relationships of Information
Technology Assets, Software Upgrades, Software costs,
Software defects, Software Performance Metrics,
Security issues, IT system versions, service level
objectives etc. Due to this, the decision making of
software upgrades and software decommissioning is a
tedious process and takes more time and effort.
There is a need to build a solution that can integrate
and validate the information like software assets,
software upgrade success and failure likelihoods, cost
benefit analysis of Cloud Computing, software metrics
for fault prediction, software maintainability prediction
results, Digital Transformation readiness and other
related factors.
There is an opportunity to apply Machine Learning
techniques in defining and deriving the success
likelihoods on the following data: Systems and data
integration, software assets compatibility, operational
service level agreement breaches, quality assurance
metrics, security issues, number of open defects, number
of defect fixes, number of priority incidents, mean time
to resolve critical incidents, expected cost increase in
software maintenance, potential cost reduction with the
software or hardware replacement etc.
This Research Proposal outlines the above
mentioned to build a recommendation system aka
decision tree namely Software Upgrades or
Decommissions Life Cycle.
