Subburaj Ramasamy is a researcher, author, educator, and software engineering expert with extensive experience in Software Reliability Engineering, Artificial Intelligence, Machine Learning, Secure Coding, Software Testing, Quality Engineering, and Embedded Systems.

He has published more than 35 journal papers and book chapters in reputed international journals and publishers including Elsevier, Springer, Hindawi, Inderscience, and others. His research contributions span software reliability growth modeling, neural network applications, secure coding practices, ontology-based systems, machine learning, cloud security, and quality engineering.

Research Areas:
• Software Reliability Engineering
• Artificial Intelligence & Machine Learning
• Secure Coding & Software Security
• Software Testing & Quality Engineering

• Cloud Security & Embedded Systems

View Google Scholar Publications

https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Subburaj+Ramasamy&btnG=Dr. Subburaj Ramasamy

Research Highlights

• More than 25 research publications in reputed international journals and conferences.

• 2 book chapters published by Springer and IGI Global.

• Research contributions in:

Software Reliability Engineering
Artificial Intelligence & Machine Learning
Secure Coding & Software Security
Software Testing & Quality Engineering
Cloud Security & Embedded Systems

• Publications have appeared in journals and publishers including Elsevier, Springer, Hindawi, Inderscience, IETE and other reputed international venues.

Software Reliability Engineering

Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Subburaj Ramasamy, Indhurani Lakshmanan

2017

https://doi.org/10.1155/2017/8040346Digital Object Identifier (DOI)

Abstract: Reliability is one of the quantiiable sotware quality attributes. Sotware Reliability Growth Models (SRGMs) are used to assess

the reliability achieved at diferent times of testing. Traditional time-based SRGMs may not be accurate enough in all situations

where test efort varies with time. To overcome this lacuna, test efort was used instead of time in SRGMs. In the past, inite test

efort functions were proposed, which may not be realistic as, at ininite testing time, test efort will be ininite. Hence in this paper,

we propose an ininite test efort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We

use Artiicial Neural Network (ANN) for training the proposed model with sotware failure data. Here it is possible to get a large

set of weights for the same model to describe the past failure data equally well. We use machine learning approach to select the

appropriate set of weights for the model which will describe both the past and the future data well. We compare the performance

of the proposed model with existing model using practical sotware failure data sets. he proposed log-power TEF based SRGM

describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and

can be used for sotware release time determination as well.

Indhurani Lakshmanan, Subburaj Ramasamy: Improving software reliability estimation using multi-layer neural-network combination model. INDERSCIENCE

International Journal of Innovative Computing and Applications 01/2017;
8(2):113-121., DOI:10.1504/IJICA.2017.10005956

Subburaj Ramasamy, Gopal Govindasamy:
A software reliability growth model
addressing learning. Journal of
Applied Statistics 10/2008; 35(10):1151-1168.,
DOI:10.1080/02664760802270621

A dynamically weighted discrete combination model for all releases of a software system

October 2020
Microprocessors and Microsystems 79(2):1-6

DOI:10.1016/j.micpro.2020.103290

Subburaj Ramasamy, MuthuKumaranA.M.J:
DISCRETE DYNAMICALLY WEIGHTED COMBINATION
SOFTWARE RELIABILITY GROWTH MODEL. International Journal of Pure and
Applied Mathematics 09/2017; 115(8):109-113.

Subburaj Ramasamy, Indhurani
Lakshmanan: Application of Artificial
Neural Network for Software Reliability Growth Modeling with Testing Effort.
Indian Journal of Science and Technology 08/2016; Vol 9(29):1-7.,
DOI:10.17485/ijst/2016/v9i29/90093

Subburaj Ramasamy, C. A. S. Deiva
Preetha: Dynamically Weighted Combination
Model for Describing Inconsistent Failure Data of Software Projects. Indian
Journal of Science and Technology 09/2016; 9(35):1-4.,
DOI:10.17485/ijst/2016/v9i35/90211,

Subburaj Ramasamy, A M J Muthu
Kumaran: Dynamically Weighted Combination
of Fault -based Software Reliability Growth Models. Indian Journal of
Science and Technology 06/2016; 9(22):1-4., DOI:10.17485/ijst/2016/v9i22/93967

Subburaj Ramasamy, Indhurani Lakshmanana: An Artificial Neural-Network Approach to
Software Reliability Growth Modeling. Procedia Computer Science 07/2015; 57
( 2015 ):695 – 702., DOI:10.1016/j.procs.2015.07.450

R Subburaj, R P Wadhwa, S C Mehta, S K Pandey, N Saxena: Quality and Reliability Assurance Programme for Personal Computers. IETE Technical Review 06/2015; 3(4):125-129., DOI:10.1080/02564602.1986.11437924

R.Subburaj Professor, Gladman Jekese, Chiedza Hwata: Impact of Object Oriented Design Patterns on Software Development. International Journal of Scientific and Engineering Research 03/2015; Volume3(Issue 2):6.

R.Subburaj, Vasudha Singh: An exploration of model based testing. International Journal of Scientific and Engineering Research 02/2015;

Subburaj Ramasamy, G.Gopal, PK Kapur: A Software Reliability Growth Model for Vital Quality Metrics. South African Journal of Industrial Engineering 11/2007; 18(2):93-108., DOI:10.7166/18-2-121

Subburaj Ramasamy, Gopal Govindasamy: Generalized Exponential Poisson Model for Software Reliability Growth International Journal of Performability Engineering 07/2006; 2(3).

Subburaj Ramasamy: A practical approach for Calibration Interval. Electronics Information and Planning 01/1997.

Subburaj Ramasamy, RP Wadhwa: Quality Certification of Personal Computers and Associated Peripherals. Electronics Information and Planning 07/1990;

Subburaj Ramasamy: Parallel Computing. Electronics Information and Planning 03/1989;

Subburaj Ramasamy, RP Wadhwa: Role of Soldering Technology in Electronics Industry. Electronics Information and Planning 01/1989

Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Subburaj Ramasamy, Indhurani Lakshmanan

2017

https://doi.org/10.1155/2017/8040346

Indhurani Lakshmanan, Subburaj Ramasamy: Improving software reliability estimation
using multi-layer neural-network combination model. INDERSCIENCE
International Journal of Innovative Computing and Applications 01/2017;
8(2):113-121., DOI:10.1504/IJICA.2017.10005956

Subburaj Ramasamy, Gopal Govindasamy:
A software reliability growth model
addressing learning. Journal of
Applied Statistics 10/2008; 35(10):1151-1168.,
DOI:10.1080/02664760802270621

A dynamically weighted discrete combination model for all releases of a software system

October 2020
Microprocessors and Microsystems 79(2):1-6