Published 12 Papers in Famous Journals & Conferences across the Globe
Can be found on Semantic Scholar
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Publications 12
h-index 3
Citations 25
Papers Description
Paper 1:
An Application of Six Sigma and Simulation in Software Testing Risk Assessment
Third International Conference on Software…
6 April 2010
TLDR
This paper presents an application of Six Sigma and Simulation in Software Testing that is compliant with CMMI® and provides for substantial Software Testing performance-driven improvements.
Abstract
The conventional approach to Risk Assessment in Software Testing is based on analytic models and statistical analysis. The analytic models are static, so they don’t account for the inherent variability and uncertainty of the testing process, which is an apparent deficiency. This paper presents an application of Six Sigma and Simulation in Software Testing. DMAIC and simulation are applied to a testing process to assess and mitigate the risk to deliver the product on time, achieving the quality goals. DMAIC is used to improve the process and achieve the required (higher) capability. Simulation is used to predict the quality (reliability) and considers the uncertainty and variability, which, in comparison with the analytic models, more accurately models the testing process. Presented experiments are applied to a real project using published data. The results are satisfactorily verified. This enhanced approach is compliant with CMMI® and provides for substantial Software Testing performance-driven improvements.
View on IEEE (opens in a new tab)
Paper 2:
A novel approach to software quality risk management
Software testing, verification & reliability
1 March 2014
Copyright © 2013 John Wiley & Sons, Ltd.
TLDR
This new practical method uses Six Sigma and Monte Carlo Simulation for ongoing quality risk management and DMAIC (Define, Measure, Analyse, Improve, Control) is systematically applied as a tactical framework to enhance the process and improve quality.
Abstract
Software quality is very important in today’s competitive business environment. It is a critical constraint on software projects. Software organizations’ major objectives are delivering products on time and achieving quality goals. Quality is directly dependent on software processes, which are inherently variable and uncertain, involving substantial risk. Managing quality risk is an important challenge. The conventional approach to quality risk management for ongoing software processes has two major deficiencies: static analytic models are used, and structured methodologies to enhance processes and improve quality are not systematically applied. This new practical method uses Six Sigma and Monte Carlo Simulation for ongoing quality risk management. DMAIC (Define, Measure, Analyse, Improve, Control) is systematically applied as a tactical framework to enhance the process and improve quality. The simulation predicts quality (reliability) at the expected process end and identifies and quantifies risk. DMAIC is a verified structured methodology for systematic process and quality improvements. Monte Carlo Simulation is superior to conventional risk models. These synergetic enhancements eliminate observed deficiencies. The method has been successfully proven and applied practically on real in‐house projects. Substantial savings, quality, and customer satisfaction have been achieved. An application on an internal project and obtained results are presented. The method is simplistically elaborated on a published third‐party project answering key research questions from practical perspectives. This CMMI® compliant method offers important benefits including savings, quality, and customer satisfaction.
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Paper 3:
A simulation approach to six sigma in software development
SOURCE-WORK-ID: https://www.semanticscholar.org/paper/A-simulation-approach-to-six-sigma-in-software-Bubevski/bb680d520b0118179dd812a196cda48295f2f51b
3 July 2009
Abstract
Six Sigma is recognized across industries as a standard methodology to systematically improve processes and increase customer satisfaction. In today’s competitive business environment, software quality and customer satisfaction are more important than ever. The traditional approach to Six Sigma Software Development is based on analytic models. The analytic models are static, so they do not account for the inherent variability and uncertainty of the software development process, which is an apparent deficiency. In contrast, this paper presents a Six Sigma approach to Software Development by applying simulation. The DMAIC methodology is applied to an ongoing software development sub-process (i. e. test) in order to achieve the required (higher) process capability to deliver the project on time, fully meeting the quality requirements. Simulation is used to provide for traditional Six Sigma analysis as well as for considering the uncertain and dynamic factors, which, in comparison with the analytic models, more accurately model the software development process. The DMAIC and simulation experiments are applied on a real IBM™ project, using published data. Applying simulation to the DMAIC methodology is a significant enhancement. This approach is compliant with CMMI® and provides a strong foundation for performance-driven improvements. The paper demonstrates the practical aspect of the approach.
Paper 4:
Six Sigma Improvements for Basel III and Solvency II in Financial Risk Management
Advances in Logistics, Operations, and Management…
5 October 2018
Paper 5:
A Stochastic Approach to Risk Modelling for Solvency II
5 September 2011
Abstract
Solvency II establishes EU-wide capital requirements and risk management standards for (re)insurers. The capital requirements are defined by the Solvency Capital Requirement (SCR), which should deliver a level of capital that enables the (re)insurer to absorb significant unforeseen losses over a specified time horizon. It should cover insurance, market, credit, and operational risks, corresponding to the Value-at-Risk (VAR) subject to a confidence level of 99.95% over one year. Standard models are deterministic, scenario-based, or covariance-based, i.e. non-stochastic. They don’t optimise the investment portfolios. These are two major deficiencies. A stochastic approach is proposed, which combines Monte Carlo Simulation and Optimisation. This method determines minimal variance portfolios and calculates VAR/SCR using the optimal portfolios’ simulation distributions, which ultimately eliminates the standard models’ deficiencies. It offers (re)insurers internal model options, which can help them to reduce their VAR/SCR providing higher underwriting capabilities and increasing their competitive position, which is their ultimate objective.
Paper 6:
A new practical approach to asset liability management for BASEL III and SOLVENCY II
Abstract
A new practical approach to Asset Liability Management (ALM) is proposed, which combines Monte Carlo Simulation, Optimisation, and Six Sigma Define, Measure, Analyse, Improve, and Control (DMAIC) methodology. This new method determines an optimally diversified minimal variance investment portfolio, which gains a desired range of return with minimal financial risk. Simulation and optimisation are conventionally applied to find the optimal portfolio to provide the required return. In addition, the Six Sigma DMAIC methodology is used to measure and improve the portfolio management process in order to establish an optimally diversified portfolio. Applying Six Sigma DMAIC to the portfolio management process is an improvement in comparison with conventional stochastic ALM risk models. It offers financial institutions internal model options for Basel III and Solvency II, which can help them to reduce their capital requirements and Value-at-Risk (VAR) providing for higher business capabilities and increasing their competitive position, which is their ultimate objective.
Paper 7:
Novel Six Sigma Approaches to Risk Assessment and Management
12 July 2017
Abstract
Novel Six Sigma Approaches to Risk Assessment and Management is a vital scholarly resource that provides an in-depth examination of innovative Six Sigma methods for risk mitigation initiatives. Featuring an array of relevant topics such as project management, production scheduling, information systems security, and agricultural planning, this is an ideal reference book for professionals, academicians, students, and researchers interested in detailed research on recent advancements in the management of risk in all fields.
Paper 8:
A Six Sigma Approach to Internal ALM Models for Solvency II
28 May 2016
TLDR
A Six Sigma method is proposed to improve the investment management process by merging DMAIC into portfolio management and is applicable to internal models for Solvency II in order to reduce the capital requirements and Value at Risk.
Abstract
A Six Sigma method is proposed to improve the investment management process. In addition to conventional stochastic optimisation, simulation, and risk management, as a new concept, Six Sigma DMAIC (Define, Measure, Analyse, Improve, and Control) is applied by merging DMAIC into portfolio management. The method is applicable to internal models for Solvency II in order to reduce the capital requirements and Value at Risk. By using it, financial institutions can achieve higher business capabilities and increase their competitive position, which is their ultimate objective.
Keywords: Investment Management; Portfolio Analysis; Asset & Liability Management; Solvency II; Six Sigma DMAIC; Stochastic Optimisation; Monte Carlo Simulation.
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Paper 9:
Generic Control Phase for Project Risk Management
Source Title: Novel Six Sigma Approaches to Risk Assessment and Management
Author: Vojo Bubevski
Copyright: © 2018 |Pages: 7
ISBN13: 9781522527039|ISBN10: 1522527036|EISBN13: 9781522527046
DOI: 10.4018/978-1-5225-2703-9.ch010
2018
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Abstract
This chapter elaborates on a generic tactic for the DMAIC Control phase for Project Control. This is required because the projects presented in the book are not implemented, so there are no data available for analysis. Also, the generic Project Control phase reuses the associated scenario elaborated in Ch. 6. Moreover, the Project Control phase is applicable to the applications of the method in Chapter 3, Chapter 4, Chapter 6, and Chapter 7, which is appropriately referenced in the respective individual chapters. Finally, elaborating on the Project Control phase in a dedicated chapter avoids repetitions in the book.
Paper 10:
Solvency II Internal Models-An Alternative
Published 2016
Corpus ID: 156831655
Paper 11:
A stochastic approach to security software quality management
25 September 2013
Paper 12:
A Six Sigma Security Software Quality Management
18 October 2016
TLDR
It is concluded that utilising Monte Carlo Simulations in a Six Sigma DMAIC structured framework is better than conventional approaches using static analysis methods to improve software quality and achieve the zero-defects quality assurance goal while assigning quality confidence levels to scheduled product releases.
Abstract
Today, the demand for security software is Six Sigma quality, i.e. practically zero-defects. A practical and stochastic method is proposed for Six Sigma security software quality management. Monte Carlo Simulation is used in a Six Sigma DMAIC (Define, Measure, Analyse, Improve, Control) approach to security software testing. This elaboration used a published real project’s data from the final product testing that lasted for 15 weeks, after which the product was delivered. The experiment utilised the first 12 weeks’ data to allow the results verification on the actual data from the last three weeks. A hypothetical testing project was applied, supposed to be completed in 15 weeks. The product due date was Week 16 with a zero-defects quality assurance aim. The testing project was analysed at the end of the 12th week with three weeks of testing remaining. Running a Monte Carlo Simulation with data from the first 12 weeks produced results that indicated that the product would not be able to meet its due date with the desired zero-defects quality. To quantify an improvement, another simulation was run to find when zero defects would be achieved. The simulation predicted that zero defects would be achieved in week 35 with 56% probability, and there would be 82 defects from Weeks 16 – 35. Therefore, to meet the quality goals, either more resources should be allocated to the project, or the deadline for the project should be moved to Week 36. The paper concluded that utilising Monte Carlo Simulations in a Six Sigma DMAIC structured framework is better than conventional approaches using static analysis methods. When the simulation results were compared to the actual data, it was found to be accurate within ﹣3.5% to +1.3%. This approach helps to improve software quality and achieve the zero-defects quality assurance goal while assigning quality confidence levels to scheduled product releases.