Identifying And Evaluation Of Risks In Software Projects in Indian IT Industries

Bhoola, Vanita; Hiremath, Shivanand B; Mallik, D. M. Arvind

doi:10.17722/ijrbt.v4i1.207

Cited by 3 publications

(2 citation statements)

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“…Previous studies had shown that risk mitigation in software projects classified into three categories-namely, qualitative, quantitative, and intelligent approaches. Firstly, quantitative risk is based on statistical methods that deal with accurate measurement about risk or leading to a quantitative inputs, that helped to form a regression model to understand how software project risk factors influence project success [72]. Furthermore, qualitative risk techniques lead to subjective opinions expressed or self-judgment by software manager using techniques namely scenario analysis, Delphi analysis, brainstorming session, and other subjective approach to mitigate risks.…”

Section: Intelligent Risk Modelsmentioning

confidence: 99%

Quantitative and Intelligent Risk Models in Risk Management for Constructing Software Development Projects: A Review

Elzamly¹,

Hussin²

2016

IJSEIA

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Techniques and models for mitigating risk in software development projects classified into three categories-namely, qualitative, quantitative, and intelligent approaches. This paper is to review the quantitative and intelligent risk models in software risk management for software development projects. Indeed, this area needs more effort from scholars and researchers in quantitative and intelligent risk models to mitigate risks. As future work, we will use these hybrid models of quantitative and intelligent for mitigating software risks in cloud computing such as neural network, genetic algorithm and others artificial intelligence techniques.

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Section: Intelligent Risk Modelsmentioning

confidence: 99%

Quantitative and Intelligent Risk Models in Risk Management for Constructing Software Development Projects: A Review

Elzamly¹,

Hussin²

2016

IJSEIA

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“…Previous studies had shown that risk mitigation in software project can be classified by 3 categories such as qualitative, quantitative and mining approaches. Quantitative risk is based on statistical methods that deal with accurate measurement about risk or leading to quantitative inputs, that helped forming a regression model to understand how software project risk factors influence project success (Bhoola et al, 2014) such as network analysis and regression models and other objective approach, but qualitative risk techniques lead to subjective opinions expressed or self judgment by software manager (Bhoola et al, 2014) such as scenario, Delphi analysis, brainstorming session and other subjective approach to mitigate risks. Mining approach is a new way of identifying risk from data that create relationships between data and find the optimum result from them.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparison of Stepwise and Fuzzy Multiple Regression Analysis Techniques for Managing Software Project Risks: Analysis Phase

Elzamly

Hussin

2014

Journal of Computer Science

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Risk is not always avoidable, but it is controllable. The aim of this study is to identify whether those techniques are effective in reducing software failure. This motivates the authors to continue the effort to enrich the managing software project risks with consider mining and quantitative approach with large data set. In this study, two new techniques are introduced namely stepwise multiple regression analysis and fuzzy multiple regression to manage the software risks. Two evaluation procedures such as MMRE and Pred (25) is used to compare the accuracy of techniques. The model's accuracy slightly improves in stepwise multiple regression rather than fuzzy multiple regression. This study will guide software managers to apply software risk management practices with real world software development organizations and verify the effectiveness of the new techniques and approaches on a software project. The study has been conducted on a group of software project using survey questionnaire. It is hope that this will enable software managers improve their decision to increase the probability of software project success.

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Impact of Project Success Factors in Managing Software Projects in India: An Empirical Analysis

Bhoola

2015

Business Perspectives and Research

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Growing at a compounded annual growth rate (CAGR) of over 15 percent during the last decade, the contribution of Information Technology (IT) sector to India's GDP is significant. In the continuously changing landscape of IT industry, effective project management is hence crucial. Success in software projects, in this article, is captured using both internal and external sources, broadly classified under four groups, namely, Project specific, Human Resources, Environmental and Organizational Culture, abbreviated as the PHRENTO. To identify and assess the effectiveness of these four categories on overall project success, a survey was conducted on 302 project managers who deal with IT projects in India. Data collected was analyzed using Stepwise Regression and Hierarchical Multiple Regression models. In contrast with similar international studies, the findings suggested that with better project planning, well-followed project processes, timely delivery of project as per customers' expectations, good leadership and team handling, flexible HR policies, state-of-the-art technology, attractive reward structures, and professional work environment, overall project success is achieved.

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Identifying And Evaluation Of Risks In Software Projects in Indian IT Industries

Cited by 3 publications

References 0 publications

Quantitative and Intelligent Risk Models in Risk Management for Constructing Software Development Projects: A Review

Quantitative and Intelligent Risk Models in Risk Management for Constructing Software Development Projects: A Review

A Comparison of Stepwise and Fuzzy Multiple Regression Analysis Techniques for Managing Software Project Risks: Analysis Phase

Impact of Project Success Factors in Managing Software Projects in India: An Empirical Analysis

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