Identifying And Managing Software Project Risks With Proposed Fuzzy Regression Analysis Techniques : Maintenance Phase

Many software projects have a very high failure rate in spite of much effort has been put for its succession. Risk is not always avoidable, but it is controllable on software development projects. The aim of this paper is to present a new mining technique that introduced the fuzzy multiple regression analysis to manage the risks in a software development project in the maintenance phase. The results show that all risks in software projects were significant in software project manager's perspective. This paper, we used fuzzy multiple regression analysis techniques to compare the risk management techniques to each of the software risk factors to determine if they are effective in mitigating the occurrence of each software risk factor in maintenance phase. The study has been conducted on a group of software project managers, which the top ten software risk factors in the maintenance phase and thirty risk management techniques were presented to the respondents. As the result, this output gives greatly improve of probability of software project success.

[1]  Abdelrafe Elzamly,et al.  AN ENHANCEMENT OF FRAMEWORK SOFTWARE RISK MANAGEMENT METHODOLOGY FOR SUCCESSFUL SOFTWARE DEVELOPMENT , 2014 .

[2]  Yen-Liang Chen,et al.  Mining fuzzy association rules from questionnaire data , 2009, Knowl. Based Syst..

[3]  Abdelrafe Elzamly,et al.  Managing Software Project Risks (Implementation Phase) with Proposed Stepwise Regression Analysis Techniques , 2013 .

[4]  Ciprian Popescu,et al.  A MODEL OF MULTIPLE LINEAR REGRESSION , 2007 .

[5]  Fei Peng,et al.  Analyzing project risks within a cultural and organizational setting , 2009, 2009 ICSE Workshop on Leadership and Management in Software Architecture.

[6]  Burairah Hussin,et al.  Estimating Quality-Affecting Risks in Software Projects , 2011 .

[7]  Burairah Hussin,et al.  Managing Software Project Risks with Proposed Regression Model Techniques and Effect Size Technique , 2011 .

[8]  Jyrki Kontio,et al.  Risk management in software development: a technology overview and the riskit method , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[9]  C. Ravindranath Pandian,et al.  Applied Software Risk Management: A Guide for Software Project Managers , 2006 .

[10]  Kalle Lyytinen,et al.  Components of Software Development Risk: How to Address Them? A Project Manager Survey , 2000, IEEE Trans. Software Eng..

[11]  Paul L. Bannerman,et al.  Managing Structure-Related Software Project Risk: A New Role for Project Governance , 2010, 2010 21st Australian Software Engineering Conference.

[12]  Sameem Abdul Kareem,et al.  Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models , 2012 .

[13]  Venus Marza,et al.  Fuzzy Multiple Regression Model for Estimating Software Development Time , 2009 .

[14]  Saleem Abuleil,et al.  Managing Software Project Risks with the Chi-Square ( ) Technique , 2008 .

[15]  Jin-Guan Lin,et al.  Fuzzy Statistical Analysis of Multiple Regression with Crisp and Fuzzy Covariates and Applications in Analyzing Economic Data of China , 2012 .

[16]  Abdelrafe Elzamly,et al.  Managing Software Project Risks (PlanningPhase) with Proposed Fuzzy RegressionAnalysis Techniques with Fuzzy Concepts , 2014 .

[17]  Abdelrafe Elzamly,et al.  Managing Software Project Risks (Design Phase) with Proposed Fuzzy Regression Analysis Techniques with Fuzzy Concepts , 2013 .

[18]  Xunmei Gu,et al.  Design of a Fuzzy Decision-Making Model and Its Application to Software Functional Size Measurement , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).