Managing Software Project Risks (PlanningPhase) with Proposed Fuzzy RegressionAnalysis Techniques with Fuzzy Concepts

Regardless of how much effort we put for the success of software projects, many software projects have very high failure rate. Risk is not always avoidable, but it is controllable on software development projects. The aim of this paper is to present new mining technique that uses the fuzzy regression analysis modelling techniques to manage the risks in a software development project and to reduce risk with software process improvement. Top ten software risk factors in planning phase and thirty risk management techniques were presented to respondents. The results showed that all risks in software projects were important in software project manager perspective, whereas all risk management techniques are used most of time, and often. However, these mining tests were performed using 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. Also ten top software risk factors ( planning phase) were mitigated by using risk management techniques. The risk management techniques were mitigated on software risk factors in Table 15. The study has been conducted on a group of software project managers. Successful software project risk management will greatly improve the probability of project success.

[1]  Rui-ge Li,et al.  The correlation influence factors analysis in the initial dosage of insulin therapy of diabetes , 2011, 2011 Second International Conference on Mechanic Automation and Control Engineering.

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

[3]  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).

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

[5]  Kathy Schwalbe,et al.  Information Technology Project Management , 1999 .

[6]  Cornelio Yáñez-Márquez,et al.  Software development effort estimation using fuzzy logic: a case study , 2005, Sixth Mexican International Conference on Computer Science (ENC'05).

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

[8]  Jakub Miler,et al.  Supporting Team Risk Management in Software Procurement and Development Projects , 2002 .

[9]  Sodhi Jag.  IT Project Management Handbook , 2001 .

[10]  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 .

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

[12]  Mark Keil,et al.  Software project risks and their effect on outcomes , 2004, CACM.

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

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

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

[16]  James Taylor,et al.  Managing information technology projects: applying project management strategies to software, hardware, and integration initiatives , 2003 .

[17]  C. Ravindranath Pandian.  Applied Software Risk Management , 2006 .

[18]  James R. Taylor,et al.  Managing Information Technology Projects , 2003 .

[19]  S.P. Masticola,et al.  A Simple Estimate of the Cost of Software Project Failures and the Breakeven Effectiveness of Project Risk Management , 2007, 2007 First International Workshop on the Economics of Software and Computation.