Managing Software Project Risks (Design Phase) with Proposed Fuzzy Regression Analysis Techniques With Fuzzy Concepts - Abdelrafe , Elzamly; Burairah, Hussin (2013)
Abstract - This Regardless how much effort we put for the success of sofnvare projects, many sofnvare projects have very high failure rate. Risk is not always avoidable, but it is controllable. The aim of this paper is to present the new mining technique that uses the fuzzy multiple regression analysis techniques with fuzzy concepts to managing the risks in a software project and reducing risk with sofnvare process improvement. Top ten sofnvare risk factors in design phase and thirty risk management techniques were presented to respondents. Tire results show that alf risks in sofnvare projects were important in sofnvare project manager perspective. whereas all risk management techniques are used most of time. and often. However, these mining tests were perfonned using fuzzy multiple regression analysis techniques to compare tire risk management techniques to each of the software risk factors to determine if they are effective in mitigating the occurrence of each sofnvare risk factor by usjng statistical package for the Social Science (SPSS) for Manipulating and analyzing tire data set, MAT LAB 7.12.0 (R20 11 a), wolfram mathematic 9. 0,. Also ten top software risk factors were mitigated by using risk management techniques except Risk 3 "Developing the Wrong User Interface". We referred the risk management techniques were mitigated on sofnvare risk factors in Table XV. The study has been conducted on a group of software project managers. Successful project risk management will greatly improve the