The aim of this study is to propose the discriminant analysis (DA)

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.

The concern of this study is to identify software risks and controls in the software development lifecycle. The aim of this study is to rank the software risks factors according to their importance and occurrence frequency based on the data source. The survey questionnaire is used to collect data and method of sample selection referred to as 'snowball' and distribution personal regular sampling was used. The seventy six software project managers have participated in this study who works in the Palestinian software development. Fifty software risk factors in all phases SDLC and thirty risk management techniques were presented to respondents. The results show that all risks in software projects were significant and important in software project manager's perspective. However, the ranking of the importance of the risks is assigned according to it: Analysis, planning, maintenance, design, and implementation. In addition, the top ten software risk factors in software development are selected and used for further analysis such as:

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.

Despite much research and progress in the areas of cloud computing project, many cloud computing projects have a very high failure rate when it comes to the banking organizations. The aim of this study is to propose a new conceptual framework modelling for cloud computing risk management in banking organizations. There are the main five stages for a successful cloud computing framework in a banking organization as described in Figure 1: Cloud mobility and cloud banking applications, cloud service models, cloud deployment models, cloud risk management models, and cloud security models. As a future work, we will apply the framework in the real banking world to mitigate and control the security issues. A successful framework modelling for cloud computing risk management will greatly improve the probability of cloud computing success in banking organizations.

The aim of this study is to classify critical security issues in cloud banking organizations, according to the cloud service models (CSM)

The aim of this paper is to propose new mining techniques by which we can study the impact of different risk management techniques and different software risk factors on software analysis development projects. The new mining technique uses the fuzzy multiple regression analysis techniques with fuzzy concepts to manage the software risks in a software project and mitigating risk with software process improvement. Top ten software risk factors in analysis phase and thirty risk management techniques were presented to respondents. The results show that all software risks in software projects were very important from software project manager perspective, whereas all risk management techniques are used most of the time, and often. However, these mining tests were performed using fuzzy multiple regression analysis techniques to compare the risk management techniques with each of the software risk factors to determine if they are effective in reducing the occurrence of each software risk factor. The study has been conducted on a group of software project managers. Successful software project risk management will greatly improve the probability of software project success.

Software risk is not always avoidable, but it is controllable. The aim of this paper is to present new techniques that were performed using quantitative and mining techniques to compare the risk management techniques to each of the software maintenance risks to identify and model if they are effective in mitigating the occurrence of each software maintenance risk in software development life cycle. The model's accuracy slightly improves in fuzzy multiple regression modelling techniques than or quite equal stepwise multiple regression modelling techniques. All models in fuzzy and stepwise acceptable value for MMRE less than 0.25 and Pred (0.25) greater or than 0.75 is desirable. The study has been conducted on a group of software project management. Successful software project risk management will greatly improve the probability of project success.