Managing Software Project Risks (Analysis Phase) with Proposed Fuzzy Regression Analysis Modelling Techniques with Fuzzy Concepts

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

Modelling and Evaluating Software Project Risks with Quantitative Analysis Techniques in Planning Software Development

Risk is not always avoidable, but it is controllable. The aim of this paper is to present new techniques which use the stepwise regression analysis tomodel and evaluate the risks in planning software development and reducing risk with software process improvement. Top ten software risk factors in planning software development phase and thirty control factors were presented to respondents. This study incorporates risk management approach and planning software development to mitigate software project failure. Performed techniques used stepwise regression analysis models to compare the controls to each of the risk planning software development factors, in order to determine and evaluate if they are effective in mitigating the occurrence of each risk planning factor and, finally, to select the optimal model. Also, top ten risk planning software development factors were mitigated by using control factors. The study has been conducted on a group of software project managers. Successful project risk management will greatly improve the probability of project success.</p

Managing And Controlling Design Process Issues By Using Stepwise Approach Modelling

The objective of study is to introduce stepwise regression approach modelling to mitigate and manage the software design process issues in software project by using proposed controls. In addition, the software design process issues were controlled and modelled by using stepwise regression approach except risk 3. Furthermore, we illustrated the design process issues were mitigated by control techniques in Table 40. However, we need to combine more techniques and artificial optimal algorithms to mitigate the issues in software design process

The Application of Artificial Intelligence in Project Management Research: A Review

The field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance, and engineering. In this work, the authors review a large number of learning techniques aimed at project management. The analysis is largely focused on hybrid systems, which present computational models of blended learning techniques. At present, these models are at a very early stage and major efforts in terms of development is required within the scientific community. In addition, we provide a classification of all the areas within project management and the learning techniques that are used in each, presenting a brief study of the different artificial intelligence techniques used today and the areas of project management in which agents are being applied. This work should serve as a starting point for researchers who wish to work in the exciting world of artificial intelligence in relation to project leadership and management

An Integrated Cybersecurity Risk Management (I-CSRM) Framework for Critical Infrastructure Protection

Risk management plays a vital role in tackling cyber threats within the Cyber-Physical System (CPS) for overall system resilience. It enables identifying critical assets, vulnerabilities, and threats and determining suitable proactive control measures to tackle the risks. However, due to the increased complexity of the CPS, cyber-attacks nowadays are more sophisticated and less predictable, which makes risk management task more challenging. This research aims for an effective Cyber Security Risk Management (CSRM) practice using assets criticality, predication of risk types and evaluating the effectiveness of existing controls. We follow a number of techniques for the proposed unified approach including fuzzy set theory for the asset criticality, machine learning classifiers for the risk predication and Comprehensive Assessment Model (CAM) for evaluating the effectiveness of the existing controls. The proposed approach considers relevant CSRM concepts such as threat actor attack pattern, Tactic, Technique and Procedure (TTP), controls and assets and maps these concepts with the VERIS community dataset (VCDB) features for the purpose of risk predication. Also, the tool serves as an additional component of the proposed framework that enables asset criticality, risk and control effectiveness calculation for a continuous risk assessment. Lastly, the thesis employs a case study to validate the proposed i-CSRM framework and i-CSRMT in terms of applicability. Stakeholder feedback is collected and evaluated using critical criteria such as ease of use, relevance, and usability. The analysis results illustrate the validity and acceptability of both the framework and tool for an effective risk management practice within a real-world environment. The experimental results reveal that using the fuzzy set theory in assessing assets' criticality, supports stakeholder for an effective risk management practice. Furthermore, the results have demonstrated the machine learning classifiers’ have shown exemplary performance in predicting different risk types including denial of service, cyber espionage, and Crimeware. An accurate prediction can help organisations model uncertainty with machine learning classifiers, detect frequent cyber-attacks, affected assets, risk types, and employ the necessary corrective actions for its mitigations. Lastly, to evaluate the effectiveness of the existing controls, the CAM approach is used, and the result shows that some controls such as network intrusion, authentication, and anti-virus show high efficacy in controlling or reducing risks. Evaluating control effectiveness helps organisations to know how effective the controls are in reducing or preventing any form of risk before an attack occurs. Also, organisations can implement new controls earlier. The main advantage of using the CAM approach is that the parameters used are objective, consistent and applicable to CPS