Using Bayesian Networks to Support Managing Technological Risk on Software Projects

Risk management is essential in software project management. It includes activities such as identifying, measuring and monitoring risks. The literature presents different approaches to support software risk management. In particular, the researchers popularly used Bayesian Networks because they can be learned from data or elicited from domain experts. Even though the literature presents many Bayesian networks (BN) for software risk management, none focus on technological risk factors. Given this, this paper presents a BN for managing risks of software projects and the results of a static validation performed through a focus group with eight practitioners. As a result, the practitioners agreed that our proposed to manage technological risks of software projects using BN is valuable and easy to use. Given the successful results, we concluded that the proposed solution is promising.

Performance-Aware Approach for Software Risk Management Using Random Forest Algorithm

Software quality assurance and related methodologies are quite prominent before actual launching the application so that any type of issues can be resolved at prior notifications. The process of software evaluation is one of the key tasks that are addressed by the quality assurance teams so that the risks in the software suite can be identified and can be removed with prior notifications. Different types of metrics can be used in defect prediction model and widely used metrics are source code and process metrics. The focus of this research manuscript is to develop a narrative architecture and design for software risk management using soft computing in integration with the proposed approach of random forest approach is expected to have the effectual results on multiple parameters with the flavor of multiple decision trees. The proposed approach is integrated with the framework of meta-heuristics with random forest in different substances and elements to produce a new substance.

Design for Software Risk Management Using Soft Computing and Simulated Biological Approach

The process of software evaluation is one of the key tasks that are addressed by the quality assurance teams so that the risks in the software suite can be identified and can be removed with prior notifications. Different types of metrics can be used in defect prediction models, and widely used metrics are source code and process metrics. A simulated environment for the entire process shall be generated for multiple scenarios and parameters so that the results and conclusion can be depicted in an effective way. The focus of research is to develop a narrative architecture and design for software risk management using soft computing and nature-inspired approach. The proposed approach titled simulated biological reaction (SBR) is expected to have the effectual results on multiple parameters with the flavor of soft computing-based optimization. The proposed approach shall be integrating the simulation of microbiological process in different substances and elements to produce a new substance.

An Empirical Evaluation of Assorted Risk Management Models and Frameworks in Software Development

Software risk management is one the key factors in software project management with the goal to improve quality as avoid vulnerabilities. The term defect refers to an imperfection that may arise because of reasons including programmers' skills, lack of suitable testing strategies, and many others. When actual results are different from expected result or meeting wrong requirement, it is called defect and it forms the basis of risk escalation in a software project which is obviously not accepted in any type of deployment. Making a reliable software should be risk free from any vulnerability. Along with reliability another issue arises is software quality which is a factor with software risk management. The quality of software is to reduce the occurrence of risks and defects with the objective to produce an effectual value software which is key point of consideration. In this article, is underlined the present assorted risk management strategies proposed and projected by a number of researchers and academicians on the different parameters using benchmark datasets from renowned sources of research.

A Pragmatic Assessment of Approaches and Paradigms in Software Risk Management Frameworks

Software risk management involves the process of prior recognition and the assessment of vulnerabilities with the classification approach so that the risk avoidance mechanism can be implemented. It includes one of the key factors in software project management with the goal to improve quality as well as the avoidance of vulnerabilities. The term defect refers to the imperfection that may arise because of reasons including programmers' skills, lack of suitable testing strategies, and many others. When there is difference in actual and expected result or meeting the wrong requirement it is called a defect and it forms the basis of risk escalation in the software project, which is obviously not accepted in any type of deployment. To make software reliable, the software should be risk-free from any type of vulnerability factor. Along with reliability, another issue that has arisen is software quality in which the associated factor is with software risk management. The quality of software is to reduce the occurrence of risks and defects with the objective to produce effective valued software.

Usage Patterns and Implementation of Random Forest Methods for Software Risk and Bugs Predictions

The software bugs predictions whereby the datasets of different types of bugs are evaluated for further predictions. In this research manuscript, the pragmatic evaluation of random forest approach is done and compared with results with traditional artificial neural networks (ANN) so that the results can be compared. From the outcome, the extracts from random forest are better on the accuracy level with the test datasets used in a specific format. The process of Random Forest (RF) Approach is adopted in this work that gives the effectual outcomes in most of the cases as compared to ANN and thereby the usage patterns of RF are performance aware. The paradigm of RF is used widely for the engineering optimization to solve the complex problems and generation of the dynamic trees. The outcomes and results obtained and presented in this work is giving the variations in favor random forest based optimization for the software risk management and predictive mining. The need of the proposed work and background of the study includes the effective and performance based software bugs detection. The current problem addressed includes the accuracy and multi-dimensional evaluations. The key methodology adopted here to solve the existing problem is the integration of Random Forest approach and the findings are quite effective and cavernous in assorted aspects