Software project risk analysis using Bayesian networks with causality constraints

Risk and risk management in software projects: A reassessment

Background - Risk Management (RM) practices are usually established towards avoiding or minimizing problems, likely to occur during software development. It can be stated as the task of analyzing and managing the impact of every important action to be performed in the project. Aim - In the context of RM practices, we developed a scoping study, aiming at analyzing the current scenario of RM practices in software development. Method - We analyzed 74 studies published by the most important venues published up to the year 2011. Based on the analyzed dataset, we sketched a set of useful practices for applying RM in software projects. Results - The analysis indicate that most of the studies subjectively describe ways to evaluate risks, instead of providing readers with details on how RM is to be performed. Conclusions - Such findings points out to the need of further research in the field of RM, specially due to its importance for software development projects.

Diagnosing organizational risks in software projects: Stakeholder resistance

The Project Management in the area of IT is becoming hot topic now. The risk problems in IT Projects, especially in software projects become more and more concentration in software project management in China. This paper analyze systematically the characteristics of project management for software itself, then the paper analyses the risk of software development project in following two aspects: one for owners another for contractors. To owners the paper identify and analyze the risk in software development projects according to life cycle of a project. To contractors the paper identifies and assess the risk of project management in software development, and on the bases of investigating of software development project in the IT enterprises of China some conclusions are obtained with the method of AHP.

Software project development has high failure rate. Software project risk management may gain a high rate of return in investment. Establishing an intelligent risk evaluation model for project will be valuable in the analysis and control of project risks. In this paper, we employed neural network (NN) and support vector machine (SVM) approaches to establish a model for risk evaluation in project development. In the model, the input is a vector of software risk factors that were obtained through interview with 30 experts, and the output is the final outcome of the project. The data for modeling were collected from 120 real software projects through questionnaires. The experiment shows the model is valid. Interestingly, SVM is a powerful supervised learning method, and some believe that it is a more promising classification method that may someday supercede NN. In our study, the standard neural network model had lower prediction accuracy compared to SVM due to its tendency in finding local optima. However, after attempt in optimizing the neural network model with genetic algorithm, the experimental results showed that our enhanced model surpassed SVM in performance.

Project organization and management software development project management model is the best form of software development enterprises in China's understanding of software development project management has been greatly improved. Traditional software development projects have different characteristics, therefore how flexible application for software project management theory and strategies of the main research objectives of domestic software development project management. Our software in enterprise application software development project management, general shortcomings in implementation, this article on software research and development analysis and project management of implementation strategies.

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Software risk management refers to systematic process for analyzing and identifying the project risks. The present paper provides a hybrid method for IT software risks identification. Software projects possess different features which increase the project failure possibilities. Therefore, the present work integrate the Artificial Neural network with the Analytic Hierarchy Process (AHP-ANN) in order to solve the problem of software project estimation in early stage. The questionnaire developed to find out the risk functional model and provide the proposed method with proper data. The results observe a major common risk in software projects is the insufficient knowledge based on different software project life cycle stages. Also, there are some other important factors in software projects such as lack of good estimation in project scheduling, poor definition of project requirements which cause human errors.

In a country like Ethiopia, where information and communication systems are in the early stage of development, software projects may face several challenges. Risk is one of the factors that challenges project performance, and even causes failure. Risk management helps project managers to control the effect of risks. However, risk management appears to be the least practiced component of project management. To help understand the software risk management practice in the developing countries (DC) context, this study explores the risk management practice of Ethiopian software projects. It also investigates the level of adoption of formal risk management models and which steps are included in the ad‐hoc risk management exercise. Finally, it looks into the relationship between risk management practice and project success. We conducted a survey of 45 banks, insurance companies and United Nations agency offices in Addis Ababa, Ethiopia. In this study, a very low rate of application of formal risk management models was observed. We observed a different perception of risk management whereby only watching projects to see if any risks occur during implementation without performing risk identification and mitigation or response plan was considered as a risk management practice by project managers. It was also found that some project managers were not able to confidently tell whether risk management processes have taken place in the projects they manage.

The risk of software projects is measured in terms of cost that is needed to abate the risk. Traditional practice to measure the risk of software projects uses risk exposure; however, risk measure cannot quantify the risk beyond the expected value of cost. Software project managers are keen to quantify the risk based on a certain probability which is beyond the expected value of the cost. This research work presents a model to measure the risk based on certain probability beyond the expectation. A case-study validates that proposed model shows an improvement in the measurement of risk of real software projects compared to the actual risk of software projects.

The paper establishes a framework for software project risk management and presents a method for risk priority management as a module in the system on the basis of Risk Management Information System. It provides a possible framework for the construction of a concrete database including a possible list of risks and fields of risk description as well as an empirical research on the identification of important risks through Delphi Method, AHP, and computerized calculations. The study is aimed at providing guiding concepts for the establishment of software project Risk Management Information Systems, thus reducing the cost and increasing the efficiency of risk management for software projects.

Risk management in software projects through Knowledge Management techniques: Cases in Brazilian Incubated Technology-Based Firms

The primary objective of this paper is to examine the market risk and liquidity risk management techniques and practices followed by the Indian scheduled commercial banks (SCB) consisting of public sector banks (PSBs) and private sector banks (PVSBs) for five years from 2016-17 to 2020-21. The other objective is to compare market and liquidity risk management practices between the public sector banks (PSBs) and private sector of banks (PVSBs). The other purpose of the study is to review the strategies adopted by the SCBs in risk management practices. To study the risk management practices, six largest banks each from PSBs and PVSBs are taken for sample study. This study finds that the SCBs are facing credit risk, market risk (Interest rate risk, foreign exchange risk, commodity price risk and equity price risk,) liquidity risk and operational risk. It also finds that the PSBs are better reporting and presenting their risk management practices in their annual reports than that of PVSBs in risk identification, risk assessment and risk analysis. The results indicate that there is no significant difference between the PVSBs and PSBs in the policies and practices of market risk and liquidity risk assessment, evaluation, monitoring risk controlling and risk taking. This paper will be relevant and value to those interested in research in the risk management banking industry. This is a descriptive research based on secondary data.

BBN-based software project risk management

Regardless how much effort we put for the success of software projects, many software projects have very high failure rate and risks are everywhere in life. Risk is not always avoidable, but it is controllable. The aim of this paper is to present new techniques by which we can study the impact of different risk management techniques and different risk factors on software development projects. The new technique uses the stepwise regression to managing the risks in a software project and reducing risk with software process improvement. Top ten software risk factors (Implementation phase) and thirty control factors were presented to respondents. The results show that all risks in software projects were important in software project manager perspective, whereas all controls are used most of time, and often. These tests were performed using regression analysis to compare the controls to each of the risk factors to determine if they are effective in mitigating the occurrence of each risk factor and selecting best model. We referred the risk management techniques were mitigated on ten top software risk factors in Table XLVI. The study has been conducted on a group of software project managers. Successful project risk management will greatly improve the probability of project success.