Different conceptions in software project risk assessment

A software development project can be considered to be risky project due to the uncertainty of the information (customer requirements), the complexity of the process, and the intangible nature of the product. Under these conditions, risk management in software development projects is mandatory, but often it is difficult and expensive to implement. Expert COCOMO is an efficient approach to software project risk management, which leverages existing knowledge and expertise from previous effort estimation activities to assess the risk in a new software project. However, the original method has a limitation because it cannot effectively deal with imprecise and uncertain inputs in the form of linguistic terms such as: Very Low (VL), Low (L), Nominal (N), High (H), Very High (VH) and Extra High (XH). This paper introduces the fuzzy-ExCOM methodology that combines the advantages of a fuzzy technique with Expert COCOMO methodology for risk assessment in a software project. A validation of this approach with project data shows that fuzzy-ExCOM provides better risk assessment results with a higher level of sensitivity with respect to risk identification compared to the original Expert COCOMO methodology.

Many uncertainties always exist in the development and management process of software project, the awareness and ability of software project risk management must be strengthened, in order to effectively prevent the occurrence of the failure of it. In this paper, on the basis of the analysis of the software project risk management process, a model based on fuzzy theory in software project risk assessment is provided. Using the fuzzy language to assess the consequences and the loss of the risk, the model can measure a combination of impact of a certain risk from a variety of risk and the combined effects of the overall consequences from individual risk, which resolves the uncertainty of expert assessment, enhances the prediction and response capabilities of the software project risk and provide a new way to effectively reduce the risk probability and increase the rate of the success of the software development.

Risk and risk management in software projects: A reassessment

Diagnosing organizational risks in software projects: Stakeholder resistance

Software project risk analysis using Bayesian networks with causality constraints

This paper concerns the role of human errors in the field of Early Risk assessment in Software Project Management. Researchers have recently begun to focus on human errors in early risk assessment in large software projects; statistics show it to be major components of problems in software over 80% of economic losses are attributed to this problem. There has been comparatively diminutive experimental research on the role of human errors in this context, particularly evident at the organizational level, largely because of reluctance to share information and statistics on security issues in online software application. Grounded theory has been employed to investigate the main root of human errors in online security risks as a research methodology. An open-ended question was asked of 103 information security experts around the globe and the responses used to develop a list of human errors causes by open coding. The paper represents a contribution to our understanding of the causes of human errors in information security contexts. It is also one of the first information security research studies of the kind utilizing Strauss and Glaser's grounded theory approaches together, during data collection phases to achieve the required number of participants' responses and is a significant contribution to the field.

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.

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.

This work characterizes software industry and software project risk management in China nowadays, discusses the potential ways to improve risk management capability of Chinese software industry. Based on the development status of software industry, we probe into the countermeasures of software project risk management. Construct mature software companies; implement metrics program to support quantitative risk management; increase companies' scale, confront risk with large scale; strengthen research on risk management technology and enhance project managers' risk management consciousness. We will go into details of these perspectives from the viewpoint of software companies.

Generally, people have used the term “risk manager” in different fields of science to represent a person who is responsible for handling risk management activities. However, in the field of software engineering, the term “risk manager” is not well defined and there is ambiguity over the term's meaning. The main objective of the paper is to review the literature thematically in order to make the terms understandable and to find the role of a risk manager for effective software project risk management. Additionally, in the end of the paper different title of software project risk managers has been classified on the base of their roles and responsibilities.

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

BBN-based software project risk management

Application of fuzzy theory in software project risk assessment

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.