Managing Software Project Risks (Implementation Phase) with Proposed Stepwise Regression Analysis Techniques

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.

Risk and risk management in software projects: A reassessment

Software project risk analysis using Bayesian networks with causality constraints

Diagnosing organizational risks in software projects: Stakeholder resistance

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.

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.

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.

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.

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.

In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user‐related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.

This paper presents the arguments for creation and validation of a composite process framework (CASDPF) that would enable formal modeling and management of the application software development & integration. The arguments for this research are based on the practical understanding of the authors that no single 'type' of process appears to be suitable for software development projects. Increasingly, there appears to be a need to use high-ceremony 'waterfall' type approaches at the programme and project management level, whereas at individual level, 'agile' aspects of a process seem to help. A composite process framework has the potential for accepting this responsibility of balancing the modeling with software projects, as is argued in this paper. The development of software application systems, and management of the corresponding systems development life cycle is considered complex, time consuming and costly. This is so because, as argued by Unhelkar [5], the nature, type and size of software projects are different and so they result in differing requirements, development and implementation procedures.. Marks [3] indicates that the Waterfall model does not provide good project planning control and risk management. According to Marks [3], the iterative process has many strengths such as rapid feedback from users based on the actual system prototype, flexibility to address system evolving requirements, the rapid discovery of design flaws, and ease of deploying new functionality. An iterative project might run the risk of never ending process, due to its ability to react to continuous changes of business needs, and incomplete system documentation due to the fact that there is no scheduled period for documentation. The agile methods are suitable for projects with requirements that are rapidly changing and not very suitable for systems that have high criticality, reliability and safety requirements [2]. Agile methods are evaluated based on project criticality, size and requirements. Boehm [1] Spiral life-cycle model that combines features of the prototyping and Waterfall model [4] is favored for large, expensive, and complicated projects. The following model as shown in (Figure 1). Shows that the Waterfall approach is best for project management as it gives the project manager full control over the project life-cycle. The iterative approach is the best during the analysis, design and implementation phases since it gives the project team more flexibility to address evolving system requirements and to iterate within these phases to ensure a comprehensive analysis and design has been done. The Agile approach is best during the implementation and testing phases since it helps produce incremental small software releases within a short iterative development cycles. The Composite approach will split the project into a set of increments; each increment will consist of a set of the project life-cycle phases. For each increment, it will bring the best aspects of available software development processes (Waterfall, Iterative and Agile) together that can be used to develop and manage application software development.

Knowledge management elevates individual knowledge to the organizational level by capturing and sharing information and turning it into organizational knowledge. In order to provide a better understanding of the most serious software project risks and the interrelations among risks, we collected software project data from developers. This data includes information about senior management, customers and users, requirements, estimation and scheduling, the project manager, the software development process, and development personnel. In order to elevate our data to organizational knowledge we conducted a variety of studies on this data and found that the most critical success factor was good requirements. Other critical success factors were either influenced by the requirements, or themselves influenced the development of the requirements.

Rehabilitation protocol after suturing the medial meniscus of a stable knee, a retrospective series of the Francophone Arthroscopy Society

Risk management and assessment of software project is highly relevant to the social and culture context of the development activities. This paper proposes a risk analysis approach which represents the organizational settings of a project as a social dependency network, and identifies risks originated from broken role dependency relations, unsatisfied goals, unfinished tasks, and unavailable resources amongst the network. Survey results on Chinese culture implications to software projects are incorporated as an example scenario to illustrate how to evaluate the influences of culture related factors to software project risk management using the proposed approach.

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