Risk analysis in project of software development

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.

Software project risk analysis using Bayesian networks with causality constraints

Purpose The study aims to investigate the application of the data element market in software project management, focusing on improving effort estimation by addressing challenges faced by traditional methods. Design/methodology/approach This study proposes a solution based on feature selection, utilizing the data element market and reinforcement learning-based algorithms to enhance the accuracy of software effort estimation. It explores the application of the MARLFS algorithm, customizing improvements to the algorithm and reward function. Findings This study demonstrates that the proposed approach achieves more precise estimation compared to traditional methods, leveraging feature selection to guide project management in software development. Originality/value This study contributes to the field by offering a novel approach that com-bines the data element market, machine learning, and feature selection to improve software effort estimation, addressing limitations of traditional methods and providing insights for future research in project management.

In project-based organizations knowledge is a critical resource used to develop and deliver products and services with a high level of quality. Therefore, a systematic and sustainable process is necessary to coordinate knowledge management, project management and product lifecycle. This scenario predominates in companies focused on the creation and maintenance of information systems. This article presents an exploratory study based on a framework that integrates cognitive, managerial, and operational processes in a public Brazilian organization that provides services in the area of information and communications technology, focusing on the construction and maintenance of information systems. Those processes are operationalized by three management models considering knowledge, project, and software development processes. Our proposal aims to understand the relationships between those three management models and their influence on the software development process in the organization under study. Our premise is based on the principle that cognitive management, project management, and software development management must be integrated to fulfill the demands of product development and service provision. The research data was composed of registers of working hours spent on software development and maintenance projects involving 244 people allocated to 5064 projects in the period from 2007 to 2013. The study resulted in the identification of the relationships among the three management models adopted by the organization, with emphasis on knowledge management activities, which were not directly identified, making it difficult to account for and measure them. We established a set of activities connected to each one of the knowledge management model phases. Since those activities were not visible before, our approach contributed to build a systematic process to register and relate activities linked to the dimensions of cognitive processes, project management, and software construction.

Software cost estimation is a process of planning, risk analysis, and decision making for project management in software development. Cost of project development encompasses a software project’s effort and development time. One popular model of software cost estimation is constructive cost model (COCOMO) model, which is a mathematical model proposed by Boehm, used for estimate the software effort and development time. The objective of this paper is to improve the basic COCOMO model’s coefficients for modern programming languages like Python, R, C++, etc. Many techniques were presented in the past for effort and time estimation using machine learning. But all these techniques were trained and tested for older programming languages. In order to improve the accuracy of COCOMO for modern programming languages, six Python projects have been considered and genetic algorithm (GA) is applied in these projects to define new values for basic COCOMO coefficients and the development time is calculated for Python projects. The time estimated using GA coefficients is compared with the original COCOMO and actual time. Using mean magnitude relative error, the error from the original COCOMO time is 54.49% and error from GA time is 21.23%.

In order to effectively avoid risks that might result in loss of failure in software development process, based on the experiences of software development and project management, this paper identifies 4 potential risk factors specific to software development projects which are integrated with 6 stages in software development process, and proposes a non-linear programming model to optimize funds allocation to reduce the risks. The paper provides an example to validate the effectiveness of the model.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

A discrete event simulation model in the case of managing a software project

Software development projects face significant risks of going over budget, over schedule, and of failing to deliver expected benefits. These risks have endured despite methodological changes within the industry, such as the rise in popularity of Agile. This research looks outside of the software industry and seeks inspiration from film production, which has developed different management strategies to address challenges similar to those faced by software development projects.First, an analysis of film production project management reveals four principles, each evidenced by a set of practices used throughout the industry. Next, we identify sets of practices that would enable software development projects to also align to those same four principles. The synthesis of each principle from its set of supporting film production practices is then validated through structured interviews with veteran film producers. Lastly, the derivation of each principle to a parallel set of supporting practices within the context of software development is validated in a second series of interviews with experienced software development project managers.In total, this research identifies a set of software development project management practices that mirror film production practices. These practices offer a framework for software development project managers to consider when seeking to tailor existing methodologies, particularly in scenarios that present challenges similar to those encountered in film production.

This paper examines the application of generative artificial intelligence in stakeholder management while studying the business aspects of software development and project management in two different universities. It explores a novel intersection of AI with software development and project management practices, offering valuable insights for both academia and industry. By investigating how students use AI alongside traditional methods under supervision, this study evaluates the effectiveness, quality of results, and creativity of students’ project assignments in identifying stakeholders and defining communication strategies. The findings suggest that AI can enhance work completion speed and contribute to greater project success due to a more complete identification of stakeholders and formulation of innovative stakeholder engagement strategies. There is a consensus, within this context, that while AI can be invaluable for project stakeholder management, human judgment remains essential.

The management of software development for single and dual processor system designs is making progress towards becoming a mature discipline. A good part of the progress can be attributed to the development and use of standard system and software engineering methods and design principles.However, new computer system designs (networking, distributed systems, embedded systems, multi- and coprocessors, fault tolerant systems, etc.) will create new challenges for managers of software development. The reason for this effect on management is that some of the system and software engineering methods and design principles developed for single and dual processor system designs are not valid for these newer designs.Some of the issues that software development project managers will need to cope with are:1. Life cycle model adjustments2. Rapid prototyping activities3. Different hardware and software phasing4. Increased tool development5. New trade-offs and hybrid developments of off-the-shelf software and newly developed software6. Development of concurrent design principles7. New software design principles to support fault tolerance and the use of new memory technologiesThe above items are just now being recognized as problems, and solutions for them either do not exist, or are not widely known.These problems create a series of new challenges that managers must deal with for software development based on the new architectures and requirements. The purposes of this paper are to discuss these issues and to identify some solutions that can serve in the interim as the technology changes to meet these new challenges.

A process model for software project management

The actual development of the IT sector generated software products that are now a significant part of any field. The increasing number of IT products led to changes in the project management of software development. If, in the beginnings, the project management tools were limited and included approaches such as: the waterfall model, the incremental spiral model, prototyping, and rapid application development, but as of lately, the quality management specific tools found a place amongst the new tools used. The proposed model uses the Quality Function Deployment (QFD) as tool in software development, which offers an accurate picture of the current state of the degree to which software meets customer requirements by calculating an offset. In developing the model we took into account the principles of Agile and the concepts introduced by Scrum, in order to integrate the proposed instrument in a widely used methodology that connects the steps between idea and market.

Software development industry is a key factor in the worldwide economy. In Mexico, this sector consists mostly of small and medium enterprises (SMEs) and they represent the 99% of the business software industry. This highlights the importance of implementing best practices in software engineering SMEs to ensure continuous improvement. In this context, there are several models, methodologies and standards that contain best practices focused on improving software development processes such as CMMI-DEV v1.3, ISO 12207, ISO 15504, etc. However, to implement it in SMEs they should be tailored according to the organization size and type of business. Therefore, SMEs require the use of techniques and tools that help them implementing the best practices. This paper aims to present a proposal of a method for creating catalogs of tools and techniques related to software development project management, specifically in risk management, that will provides a set of techniques and tools than an SME can select according to its specific needs in order of facilitate the implementation of best practices.

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.