For many years, estimating the costs of ERP system implementations has remained a significant challenge in IT project management due to the high degree of uncertainty and complexity inherent in such projects. In recent years, numerous approaches have been proposed to improve cost estimation accuracy at the early stages of projects, including methods based on fuzzy ontologies. This paper presents a comprehensive review of the current state of knowledge on fuzzy ontologies (both Type-1 and Type-2) and their applications in the cost estimation of IT projects, with a particular focus on ERP system implementations. The article discusses key concepts in fuzzy ontology modelling and the development of this field over recent years, as well as examples of fuzzy ontology implementations in software cost estimation. Alternative approaches to ERP implementation cost estimation are also presented—ranging from traditional algorithmic models to recently popular artificial intelligence (AI) -based techniques. Furthermore, the paper introduces available AI tools and techniques that may enhance fuzzy ontologies in this application domain. In the discussion, a critical comparison is made between the effectiveness of fuzzy ontologies and other AI-based methods in ERP implementation cost estimation, highlighting their advantages, limitations and potential directions for future research.

Background: Information about project tasks stored in Issue tracking systems (ITS) can be used for project analytics or process simulation. Such tasks can be categorized as stateful or recurrent. Although automatic categorization of stateful tasks is relatively simple, performing the same task for recurrent tasks constitutes a challenge. Aims: The goal of this study is to investigate the possibility of employing machine-learning algorithms to automatically categorize recurrent tasks in software projects based on information stored in ITS. Method: We perform a study on a dataset from six industrial projects containing 9,589 tasks and augment it with an additional dataset of 91,145 task descriptions from other industrial projects to up-sample minority classes during training. We perform ten runs of 10-fold cross-validation for each project and evaluate classifiers using a set of state-of-the-art prediction quality metrics, i.e., Accuracy, Precision, Recall, F1-score, and MCC. Our machine-learning pipeline includes a Transformer-based sentence embed-der (‘mxbai-embed-large-vl’) and XGBoost classifier. Results: The model automatically classifies software process tasks into 14 classes with MCC ranging between 0.69 and 0.88 (mean: 0.77). We observed higher prediction quality for the largest projects in the dataset and those managed according to “traditional” project management methodologies. Conclusions: We conclude that machine-learning algorithms can effectively categorize re-current tasks. However, it requires collecting a large balanced dataset of ITS tasks or using a pre-trained model like the one provided in this study.

The widespread use of the software is responsible for the increasing energy consumption of the world’s IT infrastructure. Many techniques, methods, and practices can reduce software’s energy consumption, but software producers do not use them. This paper aims to answer the question of why software developers do not care about energy consumption. In order to answer this question, we distinguished five research objectives and drew up a questionnaire consisting of 13 questions. We then conducted telephone interviews with 22 people from the software industry. We showed that developers need a higher awareness of the problem and more knowledge of proven ways to manage software energy efficiency. The author suggests introducing energy efficiency standards and green energy certificates for applications. Courses in energy-efficient software development at universities help prepare staff for the sustainable software development of the future.

Managing software development processes remains a significant challenge despite tool support. To remedy this situation, we propose better use of data collected in management support systems, a lightweight approach to planning inspired by agile approaches, and the assumption that constant change is inevitable. This approach lies at the heart of the described solution. The paper presents a model for planning tasks in an IT project. Its purpose is to support the management of the software development process. It allows planning work in the project, considering changes and learning new requirements, detecting defects by the testing team and by the client, and their repair. It uses a model that classifies project tasks and calculates task statistics. They control stateful and recurring task generators, creating new implementation tasks and defects during the project. Linking the types of tasks created to the roles of project team members allows for planning the composition of the project team. Two experiments demonstrate the model's replication and prediction capabilities using data from real IT software projects.

Managing software development processes remains a severe challenge despite the support of tools. To remedy this situation, we propose better use of data collected in management support systems, a light approach to planning inspired by agile approaches, and the premise that continuous changes are inevitable. This approach lies at the heart of the described solution. The article presents a simplified software development process model that classifies project work in task types and roles of project team members, uses statistics to generate tasks, and then aggregates the results in iterative (month or sprint) and incremental approaches. The simplicity of macrosimulation allows for implementation in a spreadsheet. Despite its simplicity, the model allows for analysis of the completed project work, planning further courses, selecting employees for the project team, identifying bottlenecks, and performing quick what-if simulations. The macro model concept explains its structure, operation, and components details. Three experiments demonstrate its replicative and predictive capability, using data from real-world IT industry software projects.

The growing trend in agile approach to software development today shows a tendency towards combining practices from different approaches and methodologies. This trend can be explained by the difficulties in managing agile processes. A potential solution to these challenges could be a multi-agent model of software development process of supporting management in the cycle of continuous product quality improvement. The AGent-Object MOdel (AGOMO) was constructed to investigate this idea and an experiment was carried out in which a typical medium-sized project developed using hybrid methodology was reproduced. This article describes the first phase of research, in which replicative adequacy of this model was demonstrated, thus opening avenues for further research related to project management support.

The growing trend to use agile methodologies in project management significantly affects the importance of the selection process of an appropriate management methodology for the project’s size and type. For many projects, hybrid methodologies that are a compromise between soft and hard approaches apply to project management. It is a pragmatic approach used by medium and large organizations, combining predictability and manageability of the traditional process, with high efficiency and flexibility of agile methodologies. To support the research and development process of these methodologies, the authors proposed the use of agent paradigm and the use of multi-agent systems. Its use is a consequence of adaptability of agents to simulate the work of project teams. To test its usefulness, a multi-agent AGOMO (AGent-Object Model) was built and used in the work of the team producing an IT system based on the interaction of agent behaviours and components of the agent system environment. Using this model, research experiments were carried out by evaluating the SCRUM and RUP software production process and the appropriate methods and tools for project planning.

The aim of this article is to present the project framework for constructing a Software Process Simulation Modeling (SPSM) system. SPSM systems can be used as a virtual environment for the selection of methods and tools of project management in IT support organizations. The constructed system simulates the Scrum methodology, including the management processes and the project roles. For the implementation of Scrum processes, the Scrum ontology is proposed and for the competences of the roles of project team members, a fuzzy-logic representation. As a result we present the hybrid fuzzy-ontological system. The framework of the design processes proposed in the article was verified on the basis of real courses of project management processes in a large IT company.