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Abstract: Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering problems. However, a comprehensive and holistic understanding of the current status needs to be included. To close the above gap, synthetic knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and 5 themes—namely, natural language processing in software engineering, use of artificial intelligence in the management of the software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934), Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded, showing the strong interest in this research topic. The convergence of AI and software engineering can significantly reduce the required resources, improve the quality, enhance the user experience, and improve the well-being of software developers.
Keywords: software engineering, artificial intelligence, machine learning, synthetic knowledge synthesis, AI
Published in DKUM: 30.07.2025; Views: 0; Downloads: 12
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Abstract: This study investigates the effects of LLM-based coding assistance on web application development by students using a frontend framework. Rather than comparing different models, it focuses on how students interact with LLM tools to isolate the impact of query type on coding success. To this end, participants were instructed to rely exclusively on LLMs for writing code, based on a given set of specifications, and their queries were categorized into seven types: Error Fixing (EF), Feature Implementation (FI), Code Optimization (CO), Code Understanding (CU), Best Practices (BP), Documentation (DOC), and Concept Clarification (CC). The results reveal that students who queried LLMs for error fixing (EF) were statistically more likely to have runnable code, regardless of prior knowledge. Additionally, students seeking code understanding (CU) and error fixing performed better, even when normalizing for previous coding ability. These findings suggest that the nature of the queries made to LLMs influences the success of programming tasks and provides insights into how AI tools can assist learning in software development.
Keywords: large language models, LLMs, prompt engineering, query type analysis, AI-assisted programming, educational software development
Published in DKUM: 23.06.2025; Views: 0; Downloads: 4
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Abstract: Interconnected computers and software systems have become an indispensable part of people’s lives in the period of digital transformation. Consequently, software quality research is becoming more and more critical. There have been multiple attempts to synthesise knowledge gained in software quality research; however, they were focused mainly on single aspects of software quality and did not structure the knowledge holistically. To fill this gap, we harvested software quality publications indexed in the Scopus bibliographic database. We analysed them using synthetic content analysis which is a triangulation of bibliometrics and content analysis. The search resulted in 15,468 publications. The performance bibliometric analysis showed that the production of research publications relating to software quality is currently following an exponential growth trend and that the software quality research community is growing. The most productive country was the United States, followed by China. The synthetic content analysis revealed that the published knowledge could be structured into six themes, the most important being the themes regarding software quality improvement by enhancing software engineering, advanced software testing and improved defect and fault prediction with machine learning and data mining.
Keywords: software engineering, software quality, knowledge synthesis, bibliometrics, synthetic knowledge synthesis
Published in DKUM: 07.04.2025; Views: 0; Downloads: 6
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Abstract: This study aims to explore the relation between conflict in the project team and user resistance to change in software projects. Following a cross-sectional research design, a survey was conducted among 1,000 largest companies in Slovenia (N = 114). The results of PLSSEM analysis indicate that task and process conflicts in the project team are associated with user resistance. This study is among the first to associate conflict within the project team and user resistance in the implementing organization. It is also one of the first studies to investigate the relations between different types of conflict and user resistance. Project managers may invest resources into adequately managing conflicts within the project team related to tasks in which the project team interacts with users of developed software to lower user resistance. Project with poorly defined roles (e.g., agile and information security projects) may be more prone to user resistance than projects with clearly defined roles.
Keywords: project management, software project, relationship conflict, task conflict, process conflict, resistance to change
Published in DKUM: 11.12.2024; Views: 0; Downloads: 8
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Abstract: In modern software development, three of the most popular application architectures are commonly used: monolithic, service-oriented, and microservices. At the same time, plenty of different methodologies, primarily agile ones, and tools are in use to plan and manage software projects. The research studies relations between these two entities to define the most popular and suitable methodologies and tools for projects, implementing each specific architecture. The research consists of 2 parts: qualitative and quantitative. The first one includes the study of the theoretical basis and related sources to find out the most popular modern methodologies and tools considering their characteristics and each specific architecture. Based on these characteristics, a definition of the most suitable ones is made. The second part represents preliminary statistical research among people, currently employed in software development companies about their opinions on the thematic. The second part includes 36 professionals, employed in different roles. In this part, descriptive statistics and correlation analysis are used. The results have some limitations related to the limited scope of literature studied and also to the size and representativeness of the sample. However, they are useful both for people working in software development, especially for leading personnel, making decisions about project management, and for researchers as a good starting point for continuing research. The results show the necessity of further investigation of the topic due to inconsistency between the scientific literature research and real business world research. Also, we have found out possible correlation between employees’ satisfaction and tools used in project management. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of University of Maribor’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. If applicable, University Microfilms and/or ProQuest Library, or the Archives of Canada may supply single copies of the dissertation.
Keywords: agile methodologies, software, monolithic architecture, service-oriented architecture, microservices
Published in DKUM: 25.09.2024; Views: 0; Downloads: 40
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