Articles
IEEE Transactions on Games (24751510)17(1)pp. 1-12
Model-driven game development (MDGD) leverages the concept of model-driven engineering and game development. The focus of MDGD is to automate the game development process by emphasizing a higher level of abstraction, which will make game development faster and easier. In recent years, researchers in the MDGD community have developed several approaches in this domain. The goal of this article is to survey and classify existing works in MDGD, identify the challenges in this domain, and provide promising future research directions. To achieve this, we conducted a systematic review by selecting 43 articles from a set of 849. The results show that MDE techniques are used to develop games in various genres. 42% of the investigated studies proposed a graphical concrete syntax for game specification and 56% of them used different target environment tools, such as Unity Engine. Moreover, our suggestions include taking advantage of tooling environments and focusing on game components rather than a complete game. © 2024 IEEE.
Journal of Systems and Software (01641212)219
Context: Modeling is an activity in the software development life cycle where experts and stakeholders collaborate as a team. In collaborative modeling, adhering to the optimistic versioning paradigm allows users to make concurrent changes to the same model, but conflicts may arise. To achieve an integrated and consistent merged model, conflicts must be resolved. Objective: The primary objective of this study was to provide a customizable and extensible framework for conflict management in personalized change propagation during collaborative modeling. Methods: We propose CoMPers, a customizable and extensible conflict management framework designed to address various conflicts encountered in collaborative modeling. We present the duel algorithm for automatically detecting and resolving conflicts according to user preferences. The framework utilizes personalized change propagation to customize collaboration and supports the conflict management process by executing the duel algorithm based on user preferences. As a proof-of-concept, we have implemented the CoMPers framework and extended the EMF.cloud modeling framework to demonstrate its applicability. Results: We have constructed a proof-of-concept implementation and conducted a real-world case study, a benchmark experiment, and a user experience evaluation. Our findings demonstrate that: (1) CoMPers enables collaborators to configure propagation strategies according to their habits; (2) CoMPers successfully identifies all anticipated conflicts and achieves a 100% accuracy in conflict handling; (3) The majority of participants agreed that CoMPers is user-friendly for collaborative modeling. Conclusion: This paper presents the CoMPers framework, which is based on personalized change propagation, and helps collaborators customize conflict management activities. The results confirm the feasibility and advantages of consistent and concurrent modeling within the collaborative CoMPers platform, with an acceptable functionality for approximately ten collaborators. © 2024 Elsevier Inc.
The advancements in technology and widespread internet access have revolutionized the way data collection and polling are conducted. Traditional polling methods are often hampered by significant challenges, including high operational costs, limited ability to reach a broad audience, reliance on a large workforce, and the extensive time required for data analysis. These limitations have led to inefficiencies and reduced the effectiveness of traditional approaches. In this paper, we introduce a platform designed to automate the creation of online polls, utilizing a model-driven approach and no-code development platform. This system streamlines the polling process by allowing users to easily design and deploy polls without extensive programming knowledge. The platform also includes built-in functionalities to automatically generate charts and visualizations for data analysis, reducing the need for manual data processing. By simplifying these processes, the platform makes advanced polling tools accessible to non-experts, enabling quicker, more efficient data collection and analysis in a variety of contexts. © 2025 IEEE.
Frontiers in Computer Science (26249898)7
Introduction: Natural Language Processing (NLP) and Large Language Models (LLMs) are transforming the landscape of software engineering, especially in the domain of requirement engineering. Despite significant advancements, there is a notable lack of comprehensive survey papers that provide a holistic view of the impact of these technologies on requirement engineering. This paper addresses this gap by reviewing the current state of NLP and LLMs in requirement engineering. Methods: We analyze trends in software requirement engineering papers, focusing on the application of NLP and LLMs. The review highlights their effects on improving requirement extraction, analysis, and specification, and identifies key patterns in the adoption of these technologies. Results: The findings reveal an upward trajectory in the use of LLMs for software engineering tasks, particularly in requirement engineering. The review underscores the critical role of requirement engineering in the software development lifecycle and emphasizes the transformative potential of LLMs in enhancing precision and reducing ambiguities in requirement specifications. Discussion: This paper identifies a growing interest and significant progress in leveraging LLMs for various software engineering tasks, particularly in requirement engineering. It provides a foundation for future research and highlights key challenges and opportunities in this evolving field. Copyright © 2025 Hemmat, Sharbaf, Kolahdouz-Rahimi, Lano and Tehrani.
