Legacy software, particularly from the 1990s, often becomes obsolete due to aging hardware and outdated software environments. Traditionally, software modernization required extensive manual effort, involving reverse engineering, code rewriting, and re-architecting. However, advancements in large language models (LLMs) have introduced new possibilities for automating software translation and modernization. This paper explores the feasibility of using LLMs for modernizing 90s-era Windows applications, specifically migrating legacy C and C[Formula: see text] code to Python. Our methodology includes decompilation, source code analysis, automated translation using ChatGPT, and user interface reconstruction. We empirically evaluate three software projects by analyzing LLM-based translation accuracy across different code structures, including algorithmic logic, file handling, and graphical interfaces. Results indicate that while LLMs achieve high translation accuracy [Formula: see text] for structured code, challenges persist in handling decompiled code and user interface generation. The study provides insights into the effectiveness and limitations of LLMs in real-world software renovation, offering guidelines for leveraging machine learning in legacy system modernization. These findings contribute to both academic research and practical applications, suggesting a pathway for cost-effective and scalable legacy software migration.

Parallel test forms are ubiquitous in the educational, aptitude, achievement, and licensure testing. The problem of automated assembling of parallel test forms has been extensively explored for almost 40 years. Many different mathematical models of the problem formulations and a plenty of different solutions have emerged over the last two decades, indicating that the problem has matured. However, its investigation is still challenging, especially today, when the importance of distance learning and remote knowledge testing is rapidly growing. The diversity of proposed approaches originated notably from the variety of scientific fields involved such as psychometrics, applied mathematics, operations research, and artificial intelligence. Majority of solutions of the problem are (meta)heuristics-based, since they consider the problem as a combinatorial optimization problem which is NP-hard. In this paper, a comprehensive review of this research field, referring to related works since 1985, is conducted. Problem formulations and solutions of the problem are separately classified. Possible avenues of future research are pointed out.

This paper presents our experience with the development of an automated knowledge test assembler. Items are selected from an item bank on the basis of precisely formulated formal criteria. The focus of our research was on the classification of criteria for item selection and on an algorithm that assembles a test using these criteria. The criteria are expressed through parameters of a heuristic function used to evaluate a test’s quality; they were derived from teaching experience and we consider them to be highly intuitive to teachers. Item selection is performed by searching a state space using modified hill-climbing. The proposed quality function is used to guide the search for the optimal test. The test assembler is implemented inside an in-house software system, developed for preparing and scoring of knowledge tests. We have shown a statistically significant improvement in the mean reliability of the tests assembled by our test assembler, compared to the tests assembled by a widely available state-of-the-art tool. The performance indicators of the test assembler were also measured and analyzed. The experiments’ results encourage both practical use of the assembler and further research.

This paper presents an analysis of usability of mobile prescription reference systems in medical practice, and presents implementation of mobilePDR (Physician's Desk Reference). Various aspects of mobilePDR are discussed: main functions, information content, performance and design issues, independent evaluation with other similar tools, and lessons learned from the system development.

For original paper by T. Eisenbarth et al. see ibid., vol.29, no.3, p.210-24 (2003). We compare three approaches that apply formal concept analysis on execution profiles. This survey extends the discourse of related research by Bojic and Velasevic (2000).