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News
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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?

Перспективные направления совершенствования процессов жизненного цикла разработки программного обеспечения

Прикладная информатика. 2024. Т. 20. № 2.2025. С. 79–96.
Stoyanova O., Окусков И. С.

The article explores the ways to increase the operational efficiency of IT
companies by improving approaches to solving tasks at different stages of the software
development life cycle (SDLC). It is shown that the greatest potential for growth of operational
efficiency lies in the tasks of the stage of processing requirements. Automation of these tasks
via artificial intelligence tools, especially those based on large language models (LLM), will
significantly reduce the duration of SDLC by cutting the number of iterations for rework and
error correction. The paper analyses the capabilities of such tools, as well as the complexities
associated with their implementation, including the risk of technological dependence, the
problems of integrating new tools into the current IT landscape of companies, the risks of
disrupting the continuity of processes during implementation, as well as the difficulties of
assessing the economic effects. The paper concludes that a comprehensive approach to SDLC
modernisation is needed, which should combine technological innovation with organisational
changes in the form of transformation of corporate culture and processes aimed at adapting to
new technologies. Directions for further research are suggested, including the development of
universal methodologies for implementing AI tools and models for assessing their economic
efficiency.

Research target: Economics and Management Engineering and Technology
Language: Russian
Full text
DOI
Text on another site
Keywords: искусственный интеллектоперационная эффективностьИТ-компанияIT companyoperational efficiency artificial intelligencesoftware development life cycleжизненный цикл разработки программного обеспечения
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