Luxembourg (Europe)

The Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg has entered a collaborative research partnership with financial firm Clearstream, part of Deutsche Börse Group, and consulting firm escent. Over the next four years, a joint research project will explore how Artificial Intelligence (AI) and Natural Language Processing (NLP) can be used to simplify and standardise requirements analysis for IT projects in the financial industry. The project aims eventually to render processes more cost- and time-efficient.

In IT projects, stakeholders must clearly and precisely define user requirements to decide, with the agreement of all stakeholders, on the necessary capabilities and characteristics of a system. This task is especially laborious in the financial industry because of strict legal and regulatory provisions and the multiple stakeholders involved. The new strategic initiative, run in collaboration with escent, a consulting firm specialised in business analysis and requirements engineering, looks to automate the laborious analysis tasks present in Clearstream’s already mature IT system requirements engineering practices.

Requirements typically contain content written in both software modelling and natural descriptive languages. Reconciling the two can be a significant challenge. The objective is therefore to develop software solutions enabling analysts to consistently manipulate – and detect inconsistencies between – both types of content. Further, the project will develop tools to derive acceptance criteria – the ‘pass or fail’ conditions a system must meet in order for it to be accepted by a client – from these requirements documents.

Prof. Lionel Briand, Vice-Director of SnT, emphasised the importance of precise and consistent requirements: “Nearly 50% of budget overruns in IT projects are caused by inadequate requirements which ripple through system design and deployment. Together with our partners, we will work at the cutting edge of new technology to introduce automation to the requirements engineering process - reducing incompleteness, inconsistency, and ambiguity to a minimum - and facilitating the analysis of system compliance.”

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SnT - Interdisciplinary Centre for Security, Reliability and Trust

The SnT conducts internationally competitive research in information and communication technology, ICT, with high relevance creating socio-economic impact. In addition to long-term, high-risk research, SnT engages in demand driven collaborative projects with industry and the public sector.

Therefore the centre has set up a Partnership Programme with now 32 members targeting strategic areas addressing challenges confronting industry and the public sector in ICT. These resulting concepts present a genuine, long-lasting competitive advantage for companies in Luxembourg and beyond.

SnT has undergone a rapid development since its launch in 2009; recruiting top scientists, launching over 40 EU and ESA projects, creating a technology transfer office (TTO), protecting and licensing IP, launching two spin-offs, and creating a dynamic interdisciplinary research environment with some 260 people.

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Professor Björn Ottersten, Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg, has been awarded an Advanced Grant (AdG), the most prestigious award offered by the European Research Council (ERC).

Björn Ottersten will receive 2.5 million Euros in funding over the next five years. He will use the grant to develop a novel overarching technical framework that could be used to simplify the design and operation of complex systems in different areas such as automotive radar, caching, and wireless networks. This is the second ERC Advanced Grant to be awarded to Luxembourg-based researchers, with both recipients at SnT.

Many automated systems, such as parking assist systems in cars, process data acquired from sensors and make decisions autonomously based on machine learning, a sub-field of artificial intelligence. However, such systems can be enhanced using mathematical models.

For example, in parking assist systems, a mathematical model provides methods for focussing the scan area around the vehicle to obtain reliable data about the scene.  The intelligent algorithms will then better recognise from the sensor data that there is, for example, a bin and a person nearby. The model will automatically focus attention on the person as the critical element.

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