Federal Funding: AI Research for Secure Software Development

A research project at the University of Würzburg (JMU) has received 137,000 euros in federal funding. The project will develop an AI early warning system to prevent errors in the development of new software.

Keeping track of complex software development is not easy – a new research project at the University of Würzburg aims to solve this problem. (Image: Scholtes/JMU)

The research project is being carried out by a team led by Professor Ingo Scholtes, chair of Machine Learning for Complex Networks at the CAIDAS Centre for Artificial Intelligence and Data Science. “Our goal is to create an AI-based early warning system for software development,” he explains. “It should detect problems in the organisational structure, provide early indications for emerging issues and help managers to take countermeasures.” That makes it a real game changer – especially for complex software development, which often involves many people over long periods of time. “Managers can use the platform to quickly see who is working on what tasks, where key roles in the team are located, and what potential sources of error exist.” The project is scheduled to begin in April 2024.

Funding from an Exclusive Federal Funding Pot

The Würzburg research is being funded with 137,000 euros from the coveted “Software Campus” funding programme. Announced by the German Federal Ministry of Education and Research (BMBF), this programme for the education and training of IT managers is currently only available to a small number of universities. It emphasises the transfer of research into practice and the promotion of researchers in the early stages of their careers. “This is also an important criterion for our study,” says Scholtes. “The team is not led by me, but by our PhD student Lisi Qarkaxhija. He is a research assistant at my chair and is researching new deep learning methods as part of his doctorate.”

Scholtes has brought DATEV, an IT service provider for tax consultants, accountants and law firms and their mostly medium-sized clients, on board as an industry partner. “This collaboration is an absolute win-win situation,” says the professor. “We researchers can observe first-hand where the biggest challenges lie in the management of large software projects in practice – and our partner gains access to the latest AI methods from our research.”

Previous Research Serves as a Basis

The development of the new AI platform is based on Scholtes’ previous studies on the social organisation of software teams. In these studies, for example, the computer scientist was able to show that the so-called Ringelmann effect also occurs in software projects. This is a phenomenon known from social psychology – it describes how the individual contribution of a team member tends to decrease as the size of the team increases. Put more simply, in large teams people tend to put in less effort because the responsibility for the outcome is spread across more shoulders. Scholtes’ team was also able to show that the structure of the collaboration network (i.e. who works with whom) influences how strongly this effect manifests itself in a team. The new project will use these findings to identify problematic collaboration structures and provide information on how to improve them.

The Würzburg researchers have recently started to create a database for the project. They are using git2net, a software developed at the chair, which automatically analyses data from the online collaboration platform github.com and generates temporally resolved collaboration networks between the members of a software team. These form the basis for further analysis, visualisations and AI applications.

The original title of the research project is “Data-Driven Platform for Proactive Risk Management in Software Development Projects”.

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