Software systems are among the most complex artifacts created by humans. Modern applications consist of millions of lines of code, evolve continuously, and must meet strict requirements for reliability, security, and maintainability. In this special interest group, we study how artificial intelligence and machine learning can support and automate key activities in the software development lifecycle. Our goal is to develop AI-based techniques that help developers build higher-quality software more efficiently and at larger scales.

From a technical perspective, our research focuses on applying machine learning, large language models, and data-driven methods to software engineering tasks such as code generation, automated testing, bug detection, program repair, and software maintenance. By analyzing large corpora of source code and development histories, AI systems can learn patterns of correct implementations, identify potential defects, and suggest improvements to existing code. These capabilities open new possibilities for intelligent developer tools that assist programmers during design, implementation, and debugging.

A key focus of this SIG is understanding how developers and AI systems can effectively collaborate in the software engineering process. We investigate how AI-driven tools can be integrated into development workflows while ensuring reliability, transparency, and developer control.

Chair

• Jürgen Cito, Associate Professor for Software Engineering, TU Wien

Board Member

• Dominik Bork, Assistant Professor for Business Systems Engineering, TU Wien