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Research Group

Zeller

Software Research

At Zeller's group at CISPA, we do software research. We create tools and techniques that help developers build better software – by automatically testing, analyzing, and debugging its code and its development process. Our research projects involve generating software tests, automated debugging and repair, analyzing mobile systems, analyzing user interfaces, and more – all areas in which we continue to shape academic research and industrial practice.

Head of Group

Andreas Zeller

Email

Address

Stuhlsatzenhaus 5
66123 Saarbrücken (Germany)

Members

Marius Smytzek

Tural Mammadov

Fengmin Zhu
Placeholder

Batuhan Serce
Placeholder

Marius Stutz

José Antonio Zamudio Amaya

Alexi Turcotte

Alexander Liggesmeyer

Laura Plein
Placeholder

Haoyang Liu

Valentin Huber
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Alireza Kheradmand

Tim Scheckenbach

Norman Becker

Abdullah Alhamdan

Most Recent Publications

Year 2026

2026-10-12

Can Open-Weight LLMs Predict Test Outcomes from Traces? A Cross-Subject Boundary Study on Tests4Py

Conference / Medium

International Workshop on Trustworthy and Responsible aUtonomous SysTems(TRUST) Can Open-Weight LLMs Predict Test Outcomes from Traces? A Cross-Subject Boundary Study on Tests4Py

Tags
Threat Detection and Defenses
Authors
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2026-10-12

How Grammar-Based Synthetic Data Shapes Behavior Learning:
An Empirical Study of Input Generation Strategies

Conference / Medium

International Workshop on Reliable and trustworthy Automated Software Engineering(RASE) How Grammar-Based Synthetic Data Shapes Behavior Learning: An Empirical Study of Input Generation Strategies

Tags
Threat Detection and Defenses
Authors
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2026-10-03

PyMut4SE:
Comprehensive Mutation Testing for Python

Conference / Medium

ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH-companion) PyMut4SE: Comprehensive Mutation Testing for Python

Tags
Threat Detection and Defenses
Authors
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2026-09-04

Neural Program Modeling Learning Program-Specific Models that Predict Execution Features from Input Features and vice versa

Article

ACM Transactions on Software Engineering and Methodology Neural Program Modeling Learning Program-Specific Models that Predict Execution Features from Input Features and vice versa

Tags
Threat Detection and Defenses
Authors
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2026-08-04

Feature-Focused Test Generation

Conference / Medium

Automated Software Engineering Conference (ASE) Feature-Focused Test Generation

Tags
Threat Detection and Defenses
Authors
Full Paper Visit Detail Page