Faculty
Selected Awards
2020: ISSTA 2020 Impact Paper Award for "Mutation-driven generation of unit tests and oracles" (with Gordon Fraser)
2017: MSR 10-Year Most Influential Paper Award for "How Long Will It Take to Fix This Bug?" (with Cathrin Weiß, Rahul Premraj, and
Thomas Zimmermann)
2015: MSR 10-Year Most Influential Paper Award for "When do Changes induce Fixes?" (with Jacek Sliwerski and Thomas Zimmermann)
2015: ICSE 10-Year Most Influential Paper Award, Official Runner-Up for "Locating Causes of Program Failures" isolating
cause-effect chains in failing programs (with Holger Cleve)
2014: ICSE 10-Year Most Influential Paper Award for "Mining Software Histories to Guide Software Changes" introducing mining
software repositories (with Thomas Zimmermann, Peter Weißgerber, and Stephan Diehl)
2009: ACM SIGSOFT 10-Year Impact Award for "Yesterday, my program worked. Today, it does not. Why?" introducing
Delta Debugging
Andreas Zeller is faculty at the CISPA Helmholtz Center for Information Security and professor for Software Engineering at Saarland University, both in Saarbrücken, Germany. His research on automated debugging, mining software archives, specification mining, and security testing has been highly influential. Zeller is an ACM Fellow and holds an ACM SIGSOFT Outstanding Research Award.
ESEC/FSE
ESEC/FSE 2022ESEC/FSE 2022
ESEC/FSE
DemonstrationsESEC/FSE 2022
PLDI
PLDI 2022PLDI 2022
ACM Transactions on Software Engineering and Methodology
ESEC/FSE
Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software EngineeringESEC/FSE 2021
ESEC/FSE
ESEC/FSE 2021ESEC/FSE 2021
ICSE
International Conference on Software Engineering (ICSE)International Conference on Software Engineering (ICSE)
ICSE
ICSE 2021ICSE 2021
Empirical Software Engineering
Automated Testing and Debugging
It is estimated that programmers spend half of their time testing and debugging their code. Wouldn't it be great if computers could help automating these boring tasks?
In this proseminar, we explore and evaluate automated techniques for testing and debugging software. We look at a number of classic approaches for generating software tests automatically, for finding errors and locating faults, and for having these two interact with each other. You will be
For every technique, we will be providing you with the papers (and a reference or textbook implementation, if available). For the presentation, we use standard presentation software; for demonstrations, Python and Jupyter Notebooks have shown to be practical. During the seminar, you will refine your presentation and demonstration skills with us up to the final (and decisive) presentation.
The first part of the seminar consists of ~10 virtual (Zoom) sessions of one hour, with at most one session per week. In each of these sessions, we will have two short presentations and a feedback and discussion round.
In the second part of the seminar, participants will give their final, graded presentations. This part consists of several (at most three) block sessions, which might be held virtually or on-site.
Attendance in all (virtual and on-site) proseminar meetings is mandatory.
Requirements: Programming skills will be required for demonstrations. Knowledge of Python and experience with Jupyter Notebooks is helpful, but can be acquired during the proseminar.
Registration: To register, use the central system of the CS department.
Security Testing
Software has bugs, and catching bugs can involve lots of effort. This course addresses this problem by automating software testing, specifically by generating tests automatically. Recent years have seen the development of novel techniques that lead to dramatic improvements in test generation and software testing. In this course, we explore these techniques – in theory and in code.
Every week, you will be provided with Jupyter Notebooks that teach a particular topic and illustrate it using plenty of runnable Python code. These notebooks come from The Fuzzing Book, a textbook on how to generate software tests written by yours truly.
In the notebook, you can edit the code as you like, run your own experiments, and re-use and extend the code to your liking. Your task will be to use these techniques (and their code) to build a series of fuzzers (i.e. test generators) that find bugs in a number of challenging settings.
This course uses the "inverted classroom" principle – you learn at home, and discuss issues with your instructor. In our weekly meeting, we use the gathering in the lecture hall to
These meetings come with live coding, so we can explore ideas right on the go.
During this course, you apply the techniques learned in weekly exercises and two projects which form your coursework. Projects are graded for effectiveness, efficiency, elegance, and creativity. Projects offer special challenges which allow you to gain bonus points.
Every week, you get a simple exercise assignment covering the material of the last lecture. Performance in these exercises will make 33% of the final grade. Note that there is no final exam.
Advanced programming skills (such as obtained after two years of successfully studying CS) are required. Knowledge in Python is useful, but can easily be acquired along the course.
To pass this course, you need to have
Your final grade is determined by 66% projects and 33% exercises (see above).
The course is organized as "inverted classroom": Every week, we discuss a chapter of the book, which will be supplied with an introduction video; we meet once a week to discuss the material, the associated exercises, and the ongoing projects.
The lecture plan may be subject to changes; these will be announced in time.
Proseminar: Automated Testing and Debugging
In this proseminar, we explore and evaluate automated techniques for testing and debugging software. We look at a number of classic approaches for generating software tests automatically, for finding errors and locating faults, and for having these two interact with each other.
Seminar: Advanced Fuzzing Techniques
In this seminar, we explore and evaluate automated test generation techniques (fuzzers) and related techniques for their effectiveness and efficiency. We discuss and design evaluation criteria and apply them on a number of techniques from the "Fuzzing Book" (https://www.fuzzingbook.org). Apart from reporting and presenting your results in the seminar, your evaluation results will be included in the book. If the results or techniques are novel (many of them are), we will also strive to publish them as a scientific paper, with you as co-author.
Advanced Lecture: Generating Software Tests
Software has bugs, and catching bugs can involve lots of effort. This course addresses this problem by automating software testing, specifically by generating tests automatically.