Andreas Zeller
@andreaszeller.bsky.social
1.4K followers 140 following 65 posts
Software researcher at https://cispa.de, working on #Fandango, #S3, #FuzzingBook, #DebuggingBook. Testing, debugging, analyzing, and protecting software for a better world. Find me at https://andreas-zeller.info/
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andreaszeller.bsky.social
Introducing #Fandango 💃 — a powerful bio-inspired test generator designed to supercharge your software testing. Fandango gives you unprecedented control over test inputs, integrating grammars, constraints, and Python. Now with protocol testing!

➡️ Check out Fandango at fandango-fuzzer.github.io
andreaszeller.bsky.social
On my way to ACM CCS 2025 in Taipei (via Istanbul). Meet me there!
andreaszeller.bsky.social
(The listed problems come from a Google AI summary, where I asked "What are the worst problems of Germany?")
Reposted by Andreas Zeller
claresudbery.bsky.social
Did you know there are tools you can use for automated debugging, and you can build them yourself? Have you come across @andreaszeller.bsky.social and his amazing creation The Debugging Book, which is so much more than just a book? Did you know you can access The Debugging Book for free?
andreaszeller.bsky.social
25 years of delta debugging! On this day in 2000, I presented “Simplifying Failure-Inducing Inputs” at ISSTA - now one of the most influential works in the 50-year history of Transactions on Software Engineering. Read all about its genesis and impact at doi.ieeecomputersociety.org/10.1109/TSE....
25yrs of delta dbg
andreaszeller.bsky.social
Proud and honored to have German Chancellor Friedrich Merz visit us to be shown our latest and greatest research! This is a clear signal of how relevant @cispa.de has become - and we’re still growing and expanding.
cispa.de
Federal Chancellor Friedrich Merz visits CISPA 🚀🔐

During his inaugural visit to Saarland, Friedrich Merz stopped by CISPA together with Minister President of Saarland Anke Rehlinger. The focus was on our cutting-edge research in cybersecurity and trustworthy AI.
cispa.de/en/federal-c...
andreaszeller.bsky.social
Always use these six sentences in your paper rebuttal
Here are a few tactful, slightly groveling phrases tailored for a typical “Reviewer 2” situation—showing humility, deference, and a desire to improve, while still preserving academic dignity:

⸻

🙇‍♂️ Respectful and Deferential Phrases:
	1.	“We sincerely thank Reviewer 2 for the insightful and challenging comments, which have significantly improved the quality of the paper.”
	2.	“We deeply appreciate Reviewer 2’s thorough reading and valuable suggestions, which pointed out important weaknesses that we have now addressed.”
	3.	“We are grateful to Reviewer 2 for identifying critical issues that we had previously overlooked.”
	4.	“We thank Reviewer 2 for holding our work to a high standard. We have taken the comments very seriously and made substantial revisions accordingly.”
	5.	“Reviewer 2’s detailed feedback prompted us to rethink and clarify several key aspects of our approach.”
	6.	“We humbly acknowledge Reviewer 2’s concerns regarding [X] and have revised the manuscript to better address these points.”
andreaszeller.bsky.social
How can you teach a machine how a program behaves? In our newest ACM TOSEM paper, we train machine learners from input/output pairs of thousands of program runs, producing models that _predict inputs for given desired outputs_. This got us a 150k€ ERC grant. Read the paper at doi.org/10.1145/3748...
Learning Program Behavioral Models from Synthesized Input-Output
Pairs
TURAL MAMMADOV∗
, CISPA Helmholtz Center for Information Security, Germany
DIETRICH KLAKOW, Saarland University, Germany
ALEXANDER KOLLER, Saarland University, Germany
ANDREAS ZELLER, CISPA Helmholtz Center for Information Security, Germany

We introduce Modelizer—a novel framework that, given a black-box program, learns a model from its input/output behavior
using neural machine translation algorithms. The resulting model mocks the original program: Given an input, the model
predicts the output that would have been produced by the program. However, the model is also reversible—that is, the model
can predict the input that would have produced a given output. Finally, the model is differentiable and can be efficiently
restricted to predict only a certain aspect of the program behavior. Modelizer uses grammars to synthesize and inputs and
unsupervised tokenizers to decompose the resulting outputs, allowing it to learn sequence-to-sequence associations between
token streams. Other than input grammars, Modelizer only requires the ability to execute the program. The resulting models
are small, requiring fewer than 6.3 million parameters for languages such as Markdown or HTML; and they are accurate,
achieving up to 95.4% accuracy and a BLEU score of 0.98 with standard error 0.04 in mocking real-world applications. As
it learns from and predicts executions rather than code, Modelizer departs from the LLM-centric research trend, opening
new opportunities for program-specific models that are fully tuned towards individual programs. Indeed, we foresee several
applications of these models, especially as the output of the program can be any aspect of program behavior. Beyond mocking
and predicting program behavior, the models can also synthesize inputs that are likely to produce a particular behavior, such
as failures or coverage, thus assisting in program understanding and maintenance.
Reposted by Andreas Zeller
cispa.de
🛠️ Debugging with AI?
LLMs like ChatGPT struggle with debugging—but there’s still potential. CISPA’s @andreaszeller.bsky.social explains how AI can help and what’s next in the DLF podcast Computer und Kommunikation. 🎧
Listen here: www.deutschlandfunk.de/fehlersuche-...
#AI #Debugging #Cybersecurity
andreaszeller.bsky.social
A citation injection attack? Here comes my way to h-index greatness :-)
andreaszeller.bsky.social
I got an ERC Proof of Concept grant from @erc.europa.eu! In the PROSA project, we train _program-specific models_ from myriads of synthesized executions, obtaining models that can predict how the software behaves and how we can trigger specific behavior: cispa.de/en/erc-proof... #ERCPoC
CISPA researcher Andreas Zeller awarded ERC Proof of Concept Grant for project on AI-powered software maintenance
To transfer his ERC Advanced Grant “S3 – Semantics of Software Systems” into practice, Zeller is receiving an additional €150,000.
cispa.de
andreaszeller.bsky.social
Since its release two weeks ago, our #Fandango test generator has been downloaded and installed more than 25,000 times! (Hint: It's "pip install fandango-fuzzer") Check it out here: fandango-fuzzer.github.io
Fuzzing with Fandango — Fuzzing with Fandango
fandango-fuzzer.github.io
andreaszeller.bsky.social
Oh, and a 2020 paper of mine, "Debugging inputs" apparently has been cited 11,457 times: scholar.google.com/scholar?q=%2... Title says it all :-)
andreaszeller.bsky.social
How reliable are citation metrics? According to Google Scholar, our 2025 paper "A Retrospective on Mining Version Histories to Guide Software Changes" (ieeexplore.ieee.org/abstract/doc...) has had its first citation in 1974 – 50 years BEFORE publication: scholar.google.com/scholar?oi=b...
andreaszeller.bsky.social
Kudos for the great video! The Attenborough-style narrator is indeed me - no AI or other audio post-processing involved. I think I’ll go and expand my voice imitation skills :-)
andreaszeller.bsky.social
Since many of you asked: One of the wonders of @cispa.de is that it has professionals for everything - be it photography, slide design, pitch talk training, empirical study design, proposal writing - and, of course, producing fun project videos :-)
cispa.de
FANDANGO, a new open-source fuzzing tool, uses an evolutionary algorithm to generate myriads of high-quality test inputs that satisfy defined constraints. Developed by a team of CISPA-researchers led by @andreaszeller.bsky.social. Now on GitHub! Learn more here cispa.de/en/fandango-...
andreaszeller.bsky.social
Since many of you asked: One of the wonders of @cispa.de is that it has professionals for everything - be it photography, slide design, pitch talk training, empirical study design, proposal writing, and more. This video was produced by our fabulous in-house PR team, and clearly made with love :-)
cispa.de
FANDANGO, a new open-source fuzzing tool, uses an evolutionary algorithm to generate myriads of high-quality test inputs that satisfy defined constraints. Developed by a team of CISPA-researchers led by @andreaszeller.bsky.social. Now on GitHub! Learn more here cispa.de/en/fandango-...
andreaszeller.bsky.social
Introducing #Fandango 💃 — a powerful bio-inspired test generator designed to supercharge your software testing. Fandango gives you unprecedented control over test inputs, integrating grammars, constraints, and Python. Now with protocol testing!

➡️ Check out Fandango at fandango-fuzzer.github.io
andreaszeller.bsky.social
My #Fandango team at #FSE2025 / #ISSTA2025: Alexi Turcotte, Marius Smytzek, me, Pepe Zamudio, and Laura Plein. What is #Fandango? Watch this space on Thursday for our big 1.0 release announcement and/or attend Pepe‘s presentation on Friday 16:00!
<exchange> ::= <client:request> <server:response>
<request>  ::= 0x1 <length> <payload> <padding>
<response> ::= 0x2 <length> <payload> <padding>
<length>   ::= <uint16>
<payload>  ::= <byte>*
<padding>  ::= <byte>*

where len(<payload>) == uint16(<length>)
where <response>.<payload> == <request>.<payload>
andreaszeller.bsky.social
For the first time in 25 years, I put a sticker on my laptop
Fandango Logo
Reposted by Andreas Zeller
andreaszeller.bsky.social
And here's my interview on fuzzing with video and English subtitles: www.youtube.com/watch?v=u84x...