- author = "Jose Antonio {Zamudio Amaya} and Marius Smytzek and Andreas Zeller",
- title = "Search-Based Generation of Complex Inputs with {FANDANGO}",
- booktitle = "Search-Based Software Engineering 2026 - Hot-off-the-Press",
- year = "2026",
- editor = "Gregory Gay and Mohamed Aymen Saied",
- volume = "16699",
- series = "Lecture Notes in Computer Science",
- pages = "151--154",
- address = "Montreal",
- month = "6 " # jul,
- publisher = "Springer Nature",
- keywords = "genetic algorithms, genetic programming, SBSE, Language-based testing, search-based software testing fuzzing, test generation, SBST, SBFT, evolutionary algorithms",
- isbn13 = "978-3-032-30698-2",
-
URL = "
https://conf.researchr.org/program/ssbse-2026/program-ssbse-2026/",
-
DOI = "
10.1007/978-3-032-30699-9_15",
-
code_url = "https://github.com/fandango-fuzzer/fandango",
- size = "4 pages",
- abstract = "Generating complex, semantically valid test inputs remains a central challenge in search-based software testing. FANDANGO addresses this challenge by combining context-free grammars with fully specified Python constraints and using evolutionary search to satisfy them. Compared with the symbolic state of the art, FANDANGO achieves speedups of up to three orders of magnitude while maintaining 100 percent input validity and equal or higher grammar coverage. FANDANGO is under active development: recent work explores whitebox fuzzing and stateful fuzzing through interaction grammars that model multi-turn protocol behavior. With more than 100000 PyPI downloads, adoption in industry (including Volkswagen and Bosch), and coverage in the science press, FANDANGO has established itself as a practical input generator.",
-
notes = "See \cite{DBLP:journals/pacmse/AmayaSZ25}
CISPA",
