Hackers' Pub

Syntax	Description	Examples
`"` keyword `"`	Finds the string within quotes, including spaces. Case-insensitive. (Escape quotes inside with `\"`)	`"Hackers' Pub"`
`from:` handle	Finds content written by the specified user.	`from:hongminhee` `from:hongminhee@hollo.social`
`lang:` ISO 639-1	Finds content written in the specified language.	`lang:en`
`#` tag	Finds content with the specified tag. Case-insensitive.	`#HackersPub`
condition condition	Finds content that satisfies both conditions on either side of the space (logical AND).	`"Hackers' Pub" lang:en`
condition `OR` condition	Finds content that satisfies at least one of the conditions on either side of the OR operator (logical OR).	`#HackersPub OR "Hackers' Pub" lang:en`
`(` condition `)`	Combines the operators within the parentheses first.	`(#HackersPub OR "Hackers' Pub" OR "Hackers Pub") lang:en`

Andreas Zeller @AndreasZeller@mastodon.social

4/10/2025, 7:46:22 PM

Public

What do you get if you combine #grammars, #constraints, #evolutionary algorithms, and #Python in one? A mighty fuzzer! Check out our latest #FANDANGO work, to appear at #ISSTA2025:
https://publications.cispa.de/articles/standard/FANDANGO_Evolving_Language-Based_Testing/28769252?file=53591066
To try out Fandango yourself, check out its home page: https://fandango-fuzzer.github.io/

FANDANGO: Evolving Language-Based Testing
José Antonio Zamudio Amaya, Marius Smytzek, Andreas Zeller

Language-based fuzzers leverage formal input specifications (languages) to generate arbitrarily large and diverse sets of valid inputs for a program under test. Modern language-based test generators combine grammars and constraints to satisfy syntactic and semantic input constraints. ISLa, the leading input generator in that space, uses symbolic constraint solving to solve input constraints. Using solvers places ISLa among the most precise fuzzers but also makes it slow.

In this paper, we explore search-based testing as an alternative to symbolic constraint solving. We employ a genetic algorithm that iteratively generates candidate inputs from an input specification, evaluates them against defined constraints, evolving a population of inputs through syntactically valid mutations and retaining those with superior fitness until the semantic input constraints are met. This evolutionary procedure, analogous to natural genetic evolution, leads to progressively improved inputs that cover both semantics and syntax. This change boosts the efficiency of language-based testing: In our experiments, compared to ISLa, our search-based FANDANGO prototype is faster by one to three orders of magnitude without sacrificing precision...

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