@UCLA working on dynamic and statistical program analysis.
"Constraining Fuzzing without Paying Too Much" by Miryung Kim
youtu.be/L90MBb6NLBE
"Are you sure you belong in academia?" by Will Wilson
youtu.be/qQGuQ_4V6WI
// @mboehme.bsky.social, László Szekeres, @rohan.padhye.org, @ruijiemeng.bsky.social
* From academia: Miryung Kim (Prof @ UCLA)
* From industry: Will Wilson (CEO and Co-Founder of @AntithesisHQ.bsky.social).
Stay tuned for recordings!
"Constraining Fuzzing without Paying Too Much" by Miryung Kim
youtu.be/L90MBb6NLBE
"Are you sure you belong in academia?" by Will Wilson
youtu.be/qQGuQ_4V6WI
// @mboehme.bsky.social, László Szekeres, @rohan.padhye.org, @ruijiemeng.bsky.social
We present DiffMin, a technique that makes crashing inputs closer to passing ones while keeping the crash. This helps localize and diagnose crashes more precisely. Just accepted to FSE’25 Posters 🎉
📄 arxiv.org/abs/2505.02305
#FSE25 #fuzzing #debugging
We present DiffMin, a technique that makes crashing inputs closer to passing ones while keeping the crash. This helps localize and diagnose crashes more precisely. Just accepted to FSE’25 Posters 🎉
📄 arxiv.org/abs/2505.02305
#FSE25 #fuzzing #debugging
* easier navigation
* dark mode
* search
* mobile layout
and lots of other features. Check it out at www.fuzzingbook.org/beta/html/in...
Should we continue with this new look? Comments are welcome!
* easier navigation
* dark mode
* search
* mobile layout
and lots of other features. Check it out at www.fuzzingbook.org/beta/html/in...
Should we continue with this new look? Comments are welcome!
📝https://mpi-softsec.github.io/papers/ISSTA25-topscore.pdf
🧑💻https://github.com/niklasrisse/TopScoreWrongExam
// @nrisse.bsky.social @fuzzing.bsky.social
📝https://mpi-softsec.github.io/papers/ISSTA25-topscore.pdf
🧑💻https://github.com/niklasrisse/TopScoreWrongExam
// @nrisse.bsky.social @fuzzing.bsky.social
I’ll share how statistical magic helps us quantify residual risk and make testing results more accountable and reliable✨Thanks for the invite! 🙏
Join my tutorial @ SBST (co-located w/ ICSE)
🖥️ sbft25.github.io
📅 28.04. 14:30 GMT-4 (live on Twitch too)
I’ll share how statistical magic helps us quantify residual risk and make testing results more accountable and reliable✨Thanks for the invite! 🙏
Join my tutorial @ SBST (co-located w/ ICSE)
🖥️ sbft25.github.io
📅 28.04. 14:30 GMT-4 (live on Twitch too)
I’ll share a public version soon!
It shines light on a beautiful statistical riddle! Suppose, you train a classifier. How representative was the training data? What is the proportion of the *unknown* population of classifier inputs with classes *not* in the training data?
Led by Seongmin.
I’ll share a public version soon!