Learning Compiler Fuzzing Mutators from Historical Bugs
This program is tentative and subject to change.
Bugs in compilers, which are critical infrastructure today, can have outsized negative impacts. Mutational fuzzers aid compiler bug detection by systematically mutating compiler inputs, i.e., programs. Their effectiveness depends on the quality of the mutators used. Yet, no prior work used compiler bug histories as a source of mutators.
We propose IssueMut, the first approach for extracting compiler fuzzing mutators from bug histories. Our insight is that bug reports contain hints about program elements that induced compiler bugs; they can guide fuzzers towards similar bugs. IssueMut uses an automated method to mine mutators from bug reports and retrofit such mutators into existing mutational compiler fuzzers.
Using IssueMut, we mine 587 mutators from 1760 GCC and LLVM bug reports. Then, we run IssueMut on these compilers, with all their test inputs as seed corpora. We find that “bug history” mutators are effective: they find new bugs that a state-of-the-art mutational compiler fuzzer misses-25 in GCC and 27 in LLVM. Out of the 65 bugs we reported, 60 were confirmed or fixed, validating our idea that bug histories have rich information that compiler fuzzers should leverage.
This program is tentative and subject to change.
Tue 14 AprDisplayed time zone: Brasilia, Distrito Federal, Brazil change
14:00 - 15:30 | |||
14:00 10mTalk | How are MLOps Frameworks Used in Open Source Projects? An Empirical Characterization Technical Papers Fiorella Zampetti University of Sannio, Italy, Federico Stocchetti University of Sannio, Italy, Federica Razzano University of Sannio, Italy, Damian Andrew Tamburri University of Sannio - JADS/NXP Semiconductors, Massimiliano Di Penta University of Sannio, Italy Pre-print | ||
14:10 10mTalk | Do We Agree on What an “Audit” Is? Toward Standardized Smart Contract Audit Reporting Technical Papers Ilham Qasse Reykjavik University, Mohammad Hamdaqa Polytechnique Montreal, Gísli Hjálmtýsson Reykjavik University | ||
14:20 10mTalk | AFGNN: API Misuse Detection using Graph Neural Networks and Clustering Technical Papers Ponnampalam Pirapuraj IIT Hyderabad, Tamal Mondal Oracle, Sharanya Gupta Yokogawa Digital, Akash Lal Microsoft Research, Somak Aditya IIT Kharagpur, Jyothi Vedurada IIT Hyderabad | ||
14:30 10mTalk | An Empirical Analysis of Cross-OS Portability Issues in Python Projects Technical Papers Denini Silva Federal University of Pernambuco, MohamadAli Farahat North Carolina State University, Marcelo d'Amorim North Carolina State University Pre-print | ||
14:40 10mTalk | Learning Compiler Fuzzing Mutators from Historical Bugs Technical Papers Lingjun Liu North Carolina State University, Feiran Qin North Carolina State University, Owolabi Legunsen Cornell University, Marcelo d'Amorim North Carolina State University | ||
14:50 40mMeeting | Mining Challenge Finalists MSR Program | ||