Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Sooyoung Cha"'
Autor:
Markus J Sommer, Sooyoung Cha, Ales Varabyou, Natalia Rincon, Sukhwan Park, Ilia Minkin, Mihaela Pertea, Martin Steinegger, Steven L Salzberg
Publikováno v:
eLife, Vol 11 (2022)
Recently developed methods to predict three-dimensional protein structure with high accuracy have opened new avenues for genome and proteome research. We explore a new hypothesis in genome annotation, namely whether computationally predicted structur
Externí odkaz:
https://doaj.org/article/c835503456a64f098e302b3d3173af99
Publikováno v:
IEEE Transactions on Software Engineering. 48:3640-3663
We present a technique to automatically generate search heuristics for dynamic symbolic execution. A key challenge in dynamic symbolic execution is how to effectively explore the program's execution paths to achieve high code coverage in a limited ti
Autor:
Markus J Sommer, Sooyoung Cha, Ales Varabyou, Natalia Rincon, Sukhwan Park, Ilia Minkin, Mihaela Pertea, Martin Steinegger, Steven L Salzberg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4df0175a777683d30b6dee47c8cad30c
https://doi.org/10.7554/elife.82556.sa2
https://doi.org/10.7554/elife.82556.sa2
Autor:
Markus J. Sommer, Sooyoung Cha, Ales Varabyou, Natalia Rincon, Sukhwan Park, Ilia Minkin, Mihaela Pertea, Martin Steinegger, Steven L. Salzberg
We explore a new hypothesis in genome annotation, namely whether computationally predicted protein structures can help to identify which of multiple possible gene isoforms represents a functional protein product. Guided by structure predictions, we e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a601ccad6f91f2013a4b623ec813d737
https://doi.org/10.1101/2022.06.08.495354
https://doi.org/10.1101/2022.06.08.495354
SymTuner SymTuneris a tool that automaticallytunes external parameters of symbolic executionvia online learning. This tool is implemented on the top ofKLEE, a publicly available symbolic execution tool for testing C programs. For more technical detai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::138dc644493cb9dd50a021bb594bd46c
Autor:
Sommer, Markus J., Sooyoung Cha, Varabyou, Ales, Rincon, Natalia, Park, Sukhwan, Minkin, Ilia, Pertea, Mihaela, Steinegger, Martin, Salzberg, Steven L.
Publikováno v:
eLife; 1/4/2023, p1-21, 21p
Autor:
Sommer, Markus J., Sooyoung Cha, Ales Varabyou, Rincon, Natalia, Sukhwan Park, Ilia Minkin, Pertea, Mihaela, Steinegger, Martin, Salzberg, Steven L.
Publikováno v:
eLife; 12/15/2022, p1-21, 21p
Autor:
Hakjoo Oh, Sooyoung Cha
Publikováno v:
ESEC/SIGSOFT FSE
We present HOMI, a new technique to enhance symbolic execution by maintaining only a small number of promising states. In practice, symbolic execution typically maintains as many states as possible in a fear of losing important states. In this paper,
Publikováno v:
ISSTA
We present Adapt, a new white-box testing technique for deep neural networks. As deep neural networks are increasingly used in safety-first applications, testing their behavior systematically has become a critical problem. Accordingly, various testin
Publikováno v:
Information and Software Technology. 104:1-13
Context: Recently data-driven program analysis has emerged as a promising approach for building cost-effective static analyzers. The ideal static analyzer should apply accurate but costly techniques only when they benefit. However, designing such a s