Zobrazeno 1 - 10
of 316
pro vyhledávání: '"Isil Dillig"'
Autor:
Isil Dillig, Serdar Tasiran
This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together w
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 8 (2021)
Externí odkaz:
https://doaj.org/article/1da3065e722e424292a764be341a1a5e
Autor:
Hayley LeBlanc, Shankara Pailoor, Om Saran K R E, Isil Dillig, James Bornholt, Vijay Chidambaram
Publikováno v:
Proceedings of the Eighteenth European Conference on Computer Systems.
Publikováno v:
Proceedings of the ACM on Programming Languages. 5:1-26
Many data processing systems allow SQL queries that call user-defined functions (UDFs) written in conventional programming languages. While such SQL extensions provide convenience and flexibility to users, queries involving UDFs are not as efficient
Publikováno v:
Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering.
Publikováno v:
Proceedings of the ACM on Programming Languages. 5:1-30
Several real-world libraries (e.g., reentrant locks, GUI frameworks, serialization libraries) require their clients to use the provided API in a manner that conforms to a context-free specification. Motivated by this observation, this paper describes
Publikováno v:
Proceedings of the ACM on Programming Languages. 4:1-26
System call whitelisting is a powerful sandboxing approach that can significantly reduce the capabilities of an attacker if an application is compromised. Given a policy that specifies which system calls can be invoked with what arguments, a sandboxi
Publikováno v:
ESEC/SIGSOFT FSE
A good graphical user interface (GUI) is crucial for an application's usability, so vendors and regulatory agencies increasingly place restrictions on how GUI elements should appear to and interact with users. Motivated by this concern, this paper pr
Publikováno v:
ESEC/SIGSOFT FSE
While machine learning (ML) models play an increasingly prevalent role in many software engineering tasks, their prediction accuracy is often problematic. When these models do mispredict, it can be very difficult to isolate the cause. In this paper,