Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Noller, Yannic"'
Despite the impressive performance of Large Language Models (LLMs) in software development activities, recent studies show the concern of introducing vulnerabilities into software codebase by AI programming assistants (e.g., Copilot, CodeWhisperer).
Externí odkaz:
http://arxiv.org/abs/2405.03927
The increasing number of computer science students pushes lecturers and tutors of first-year programming courses to their limits to provide high-quality feedback to the students. Existing systems that handle automated grading primarily focus on the a
Externí odkaz:
http://arxiv.org/abs/2310.05472
Automated program repair is an emerging technology which consists of a suite of techniques to automatically fix bugs or vulnerabilities in programs. In this paper, we present a comprehensive survey of the state of the art in program repair. We first
Externí odkaz:
http://arxiv.org/abs/2211.12787
Publikováno v:
Information and Software Technology, Volume 144, April 2022, 106809
Context: Identifying potential vulnerable code is important to improve the security of our software systems. However, the manual detection of software vulnerabilities requires expert knowledge and is time-consuming, and must be supported by automated
Externí odkaz:
http://arxiv.org/abs/2201.08441
Autor:
Noller, Yannic
Differentielles Testen ist ein wichtiger Bestandteil der Qualitätssicherung von Software, mit dem Ziel Testeingaben zu generieren, die Unterschiede im Verhalten der Software deutlich machen. Solche Unterschiede können zwischen zwei Ausführungspfad
Externí odkaz:
http://edoc.hu-berlin.de/18452/22727
Automated program repair is an emerging technology that seeks to automatically rectify bugs and vulnerabilities using learning, search, and semantic analysis. Trust in automatically generated patches is necessary for achieving greater adoption of pro
Externí odkaz:
http://arxiv.org/abs/2108.13064
Autor:
Noller, Yannic, Tizpaz-Niari, Saeid
Publikováno v:
ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'21), July 11-17, 2021, Virtual, Denmark
Side channels pose a significant threat to the confidentiality of software systems. Such vulnerabilities are challenging to detect and evaluate because they arise from non-functional properties of software such as execution times and require reasonin
Externí odkaz:
http://arxiv.org/abs/2106.03346
We present NNrepair, a constraint-based technique for repairing neural network classifiers. The technique aims to fix the logic of the network at an intermediate layer or at the last layer. NNrepair first uses fault localization to find potentially f
Externí odkaz:
http://arxiv.org/abs/2103.12535
This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analy
Externí odkaz:
http://arxiv.org/abs/2103.00124
A fuzzer provides randomly generated inputs to a targeted software to expose erroneous behavior. To efficiently detect defects, generated inputs should conform to the structure of the input format and thus, grammars can be used to generate syntactica
Externí odkaz:
http://arxiv.org/abs/2008.01150