Security & Software Engineering Research Lab at University of Notre Dame
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Repository for "SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques" published in MSR4P&S'22.
"SALLM: Security Assessment of Generated Code" accepted at ASYDE workshop co-located with ASE'24.
Source code for "An Empirical Study of Code Smells in Transformer-based Code Generation Techniques".
Source code for the paper titled 'BERT-Based GitHub Issue Report Classification'.
This repository contains the source code for Seneca, a taint-based call graph construction for Java programs.
Source code for the accepted paper in ICSE-NIER'24: Re(gEx|DoS)Eval: Evaluating Generated Regular Expressions and their Proneness to DoS Attacks.
Repositories
19Repository for "SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques" published in MSR4P&S'22.
No description provided.
"SALLM: Security Assessment of Generated Code" accepted at ASYDE workshop co-located with ASE'24.
From "A comprehensive evaluation of SZZ Variants through a developer-informed oracle" (pdf open-access at https://doi.org/10.1016/j.jss.2023.111729)
No description provided.
Source code for the accepted paper in ICSE-NIER'24: Re(gEx|DoS)Eval: Evaluating Generated Regular Expressions and their Proneness to DoS Attacks.
No description provided.
An empirical study of model serialization format evolution.
This repository contains the source code for Seneca, a taint-based call graph construction for Java programs.
Source code for the paper titled 'BERT-Based GitHub Issue Report Classification'.
Source code for "An Empirical Study of Code Smells in Transformer-based Code Generation Techniques".
A framework for Filtering, Ranking, Repairing generated code to have quality code. Accepted at SCAM 2024.
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Repository to our survey on source code representations for security-related tasks.
Accepted at 56th SIGCSE TS
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An empirical investigation of quality in code benchmark datasets; Accepted at SCAM 2024.
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