SEMERU-Lab
This group holds the code for research projects conducted by the SEMERU Lab at William & Mary
Williamsburg
Pinned Repositories
ACER
ACER is an AST-based Callgraph Generator Development Framework
CausalSE
Causal Interpretability for SE
dl4se
A Systematic Literature Review of Deep Learning in Software Engineering
ds4se
Data Science for Software Engineering (ds4se) is an academic initiative to perform exploratory and causal inference analysis on software engineering artifacts and metadata. Data Management, Analysis, and Benchmarking for DL and Traceability.
galeras-benchmark
Benchmarking Causl Study to Interpret Large Language Models for Source Code
galeras-dataset
Curated datasets extractor and API
hephaestus
SecureReqNet
We present a novel approach, called SecureReqNet, for automatically identifying whether issues in bug or issue tracking systems describe security related content that should be given careful attention. Our approach consists of a two-phase deep learning architecture that operates purely on the natural language descriptions of issues. The first phase of our approach learns high dimensional sentence embeddings from hundreds of thousands of descriptions extracted from software vulnerabilities listed in the CVE database and issue descriptions extracted from open source projects using an unsupervised learning process. The second phase then utilizes this semantic ontology of embeddings to train a deep convolutional neural network capable of predicting whether a given issue contains security- related information.
SemeruGuidelines
Semeru Data and Machine Guidelines
SyntaxEval
SEMERU-Lab's Repositories
WM-SEMERU/ACER
ACER is an AST-based Callgraph Generator Development Framework
WM-SEMERU/dl4se
A Systematic Literature Review of Deep Learning in Software Engineering
WM-SEMERU/SecureReqNet
We present a novel approach, called SecureReqNet, for automatically identifying whether issues in bug or issue tracking systems describe security related content that should be given careful attention. Our approach consists of a two-phase deep learning architecture that operates purely on the natural language descriptions of issues. The first phase of our approach learns high dimensional sentence embeddings from hundreds of thousands of descriptions extracted from software vulnerabilities listed in the CVE database and issue descriptions extracted from open source projects using an unsupervised learning process. The second phase then utilizes this semantic ontology of embeddings to train a deep convolutional neural network capable of predicting whether a given issue contains security- related information.
WM-SEMERU/ds4se
Data Science for Software Engineering (ds4se) is an academic initiative to perform exploratory and causal inference analysis on software engineering artifacts and metadata. Data Management, Analysis, and Benchmarking for DL and Traceability.
WM-SEMERU/hephaestus
WM-SEMERU/galeras-benchmark
Benchmarking Causl Study to Interpret Large Language Models for Source Code
WM-SEMERU/CausalSE
Causal Interpretability for SE
WM-SEMERU/galeras-dataset
Curated datasets extractor and API
WM-SEMERU/SemeruGuidelines
Semeru Data and Machine Guidelines
WM-SEMERU/SyntaxEval
WM-SEMERU/CodeSyntaxConcept
Describing and Evaluating Semantic Capabilities for SOTA Code Models.
WM-SEMERU/mlproj_template_deprecated
Machine learning project template based on the awesome nbdev_template
WM-SEMERU/big_clone_benchmark_setup
Repo for automatically setting up environment for the Big Clone Benchmark
WM-SEMERU/code
This project contains code specific processing utilities, mostly focused for helping software engineering research with machine learning models for code data.
WM-SEMERU/csci-435_what_if_tool
Project #3: What-if-tool Code. A Visual Tool for Understanding Machine Learning Models for Software Engineering
WM-SEMERU/docker-container-example
WM-SEMERU/gpu-jupyter
Leverage the flexibility of Jupyterlab through the power of your NVIDIA GPU to run your code from Tensorflow and Pytorch in collaborative notebooks on the GPU.
WM-SEMERU/traceXplainer
A Library for Software Artifact Vectorization, Distance Computation, and Statistical Analysis on vectors.
WM-SEMERU/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
WM-SEMERU/WM-Thesis-Template
This repo holds the latest version of the LaTeX template for writing Theses and Dissertations at the College of William & Mary