WM-SEMERU/ACER

Stars
24
Rank 982,717 (Top 20 %)
Language
Python
License
MIT License
Created about 1 year ago
Updated 4 months ago

WM-SEMERU/ACER

WM-SEMERU

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ACER is an AST-based Callgraph Generator Development Framework

dl4se

A Systematic Literature Review of Deep Learning in Software Engineering

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.

Jupyter Notebook

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.

Jupyter Notebook

hephaestus

Jupyter Notebook

CSci435-Fall21-CallGraph

galeras-benchmark

Benchmarking Causl Study to Interpret Large Language Models for Source Code

Jupyter Notebook

SyntaxEval

Jupyter Notebook

call_graph

Jupyter Notebook

SemeruGuidelines

Semeru Data and Machine Guidelines

CausalSE

Causal Interpretability for SE

Jupyter Notebook

galeras-dataset

Curated datasets extractor and API

Jupyter Notebook

CodeSyntaxConcept

Describing and Evaluating Semantic Capabilities for SOTA Code Models.

Jupyter Notebook

mlproj_template_deprecated

Machine learning project template based on the awesome nbdev_template