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ACER
ACER is an AST-based Callgraph Generator Development Frameworkdl4se
A Systematic Literature Review of Deep Learning in Software EngineeringSecureReqNet
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.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.hephaestus
CSci435-Fall21-CallGraph
galeras-benchmark
Benchmarking Causl Study to Interpret Large Language Models for Source CodeSyntaxEval
call_graph
SemeruGuidelines
Semeru Data and Machine GuidelinesCausalSE
Causal Interpretability for SEgaleras-dataset
Curated datasets extractor and APICodeSyntaxConcept
Describing and Evaluating Semantic Capabilities for SOTA Code Models.Love Open Source and this site? Check out how you can help us