Causal Interpretability for DL4SE [Final Defense]

Discover innovative approaches to enhance the interpretability of deep learning in software engineering. Explore groundbreaking insights that promise to transform the field and foster a deeper understanding of complex models. Join us on this exciting journey!

Causal Interpretability [Pre-Defense]

Discover innovative approaches to enhance the interpretability of deep learning in software engineering. Explore groundbreaking insights that promise to transform the field and foster a deeper understanding of complex models. Join us on this exciting journey!

A.I 2023

Code Rationales

Design Patterns

Statistical Inference for Software Retrieval

CISCO Traceability Research

COMET's Tutorial

journalclubAI

Learning to Identify Security-Related Issues Using Convolutional Neural Networks

Deep Software Engineering for Artificial Code Generation

Towards self-maintenance

Towards Reconstructing Software Evolution Trends and Artifacts Relationships with Statistical Models

Simulation of Repositories

Evolving Discipline

Comet Futures and Actor Model

Quantum

Convergence_Refactoring

GECCO 2018

[compilers] Facade Presentation

Deep Code Search

Neural Network-based Graph Embeddings for Cross-Platform Binary Code Similarity Detection

First Software Engineering Presentation

Causal Traceability

Introduction to Causal Inference

Reverse Engineering from Machine to C code

Proposal & Milestone