Enhancing and Hardening Neural Code Model

PhD Thesis Proposal Defence


Title: "Enhancing and Hardening Neural Code Model"

by

Mr. Zongjie LI


Abstract:

With the rapid advancement of deep learning technologies, neural code models 
have achieved remarkable success, facilitating significant breakthroughs 
across various code-related applications. Leveraging powerful computational 
resources and massive training data, these models demonstrate sophisticated 
capabilities in understanding, analyzing, and generating diverse programming 
code. Unlike models primarily designed for natural language tasks, code 
models are typically engineered for integration into various productivity 
scenarios and practical development workflows. Consequently, developing 
neural code models with high accuracy, reliability, and freedom from 
potential intellectual property risks has become imperative.

This thesis proposal focuses on designing and developing neural code models 
through three key aspects: 1) enhancing model performance through data 
augmentation and architectural improvements, 2) refining output consistency 
through code structure and semantic analysis, and 3) incorporating 
verifiable watermarks to protect intellectual property. In our first 
contribution, we present a framework that leverages compiler-generated 
Intermediate Representation (IR) code for data augmentation, enabling 
improved embeddings that support various downstream code applications. To 
further enhance code generation capabilities, our second work introduces 
CCTEST, a system that inserts context-free code snippets to detect and 
rectify inconsistencies. In our third work, we exploit programming language 
semantics and token distribution characteristics to embed verifiable 
watermarks in model outputs, thereby enhancing model security and 
intellectual property protection.


Date:                   Thursday, 27 March 2025

Time:                   3:00pm - 5:00pm

Venue:                  Room 2408
                        Lifts 17/18

Committee Members:      Dr. Shuai Wang (Supervisor)
                        Dr. Wei Wang (Chairperson)
                        Dr. Hao Chen
                        Dr. Dongdong She