A Dual-Modal Framework Utilizing Visual Prompts for Enhanced Patch Analysis
Keywords: Code Generation, Domain Adaptation
TL;DR: This paper presents a groundbreaking approach to patch representation learning through the Image-Guided Code Patch Framework (IGCP)
Abstract: Patch representation learning has emerged as a crucial innovation in software development, leveraging machine learning techniques to advance software generation workflows. This approach has led to significant enhancements across various applications involving code alterations. However, existing methods often exhibit a tendency towards specialization, excelling predominantly in either predictive tasks such as security patch classification or in generative tasks like the automated creation of patch descriptions. This paper presents a groundbreaking approach to patch representation learning through the Image-Guided Code Patch Framework (IGCP), a novel architecture that bridges the gap between code analysis and image processing domains. We introduce a rigorous mathematical foundation for IGCP, leveraging measure theory, functional analysis, and information geometry to formalize the domain adaptation process in patch representation learning. The optimization dynamics of IGCP are rigorously analyzed through the lens of Stochastic Gradient Langevin Dynamics, providing convergence guarantees in both convex and non-convex loss landscapes. Empirical evaluations demonstrate that IGCP not only achieves state-of-the-art performance in patch description generation but also exhibits remarkable domain generalization capabilities.
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 13050
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