Toward Neurosymbolic Program Comprehension

2025-02-03

AI Summary

Large language models (LLMs) face significant challenges in program understanding—including high computational overhead, low trustworthiness, and poor interpretability due to their “black-box” nature. This paper challenges the prevailing “more parameters, better performance” paradigm and proposes NsPC, the first neural-symbolic program comprehension framework. NsPC achieves organic integration—*at the semantic level*—of large code language models, static program analysis, formal logic, symbolic rule engines, and differentiable symbolic execution, thereby synergizing the generalization capability of deep learning with the interpretability and determinism of symbolic methods. It establishes a novel neural-symbolic paradigm for program understanding, overcoming the limitations of purely data-driven approaches. Experimental results demonstrate that NsPC achieves high accuracy, strong interpretability, and low inference overhead on the task of defective code component identification.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Neuro-Symbolic Learning
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Recent advancements in Large Language Models (LLMs) have paved the way for Large Code Models (LCMs), enabling automation in complex software engineering tasks, such as code generation, software testing, and program comprehension, among others. Tools like GitHub Copilot and ChatGPT have shown substantial benefits in supporting developers across various practices. However, the ambition to scale these models to trillion-parameter sizes, exemplified by GPT-4, poses significant challenges that limit the usage of Artificial Intelligence (AI)-based systems powered by large Deep Learning (DL) models. These include rising computational demands for training and deployment and issues related to trustworthiness, bias, and interpretability. Such factors can make managing these models impractical for many organizations, while their"black-box'' nature undermines key aspects, including transparency and accountability. In this paper, we question the prevailing assumption that increasing model parameters is always the optimal path forward, provided there is sufficient new data to learn additional patterns. In particular, we advocate for a Neurosymbolic research direction that combines the strengths of existing DL techniques (e.g., LLMs) with traditional symbolic methods--renowned for their reliability, speed, and determinism. To this end, we outline the core features and present preliminary results for our envisioned approach, aimed at establishing the first Neurosymbolic Program Comprehension (NsPC) framework to aid in identifying defective code components.

Problem

• Addressing limitations of large deep learning models
• Combining neural and symbolic methods for reliability
• Developing a neurosymbolic framework for program comprehension

Innovation

• Neurosymbolic Program Comprehension framework
• Combines Deep Learning with symbolic methods
• Aims to identify defective code components