paper-with-me

홈 › Papers

Software Language Comprehension using a Program-Derived Semantics Graph

2020-04-02 · NeurIPS Workshop CAP 2020 12 · Roshni G. Iyer, Yizhou Sun, Wei Wang, Justin Gottschlich

Traditional code transformation structures, such as abstract syntax trees (ASTs), conteXtual flow graphs (XFGs), and more generally, compiler intermediate representations (IRs), may have limitations in extracting higher-order semantics from code. While work has already begun on higher-order semantics lifting (e.g., Aroma's simplified parse tree (SPT), verified lifting's lambda calculi, and Halide's intentional domain specific language (DSL)), research in this area is still immature. To continue to advance this research, we present the program-derived semantics graph, a new graphical structure to capture semantics of code. The PSG is designed to provide a single structure for capturing program semantics at multiple levels of abstraction. The PSG may be in a class of emerging structural representations that cannot be built from a traditional set of predefined rules and instead must be learned. In this paper, we describe the PSG and its fundamental structural differences compared to state-of-the-art structures. Although our exploration into the PSG is in its infancy, our early results and architectural analysis indicate it is a promising new research direction to automatically extract program semantics.

📄 PDF Abstract BibTeX arXiv:2004.00768

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code

2025-04-24 · Michele Carissimi, Martina Saletta, Claudio Ferretti

Understanding source code is a topic of great interest in the software engineering community, since it can help programmers in various tasks such as software maintenance and reuse. Recent advances in large language model…

Code SearchLanguage ModelingLanguage ModellingLarge Language Model

How could Neural Networks understand Programs?

2021-05-10 · Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke 외

Semantic understanding of programs is a fundamental problem for programming language processing (PLP). Recent works that learn representations of code based on pre-training techniques in NLP have pushed the frontiers in …

valid

Probabilistic Software Modeling: A Data-driven Paradigm for Software Analysis

2019-12-17 · Hannes Thaller, Lukas Linsbauer, Rudolf Ramler, Alexander Egyed

Software systems are complex, and behavioral comprehension with the increasing amount of AI components challenges traditional testing and maintenance strategies.The lack of tools and methodologies for behavioral software…

Anomaly Detection

Smart Knowledge Transfer using Google-like Search

2023-08-12 · Srijoni Majumdar, Partha Pratim Das

To address the issue of rising software maintenance cost due to program comprehension challenges, we propose SMARTKT (Smart Knowledge Transfer), a search framework, which extracts and integrates knowledge related to vari…

Transfer Learning

A Joint Learning Model with Variational Interaction for Multilingual Program Translation

2024-08-25 · Yali Du, Hui Sun, Ming Li

Programs implemented in various programming languages form the foundation of software applications. To alleviate the burden of program migration and facilitate the development of software systems, automated program trans…

DisentanglementTranslationVariational Inference