Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables

• URL: http://arxiv.org/abs/2209.01566v4
• Date: Thu, 24 Aug 2023 08:16:38 GMT
• Title: Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables
• Authors: Jian Gu, Harald C. Gall
• Abstract summary: We build a taxonomy on code data, namely code taxonomy, leveraging the categorization of code information. We introduce a three-layer semantic pyramid (SP) to associate text data and code data. We propose a semantic pyramid framework (SPF) as the approach, focusing on software of high modularity and low complexity.
• Score: 4.844958528198992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep code generation is a topic of deep learning for software engineering (DL4SE), which adopts neural models to generate code for the intended functions. Since end-to-end neural methods lack domain knowledge and software hierarchy awareness, they tend to perform poorly w.r.t project-level tasks. To systematically explore the potential improvements of code generation, we let it participate in the whole top-down development from \emph{expressibles} to \emph{executables}, which is possible in limited scopes. In the process, it benefits from massive samples, features, and knowledge. As the foundation, we suggest building a taxonomy on code data, namely code taxonomy, leveraging the categorization of code information. Moreover, we introduce a three-layer semantic pyramid (SP) to associate text data and code data. It identifies the information of different abstraction levels, and thus introduces the domain knowledge on development and reveals the hierarchy of software. Furthermore, we propose a semantic pyramid framework (SPF) as the approach, focusing on software of high modularity and low complexity. SPF divides the code generation process into stages and reserves spots for potential interactions. In addition, we conceived preliminary applications in software development to confirm the neuro-symbolic framework.

Related papers

• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [59.32609948217718]
  We present CodeIP, a new watermarking technique for Large Language Models (LLMs)-based code generation. CodeIP enables the insertion of multi-bit information while preserving the semantics of the generated code.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit [63.82016263181941]
  Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora. Currently, there is already a thriving research community focusing on code intelligence.
  arXiv  Detail & Related papers  (2023-12-30T17:48:37Z)
• TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation [9.477734501499274]
  We present TransformCode, a novel framework that learns code embeddings in a contrastive learning manner. Our framework is encoder-agnostic and language-agnostic, which means that it can leverage any encoder model and handle any programming language.
  arXiv  Detail & Related papers  (2023-11-10T09:05:23Z)
• When Do Program-of-Thoughts Work for Reasoning? [51.2699797837818]
  We propose complexity-impacted reasoning score (CIRS) to measure correlation between code and reasoning abilities. Specifically, we use the abstract syntax tree to encode the structural information and calculate logical complexity. Code will be integrated into the EasyInstruct framework at https://github.com/zjunlp/EasyInstruct.
  arXiv  Detail & Related papers  (2023-08-29T17:22:39Z)
• Adding Context to Source Code Representations for Deep Learning [13.676416860721877]
  We argue that it is beneficial for deep learning models to have access to additional contextual information about the code being analysed. We present preliminary evidence that encoding context from the call hierarchy along with information from the code itself can improve the performance of a state-of-the-art deep learning model.
  arXiv  Detail & Related papers  (2022-07-30T12:47:32Z)
• A Survey of Deep Learning Models for Structural Code Understanding [21.66270320648155]
  We present a comprehensive overview of the structures formed from code data. We categorize the models for understanding code in recent years into two groups: sequence-based and graph-based models. We also introduce metrics, datasets and the downstream tasks.
  arXiv  Detail & Related papers  (2022-05-03T03:56:17Z)
• CogNGen: Constructing the Kernel of a Hyperdimensional Predictive Processing Cognitive Architecture [79.07468367923619]
  We present a new cognitive architecture that combines two neurobiologically plausible, computational models. We aim to develop a cognitive architecture that has the power of modern machine learning techniques.
  arXiv  Detail & Related papers  (2022-03-31T04:44:28Z)
• Contrastive Learning for Source Code with Structural and Functional Properties [66.10710134948478]
  We present BOOST, a novel self-supervised model to focus pre-training based on the characteristics of source code. We employ automated, structure-guided code transformation algorithms that generate functionally equivalent code that looks drastically different from the original one. We train our model in a way that brings the functionally equivalent code closer and distinct code further through a contrastive learning objective.
  arXiv  Detail & Related papers  (2021-10-08T02:56:43Z)
• Unsupervised Learning of Neurosymbolic Encoders [40.3575054882791]
  We present a framework for the unsupervised learning of neurosymbolic encoders, i.e., encoders obtained by composing neural networks with symbolic programs from a domain-specific language. Such a framework can naturally incorporate symbolic expert knowledge into the learning process and lead to more interpretable and factorized latent representations than fully neural encoders.
  arXiv  Detail & Related papers  (2021-07-28T02:16:14Z)
• COSEA: Convolutional Code Search with Layer-wise Attention [90.35777733464354]
  We propose a new deep learning architecture, COSEA, which leverages convolutional neural networks with layer-wise attention to capture the code's intrinsic structural logic. COSEA can achieve significant improvements over state-of-the-art methods on code search tasks.
  arXiv  Detail & Related papers  (2020-10-19T13:53:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.