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Self-Planning Code Generation with Large Language Models

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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The Waterfall Model in Large-Scale Development

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LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

inferredcites →https://doi.org/10.1109/icra48891.2023.10161317openalex-snapshot

ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

inferredcites →https://doi.org/10.1109/icse48619.2023.00179openalex-snapshot

SkCoder: A Sketch-based Approach for Automatic Code Generation

inferredcites →https://doi.org/10.1126/science.abq1158openalex-snapshot

Competition-level code generation with AlphaCode

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Software development lifecycle models

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Code completion with statistical language models

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Deep API learning

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Incorporating domain knowledge through task augmentation for front-end JavaScript code generation

inferredcites →https://doi.org/10.1145/3560815openalex-snapshot

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

inferredcites →https://doi.org/10.1145/3580305.3599790openalex-snapshot

CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X

inferredcites →https://doi.org/10.1145/3597926.3598048openalex-snapshot

CODEP: Grammatical Seq2Seq Model for General-Purpose Code Generation

inferredcites →https://doi.org/10.1145/3613372.3614197openalex-snapshot

Large Language Models for Education: Grading Open-Ended Questions Using ChatGPT

inferredcites →https://doi.org/10.1145/3689217.3690621openalex-snapshot

PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

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CodeScore: Evaluating Code Generation by Learning Code Execution

inferredcites →https://doi.org/10.1609/aaai.v33i01.33017055openalex-snapshot

A Grammar-Based Structural CNN Decoder for Code Generation

inferredcites →https://doi.org/10.1609/aaai.v34i05.6430openalex-snapshot

TreeGen: A Tree-Based Transformer Architecture for Code Generation

inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.685openalex-snapshot

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

inferredcites →https://doi.org/10.18653/v1/2022.acl-long.499openalex-snapshot

UniXcoder: Unified Cross-Modal Pre-training for Code Representation

inferredcites →https://doi.org/10.18653/v1/2022.emnlp-main.90openalex-snapshot

Language Models of Code are Few-Shot Commonsense Learners

inferredcites →https://doi.org/10.18653/v1/2022.naacl-main.191openalex-snapshot

Learning To Retrieve Prompts for In-Context Learning

inferredcites →https://doi.org/10.18653/v1/2023.acl-long.754openalex-snapshot

Self-Instruct: Aligning Language Models with Self-Generated Instructions

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Self-Criticism: Aligning Large Language Models with their Understanding of Helpfulness, Honesty, and Harmlessness

inferredcites →https://doi.org/10.18653/v1/2023.emnlp-main.67openalex-snapshot

Large Language Models Can Self-Improve

inferredcites →https://doi.org/10.18653/v1/2023.findings-acl.138openalex-snapshot

Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding

inferredcites →https://doi.org/10.18653/v1/d18-2002openalex-snapshot

TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

inferredcites →https://doi.org/10.18653/v1/p16-1002openalex-snapshot

Data Recombination for Neural Semantic Parsing

inferredcites →https://doi.org/10.18653/v1/p16-1057openalex-snapshot

Latent Predictor Networks for Code Generation

inferredcites →https://doi.org/10.18653/v1/p17-1041openalex-snapshot

A Syntactic Neural Model for General-Purpose Code Generation

inferredcites →https://doi.org/10.18653/v1/p17-1105openalex-snapshot

Abstract Syntax Networks for Code Generation and Semantic Parsing

inferredcites →https://doi.org/10.24963/ijcai.2017/514openalex-snapshot

DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning

inferredcites →https://doi.org/10.4018/9781599048871.ch006openalex-snapshot

Requirements Engineering

inferredcites →https://doi.org/10.48550/arxiv.1709.08439openalex-snapshot

Agile Software Development Methods: Review and Analysis

inferredcites →https://doi.org/10.48550/arxiv.2201.11903openalex-snapshot

BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence

inferredcites →https://doi.org/10.48550/arxiv.2203.13474openalex-snapshot

CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

inferredcites →https://doi.org/10.48550/arxiv.2305.06599openalex-snapshot

Structured Chain-of-Thought Prompting for Code Generation

inferredcites →https://doi.org/10.52202/075280-1657openalex-snapshot

HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face

What points at this work

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inferred← citeshttps://doi.org/10.1145/3805713.3820415openalex-snapshot

Plan-and-Refine in RAG: Generating Diverse and Comprehensive Responses through Global Exploration and Local Exploitation

inferred← citeshttps://doi.org/10.1145/3808097openalex-snapshot

PlayCoder: Making LLM-Generated GUI Code Playable

inferred← citeshttps://doi.org/10.1145/3808198openalex-snapshot

One Size Does Fit All: Exploring Model Fusion for Software Engineering Tasks

inferred← citeshttps://doi.org/10.1145/3808200openalex-snapshot

Influence-Aware Bayesian-Inspired Token Reweighting for Improved Code Generation

inferred← citeshttps://doi.org/10.1145/3832783.3837422openalex-snapshot

DesignFix: Context-Augmented Repair of Design Rule Violations

inferred← citeshttps://doi.org/10.1145/3832783.3837433openalex-snapshot

AlphaCu: A Transformation-Driven Synthesis Framework for LLM-Based GPU Kernel Generation

inferred← citeshttps://doi.org/10.1145/3832783.3837454openalex-snapshot

It Takes Two to Tango: Accelerating Code Generation via Cache-Inspired Hierarchical Speculative Decoding

inferred← citeshttps://doi.org/10.1145/3832783.3837487openalex-snapshot

Aligning User Requirement with Large Language Model in Code Generation

inferred← citeshttps://doi.org/10.1145/3839452openalex-snapshot

T-REX: Teaching Large Language Models to Reason with Verbalized Execution Semantics

inferred← citeshttps://doi.org/10.1145/3840382openalex-snapshot

Cross-Model Collaboration for Enhancing LLM-Based Code Generation

inferred← citeshttps://doi.org/10.1145/3842390openalex-snapshot

KGMACG: Knowledge-Guided Multi-Agent Orchestration for Scalable Application-Level Code Generation

inferred← citeshttps://doi.org/10.1145/3849809openalex-snapshot

LLM See, LLM Do: Enhancing Repository-Aware Code Generation in LLMs Through Logical Context and Usage Knowledge

inferred← citeshttps://doi.org/10.1177/01466216261492558openalex-snapshot

Selecting Between Whole-Sample and Split-Sample Strategies for Exploratory and Confirmatory Factor Analyses: Guidance for Psychometric Scale Development

inferred← citeshttps://doi.org/10.1177/22104968261465514openalex-snapshot

Large Language Models for Ontology Engineering: A Systematic Literature Review

inferred← citeshttps://doi.org/10.1371/journal.pone.0317084openalex-snapshot

Leveraging large language models for data analysis automation

inferred← citeshttps://doi.org/10.14801/jkiit.2025.23.2.209openalex-snapshot

Automated Code Generation System for Arduino based on Device Registry

inferred← citeshttps://doi.org/10.15439/2025f1481openalex-snapshot

Optimizing the Optimizer: An Example Showing the Power of LLM Code Generation

inferred← citeshttps://doi.org/10.1609/aaai.v38i18.30006openalex-snapshot

Generalized Planning in PDDL Domains with Pretrained Large Language Models

inferred← citeshttps://doi.org/10.21203/rs.3.rs-9719613/v1openalex-snapshot

Evaluating Large Language Models for Translating Multimodal Phenotype Documentations into Executable EHR Phenotyping Algorithms

inferred← citeshttps://doi.org/10.2139/ssrn.5351223openalex-snapshot

Rk3e-Coder:Llm-Based Code Generation Framework with Code Knowledge, Algorithmic Knowledge, Third-Party Library Knowledge

inferred← citeshttps://doi.org/10.2139/ssrn.6843083openalex-snapshot

BenjiTest: An Agentic Framework for Evaluating LLM-Based Unit Test Generation and Correction

inferred← citeshttps://doi.org/10.2139/ssrn.6843084openalex-snapshot

BenjiTest: An Agentic Framework for Evaluating LLM-Based Unit Test Generation and Correction

inferred← citeshttps://doi.org/10.2139/ssrn.7442692openalex-snapshot

BenjiTest: An Agentic Framework for Evaluating LLM-Based Unit Test Generation and Correction

inferred← citeshttps://doi.org/10.30857/2786-5371.2025.1.3openalex-snapshot

Optimising productivity and automating software development: Innovative memory system approaches in large language models

inferred← citeshttps://doi.org/10.3389/frai.2025.1660912openalex-snapshot

Blueprint2Code: a multi-agent pipeline for reliable code generation via blueprint planning and repair

inferred← citeshttps://doi.org/10.3390/aerospace12060498openalex-snapshot

Using Large Language Models for Aerospace Code Generation: Methods, Benchmarks, and Potential Values

inferred← citeshttps://doi.org/10.3390/sym17081325openalex-snapshot

Symmetry-Aware Code Generation: Distilling Pseudocode Reasoning for Lightweight Deployment of Large Language Models

inferred← citeshttps://doi.org/10.3390/sym17101660openalex-snapshot

Leveraging Symmetry in Multi-Agent Code Generation: A Cross-Verification Collaboration Protocol for Competitive Programming

inferred← citeshttps://doi.org/10.36227/techrxiv.22773842openalex-snapshot

CHANCES AND CHALLENGES OF CHATGPT AND SIMILAR MODELS FOR EDUCATION IN M&S

inferred← citeshttps://doi.org/10.36227/techrxiv.22773842.v1openalex-snapshot

CHANCES AND CHALLENGES OF CHATGPT AND SIMILAR MODELS FOR EDUCATION IN M&S

inferred← citeshttps://doi.org/10.3991/ijim.v20i03.59861openalex-snapshot

Large Language Model Selection for Test-Driven Prompt Android iOS Development

inferred← citeshttps://doi.org/10.56975/jetir.v12i5.563406openalex-snapshot

Assessing Code Synthesis In LLMS: CodeGeex and Codeium

inferred← citeshttps://doi.org/10.62836/jer.v3n9.0676openalex-snapshot

新质人才培养导向下应用型高校软件工程“一体两翼”实践教学模式创新研究

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.