Program Synthesis
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Program synthesis is the process of automatically generating a program or code snippet that satisfies a given specification or set of requirements. This can include generating code from a formal specification, a natural language description, or example inputs and outputs. The primary goal of program synthesis is to minimize human intervention in the coding process, reduce errors, and improve productivity.
Program synthesis often involves the use of advanced algorithms, artificial intelligence, and machine learning techniques to search the space of possible programs that meet the given constraints. This process can be guided by a variety of techniques, such as constraint solving, symbolic execution, and genetic algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lemingshen/AutoIOT
공식 코드AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT Applications
https://github.com/lemingshen/GPIoT
공식 코드GPIoT: Tailoring Small Language Models for IoT Program Synthesis and Development
https://github.com/lin-tan/CURE
공식 코드CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
https://github.com/lin-tan/knod
공식 코드KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair
https://github.com/lin-tan/llm-vul
공식 코드How Effective Are Neural Networks for Fixing Security Vulnerabilities
https://github.com/lishangyu-hkust/osvbench
공식 코드OSVBench: Benchmarking LLMs on Specification Generation Tasks for Operating System Verification
https://github.com/liyuechao1998/scgsl
공식 코드https://github.com/llm4se/obfuscated-chatgpt-experiments
공식 코드Breaking the Silence: the Threats of Using LLMs in Software Engineering
https://github.com/loadingbyte/timexplain
공식 코드timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
https://github.com/logic-and-learning-lab/aaai22-dcc
공식 코드Learning logic programs through divide, constrain, and conquer
https://github.com/logic-and-learning-lab/aaai23-disco
공식 코드https://github.com/logic-and-learning-lab/ecai23-combo
공식 코드https://github.com/lucy66666/okt
공식 코드https://github.com/lzhenwen/mwp-bert
공식 코드MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
https://github.com/machine-teaching-group/edm2022_studentsyn
공식 코드From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks
https://github.com/manueleberhardinger/ec-rl
공식 코드https://github.com/mboehme/learn2fix
공식 코드https://github.com/md-ashraful-pramanik/mapcoder
공식 코드MapCoder: Multi-Agent Code Generation for Competitive Problem Solving
https://github.com/mdrafiqulrabin/CI-Memorization
공식 코드Memorization and Generalization in Neural Code Intelligence Models
https://github.com/mdrafiqulrabin/SIVAND
공식 코드Understanding Neural Code Intelligence Through Program Simplification
https://github.com/meelgroup/DeQuS
공식 코드Program Synthesis as Dependency Quantified Formula Modulo Theory
https://github.com/megagonlabs/doduo
공식 코드https://github.com/michaelhodel/re-arc
공식 코드https://github.com/michiyasunaga/DrRepair
공식 코드Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
https://github.com/michiyasunaga/bifi
공식 코드https://github.com/microsoft/CodeBERT
공식 코드CodeBERT: A Pre-Trained Model for Programming and Natural Languages
https://github.com/microsoft/ProphetNet
공식 코드CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
https://github.com/microsoft/PythonProgrammingPuzzles
공식 코드https://github.com/microsoft/nemoeval
공식 코드Enhancing Network Management Using Code Generated by Large Language Models
https://github.com/microsoft/tracecodegen
공식 코드Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions
https://github.com/ml-research/pix2code
공식 코드Pix2Code: Learning to Compose Neural Visual Concepts as Programs
https://github.com/modelscope/adaseq
공식 코드Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing
https://github.com/mojtabavalizadeh/paresy
공식 코드https://github.com/mtensor/neural_sketch
공식 코드https://github.com/mtensor/rulesynthesis
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.