Related papers: Automatic Programming: Large Language Models and Beyond

Automatic Programming: Large Language Models and Beyond

URL: http://arxiv.org/abs/2405.02213v2
Date: Wed, 15 May 2024 16:33:57 GMT
Title: Automatic Programming: Large Language Models and Beyond
Authors: Michael R. Lyu, Baishakhi Ray, Abhik Roychoudhury, Shin Hwei Tan, Patanamon Thongtanunam,
Abstract summary: We study concerns around code quality, security and related issues of programmer responsibility. We discuss how advances in software engineering can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future.
Score: 48.34544922560503
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance

Related papers

No Man is an Island: Towards Fully Automatic Programming by Code Search, Code Generation and Program Repair [9.562123938545522]
toolname can integrate various code search, generation, and repair tools, combining these three research areas together for the first time. We conduct preliminary experiments to demonstrate the potential of our framework, eg helping CodeLlama solve 267 programming problems with an improvement of 62.53%.
arXiv Detail & Related papers (2024-09-05T06:24:29Z)
MarsCode Agent: AI-native Automated Bug Fixing [7.909344108948294]
We introduce MarsCode Agent, a novel framework that leverages large language models to automatically identify and repair bugs in software code. Our approach follows a systematic process of planning, bug reproduction, fault localization, candidate patch generation, and validation to ensure high-quality bug fixes. Our results show that MarsCode Agent achieves a high success rate in bug fixing compared to most of the existing automated approaches.
arXiv Detail & Related papers (2024-09-02T02:24:38Z)
Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z)
AutoCodeRover: Autonomous Program Improvement [8.66280420062806]
We propose an automated approach for solving GitHub issues to autonomously achieve program improvement. In our approach called AutoCodeRover, LLMs are combined with sophisticated code search capabilities, ultimately leading to a program modification or patch. Experiments on SWE-bench-lite (300 real-life GitHub issues) show increased efficacy in solving GitHub issues (19% on SWE-bench-lite), which is higher than the efficacy of the recently reported SWE-agent.
arXiv Detail & Related papers (2024-04-08T11:55:09Z)
AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning [54.47116888545878]
AutoAct is an automatic agent learning framework for QA. It does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models.
arXiv Detail & Related papers (2024-01-10T16:57:24Z)
Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
arXiv Detail & Related papers (2022-03-06T10:12:56Z)
Competition-Level Code Generation with AlphaCode [74.87216298566942]
We introduce AlphaCode, a system for code generation that can create novel solutions to problems that require deeper reasoning. In simulated evaluations on recent programming competitions on the Codeforces platform, AlphaCode achieved on average a ranking of top 54.3%.
arXiv Detail & Related papers (2022-02-08T23:16:31Z)
Automated Aggregator -- Rewriting with the Counting Aggregate [0.0]
We present an automated rewriting system that produces a family of equivalent programs with complementary performance. We propose the system's use in automated answer set programming solver selection tools.
arXiv Detail & Related papers (2020-09-22T00:48:33Z)
Induction and Exploitation of Subgoal Automata for Reinforcement Learning [75.55324974788475]
We present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induction of a subgoal automaton, an automaton whose edges are labeled by the task's subgoals. A subgoal automaton also consists of two special states: a state indicating the successful completion of the task, and a state indicating that the task has finished without succeeding.
arXiv Detail & Related papers (2020-09-08T16:42:55Z)

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