mlsanityData to 7 Oct 2026

AutoDev: Automated AI-Driven Development

arXiv 2403.08299 ↗First posted 13 Mar 2024cs.SE cs.AIcited by 24

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Abstract (arXiv)

The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot. However, existing solutions are not leveraging all the potential capabilities available in an IDE such as building, testing, executing code, git operations, etc. Therefore, they are constrained by their limited capabilities, primarily focusing on suggesting code snippets and file manipulation within a chat-based interface. To fill this gap, we present AutoDev, a fully automated AI-driven software development framework, designed for autonomous planning and execution of intricate software engineering tasks. AutoDev enables users to define complex software engineering objectives, which are assigned to AutoDev's autonomous AI Agents to achieve. These AI agents can perform diverse operations on a codebase, including file editing, retrieval, build processes, execution, testing, and git operations. They also have access to files, compiler output, build and testing logs, static analysis tools, and more. This enables the AI Agents to execute tasks in a fully automated manner with a comprehensive understanding of the contextual information required. Furthermore, AutoDev establishes a secure development environment by confining all operations within Docker containers. This framework incorporates guardrails to ensure user privacy and file security, allowing users to define specific permitted or restricted commands and operations within AutoDev. In our evaluation, we tested AutoDev on the HumanEval dataset, obtaining promising results with 91.5% and 87.8% of Pass@1 for code generation and test generation respectively, demonstrating its effectiveness in automating software engineering tasks while maintaining a secure and user-controlled development environment.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 13 Mar 2024.

Builds on

8 of 16 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  5. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  6. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  7. 2022Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Modelcited by 557
  8. 2023CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completioncited by 181

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 12; 2026: 7.

24 papers.

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  1. 2024Agent-as-a-Judge: Evaluate Agents with Agentscited by 223
  2. 2024MASAI: Modular Architecture for Software-engineering AI Agentscited by 49
  3. 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AIcited by 39
  4. 2025Evaluating Agent-based Program Repair at Googlecited by 35
  5. 2025RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Codecited by 28
  6. 2025Agent READMEs: An Empirical Study of Context Files for Agentic Codingcited by 21
  7. 2024Causal Evaluation of Language Modelscited by 18
  8. 2025Unified Software Engineering Agent as AI Software Engineercited by 17
  9. 2025On the Use of Agentic Coding Manifests: An Empirical Study of Claude Codecited by 14
  10. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  11. 2024Collaborative Evolving Strategy for Automatic Data-Centric Developmentcited by 6
  12. 2025TableTalk: Scaffolding Spreadsheet Development with a Language Agentcited by 5
12 more
  1. 2025OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Modelscited by 5
  2. 2025Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationalecited by 3
  3. 2026Why Human Guidance Matters in Collaborative Vibe Codingcited by 3
  4. 2026How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responsescited by 3
  5. 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agentscited by 2
  6. 2024Morescient GAI for Software Engineering (Extended Version)cited by 1
  7. 2026Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?cited by 1
  8. 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Reviewcited by 1
  9. 2026Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systemscited by 1
  10. 2025Multi-Agent Game Generation and Evaluation via Audio-Visual Recordingscited by 0
  11. 2026Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testingcited by 0
  12. 2026From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Datacited by 0