Debug2Fix integrates interactive debugging via subagents into coding agents, delivering >20% gains on GitBug-Java and SWE-Bench-Live while enabling weaker models to match stronger ones.
Autodev: Automated ai-driven development.CoRR, abs/2403.08299
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
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.
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AgentLocate localizes multi-agent LLM failures to a responsible agent and earliest decisive step via judge hypotheses, confidence-weighted multi-evaluator verification, and LoRA refinement.
Deterministic orchestration matches LLM-controlled methods in COBOL-to-Python translation accuracy but improves worst-case robustness, reduces run-to-run variability, and cuts token consumption by up to 3.5 times.
Coding agents require a three-level proactivity taxonomy (Reactive, Scheduled, Situation Aware) evaluated by insight policy quality using Insight Decision Quality, Context Grounding Score, and Learning Lift.
Proposes GATF framework integrating governance controls into autonomous testing and reports 89.6% governance risk reduction plus 94.3-96.5% accuracy/reliability metrics on Defects4J and PROMISE datasets.
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
citing papers explorer
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Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?
Debug2Fix integrates interactive debugging via subagents into coding agents, delivering >20% gains on GitBug-Java and SWE-Bench-Live while enabling weaker models to match stronger ones.
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Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems
AgentLocate localizes multi-agent LLM failures to a responsible agent and earliest decisive step via judge hypotheses, confidence-weighted multi-evaluator verification, and LoRA refinement.
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Deterministic vs. LLM-Controlled Orchestration for COBOL-to-Python Modernization
Deterministic orchestration matches LLM-controlled methods in COBOL-to-Python translation accuracy but improves worst-case robustness, reduces run-to-run variability, and cuts token consumption by up to 3.5 times.
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Agentic Coding Needs Proactivity, Not Just Autonomy
Coding agents require a three-level proactivity taxonomy (Reactive, Scheduled, Situation Aware) evaluated by insight policy quality using Insight Decision Quality, Context Grounding Score, and Learning Lift.
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Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testing
Proposes GATF framework integrating governance controls into autonomous testing and reports 89.6% governance risk reduction plus 94.3-96.5% accuracy/reliability metrics on Defects4J and PROMISE datasets.
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Large Language Model-Based Agents for Software Engineering: A Survey
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.