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, 2024
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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.
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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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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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.