PhoenixRepair is a multi-agent framework that systematically explores multiple candidate location sets and performs iterative reflection and refinement on patch generation, thereby expanding the search space of repair strategies for automated issue resolution.
- State-of-the-art Performance: Achieves 74.4% pass@1 on SWE-bench Verified with DeepSeek-V3.2
- Superior Fault Localization: Demonstrates higher fault localization accuracy across file, module, and function granularities
- Model-Agnostic: Consistent performance gains across different LLMs (DeepSeek-V3.1, DeepSeek-V3.2, Qwen-Coder-Plus)
- Generalizable Framework: Successfully transfers to other agent-based methods (Mini-SWE-agent, Live-SWE-agent)
| Method | LLM | Resolved (%) |
|---|---|---|
| Mini-SWE-agent | DeepSeek-V3.2 | 61.0% |
| Live-SWE-agent | DeepSeek-V3.2 | 63.8% |
| SWE-Search | DeepSeek-V3.2 | 65.4% |
| Agentless | DeepSeek-V3.2 | 67.0% |
| Trae-agent | DeepSeek-V3.2 | 67.8% |
| SWE-agent | DeepSeek-V3.2 | 69.4% |
| Moatless-tools | Claude-4-Sonnet | 70.8% |
| PhoenixRepair | DeepSeek-V3.2 | 74.4% (+5.0%) |
| Method | LLM | File Acc@1 | Module Acc@1 | Function Acc@1 |
|---|---|---|---|---|
| SWE-agent | DeepSeek-V3.2 | 83.04% | 74.22% | 63.42% |
| PhoenixRepair | DeepSeek-V3.2 | 85.64% (+2.60%) | 76.82% (+2.60%) | 66.64% (+3.22%) |
PhoenixRepair comprises three main phases:
- Localization Sampling Agent: Iteratively samples N diverse location sets
- Graph-based Localization (for difficult tasks): Provides cross-file dependency information
- Deduplication: Removes duplicate location sets to obtain final candidates
- Coder Agent: Generates patches constrained to specific location sets
- Analysis & Test Agent: Evaluates patch quality through:
- Test quality assessment
- Regression test pass rate
- Selector Agent: Selects top-performing patches
- Analysis Agent: Distills guidance from historical attempts
- Iterative Refinement: Continues until converging to final location set
- Analysis agent distills insights from all historical attempts
- Generates final patch guided by comprehensive distilled knowledge
Run
conda create --name phoenix python=3.11 conda activate phoenix python -m pip install --upgrade pip && pip install --editable .
at the repository root
# Set up your API keys export OPENAI_API_KEY="your-api-key" export DEEPSEEK_API_KEY="your-deepseek-key"
We design a tool at PhoenixRepair/tools/analysis_complete/ to verify whether each task has been completed successfully.
sweagent run-locate \
--config config/locate.yaml \
--agent.model.name "" \ # Please enter the model name, for example "deepseek-chat"
--agent.model.api_base "" \ # Please enter the url, for example "https://api.deepseek.com/v1"
--agent.model.per_instance_cost_limit 3.00 \
--instances.type swe_bench \
--instances.subset verified \
--instances.split test \
--enable_multi_sampling=True \
--specific_instance_ids="" \ # Specify the instance IDs to be executed
--num_samples=5 \ # The Number of sequential samples per task
--num_workers=25 \ # The number of tasks executed in parallel
--enable_best_sample_selection=False \
--comparison_model_name="" \ # Please enter the model name, for example "deepseek-chat"
--comparison_api_key="" \ # Please enter the api_key
--comparison_api_base="" \ # Please enter the url, for example "https://api.deepseek.com/v1"sweagent run-batch \
--config config/default.yaml \
--agent.model.name "" \ # Please enter the model name, for example "deepseek-chat"
--agent.model.api_base "" \ # Please enter the url, for example "https://api.deepseek.com/v1"
--agent.model.per_instance_cost_limit 3.00 \
--instances.type swe_bench \
--instances.subset verified \
--instances.split test \
--enable_multi_sampling=True \
--specific_instance_ids="" \ # Specify the instance IDs to be executed
--num_samples=3 \
--comparison_model_name="" \ # Please enter the model name, for example "deepseek-chat"
--comparison_api_key="" \ # Please enter the api_key
--comparison_api_base="" \ # Please enter the url, for example "https://api.deepseek.com/v1"
--deduplicated_patches_root "" # Please enter the absolute path obtained in Phase 1， for example "/home/PhoenixPepair/trajectories/root/locate__openai--GLM-4.7__t-0.70__p-1.00__c-0.00___swe_bench_verified_test"git clone https://github.com/SWE-bench/SWE-bench.git
python tackle_pred.py
python evaluation/run_evaluation.py \
--results_dir "" \ # Please Enter The absolute path obtained in Phase 2, for example "/home/PhoenixPepair/trajectories/root/default__openai--GLM-4.7__t-0.70__p-1.00__c-0.00___swe_bench_verified_test"