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| Name | Name | Last commit message | Last commit date |
| ## Latest commit<br>[akshai0296](https://github.com/akshai0296/coderdebugger/commits?author=akshai0296)<br>[Enhance README with project details and structure](https://github.com/akshai0296/coderdebugger/commit/accfa920718458f6ecc562b2a64ea9164bb4e81f)<br>Open commit details<br>last monthMar 13, 2026<br>[accfa92](https://github.com/akshai0296/coderdebugger/commit/accfa920718458f6ecc562b2a64ea9164bb4e81f) · last monthMar 13, 2026<br>## History<br>[27 Commits](https://github.com/akshai0296/coderdebugger/commits/main/) <br>Open commit details<br>View commit history for this file. 27 Commits |
| [Code Generation and Debugger](https://github.com/akshai0296/coderdebugger/tree/main/Code%20Generation%20and%20Debugger "Code Generation and Debugger") | [Code Generation and Debugger](https://github.com/akshai0296/coderdebugger/tree/main/Code%20Generation%20and%20Debugger "Code Generation and Debugger") | Delete Code Generation and Debugger/Fine tune | 10 months agoJun 6, 2025 |
| [Result](https://github.com/akshai0296/coderdebugger/tree/main/Result "Result") | [Result](https://github.com/akshai0296/coderdebugger/tree/main/Result "Result") | [Rename language\success\20250518\152054.png to Result/language\succes…](https://github.com/akshai0296/coderdebugger/commit/b5254b939c715af4f9fbbc628289e7a72777e088 "Rename languagesuccess20250518152054.png to Result/languagesuccess20250518_152054.png Signed-off-by: Akshai Padmanabhan <81634798+akshai0296@users.noreply.github.com>") | 10 months agoJun 6, 2025 |
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| [README.md](https://github.com/akshai0296/coderdebugger/blob/main/README.md "README.md") | [README.md](https://github.com/akshai0296/coderdebugger/blob/main/README.md "README.md") | Enhance README with project details and structure | last monthMar 13, 2026 |
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Coder Debugger
A runtime-grounded evaluation and self-correction pipeline for LLM code generation across Python, Java, and C++.
Motivation
Large language models can generate plausible code that still fails at compile time, breaks under tests, or contains deeper logical errors. For code agents to be reliable, they need more than one-shot generation. They need a closed-loop process that can execute outputs, diagnose failures, and iteratively repair buggy solutions using runtime feedback.
This repository studies that reliability problem directly. It implements a modular generate → execute → debug pipeline and evaluates whether runtime-grounded repair improves end-to-end code generation performance.
What this project does
Permalink: What this project does
The system takes a programming task plus tests, generates candidate code with an LLM, executes the code, captures compiler traces and runtime failures, and routes the failure context to a debugger module that proposes repairs.
The current pipeline supports:
- code generation
- runtime execution and validation
- failure capture from compiler errors, test failures, and runtime exceptions
- iterative self-debugging
- batch evaluation on benchmark tasks
- PEFT fine-tuning with LoRA / QLoRA for an offline debugger model
Why this matters
This project is aimed at reliable AI behavior rather than raw generation quality alone. It focuses on:
- detecting failure modes instead of trusting surface-level plausibility
- using runtime evidence as a grounding signal
- measuring recovery on concrete bug classes
- improving the dependability of code-generating systems
That makes it relevant to work on model evaluation, safeguards, runtime validation, and reliable agentic systems.
Pipeline overview
The system is organized as four main components:
- Generator
Produces candidate code for a task using an LLM.
- Executor
Runs the generated code against tests and captures compile errors, runtime errors, and failing outputs.
- Debugger
Uses the failure context to propose repairs, either through prompting or a fine-tuned offline debugger.
- Coordinator
Orchestrates the full loop from generation to execution to repair.
Repository structure
Permalink: Repository structure
coder_debugger/
│
├── Code Generation and Debugger/
│ ├── generator/ # Code generation logic
│ │ └── generator.py
│ ├── executor/ # Runtime execution module
│ │ └── executor.py
│ ├── debugger/ # Self-debugging and repair logic
│ │ └── debugger.py
│ ├── coordinator/ # End-to-end orchestration
│ │ └── pipeline.py
│
├── scripts/
│ ├── run_pipeline.py # Single-example execution
│ ├── batch_eval.py # Batch evaluation on benchmarks
│ └── train_lora.py # LoRA / QLoRA fine-tuning
│
├── benchmarks/
│ ├── results/ # Output from model runs
│ └── metrics/ # pass@1, accuracy, and related metricsAbout
Runtime-grounded evaluation and self-correction pipeline for LLM code generation across Python, Java, and C++.
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- [akshai0296 Akshai Padmanabhan](https://github.com/akshai0296)