# DEAR: A Novel Deep Learning-based Approach for Automated Program Repair

arXiv: https://arxiv.org/abs/2205.01859 · HTML: https://mlsanity.com/paper/2205.01859/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2022.

## Built on by (most cited first, 22 of 22)

- [Is ChatGPT the Ultimate Programming Assistant -- How far is it?](https://mlsanity.com/paper/2304.11938/) (2023)
- [MarsCode Agent: AI-native Automated Bug Fixing](https://mlsanity.com/paper/2409.00899/) (2024)
- [Large Language Models for Test-Free Fault Localization](https://mlsanity.com/paper/2310.01726/) (2023)
- [RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair](https://mlsanity.com/paper/2309.06057/) (2023)
- [A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification](https://mlsanity.com/paper/2305.14752/) (2023)
- [An Empirical Study on LLM-based Agents for Automated Bug Fixing](https://mlsanity.com/paper/2411.10213/) (2024)
- [SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs](https://mlsanity.com/paper/2504.14757/) (2025)
- [Exploring ChatGPT's Capabilities on Vulnerability Management](https://mlsanity.com/paper/2311.06530/) (2023)
- [ITER: Iterative Neural Repair for Multi-Location Patches](https://mlsanity.com/paper/2304.12015/) (2023)
- [Large Language Models Based Automatic Synthesis of Software Specifications](https://mlsanity.com/paper/2304.09181/) (2023)
- [Improving Automated Program Repair with Domain Adaptation](https://mlsanity.com/paper/2212.11414/) (2022)
- [Extending the Frontier of ChatGPT: Code Generation and Debugging](https://mlsanity.com/paper/2307.08260/) (2023)
- [Multi-Granularity Detector for Vulnerability Fixes](https://mlsanity.com/paper/2305.13884/) (2023)
- [The Impact of Fine-tuning Large Language Models on Automated Program Repair](https://mlsanity.com/paper/2507.19909/) (2025)
- [Risks of ignoring uncertainty propagation in AI-augmented security pipelines](https://mlsanity.com/paper/2407.14540/) (2024)
- [Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent](https://mlsanity.com/paper/2512.14990/) (2025)
- [Multi-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs](https://mlsanity.com/paper/2607.12605/) (2026)
- [A Retrospective on ICSE 2022](https://mlsanity.com/paper/2207.12578/) (2022)
- [Multi-Task Program Error Repair and Explanatory Diagnosis](https://mlsanity.com/paper/2410.07271/) (2024)
- [Logic Error Localization in Student Programming Assignments Using Pseudocode and Graph Neural Networks](https://mlsanity.com/paper/2410.21282/) (2024)
- [T3: Multi-level Tree-based Automatic Program Repair with Large Language Models](https://mlsanity.com/paper/2506.21211/) (2025)
- [Smark: A Watermark for Text-to-Speech Diffusion Models via Discrete Wavelet Transform](https://mlsanity.com/paper/2512.18791/) (2025)

## Abstract (arXiv)

The existing deep learning (DL)-based automated program repair (APR) models are limited in fixing general software defects. % We present tool, a DL-based approach that supports fixing for the general bugs that require dependent changes at once to one or multiple consecutive statements in one or multiple hunks of code. % We first design a novel fault localization (FL) technique for multi-hunk, multi-statement fixes that combines traditional spectrum-based (SB) FL with deep learning and data-flow analysis. It takes the buggy statements returned by the SBFL model, detects the buggy hunks to be fixed at once, and expands a buggy statement s in a hunk to include other suspicious statements around s. We design a two-tier, tree-based LSTM model that incorporates cycle training and uses a divide-and-conquer strategy to learn proper code transformations for fixing multiple statements in the suitable fixing context consisting of surrounding subtrees. We conducted several experiments to evaluate tool on three datasets: Defects4J (395 bugs), BigFix (+26k bugs), and CPatMiner (+44k bugs). On Defects4J dataset, tool outperforms the baselines from 42%--683% in terms of the number of auto-fixed bugs with only the top-1 patches. On BigFix dataset, it fixes 31--145 more bugs than existing DL-based APR models with the top-1 patches. On CPatMiner dataset, among 667 fixed bugs, there are 169 (25.3%) multi-hunk/multi-statement bugs. tool fixes 71 and 164 more bugs, including 52 and 61 more multi-hunk/multi-statement bugs, than the state-of-the-art, DL-based APR models.