# Automated Classification of Overfitting Patches with Statically Extracted Code Features

arXiv: https://arxiv.org/abs/1910.12057 · HTML: https://mlsanity.com/paper/1910.12057/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [CigaR: Cost-efficient Program Repair with LLMs](https://mlsanity.com/paper/2402.06598/) (2024)
- [Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning](https://mlsanity.com/paper/2301.01113/) (2023)
- [Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness](https://mlsanity.com/paper/2208.04125/) (2022)
- [Automated Program Repair: Emerging trends pose and expose problems for benchmarks](https://mlsanity.com/paper/2405.05455/) (2024)
- [Predicting Patch Correctness Based on the Similarity of Failing Test Cases](https://mlsanity.com/paper/2107.13296/) (2021)
- [InvAASTCluster: On Applying Invariant-Based Program Clustering to Introductory Programming Assignments](https://mlsanity.com/paper/2206.14175/) (2022)
- [Multi-Granularity Detector for Vulnerability Fixes](https://mlsanity.com/paper/2305.13884/) (2023)
- [CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back](https://mlsanity.com/paper/2302.03924/) (2023)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment](https://mlsanity.com/paper/2507.22580/) (2025)

## Abstract (arXiv)

Automatic program repair (APR) aims to reduce the cost of manually fixing software defects. However, APR suffers from generating a multitude of overfitting patches, those patches that fail to correctly repair the defect beyond making the tests pass. This paper presents a novel overfitting patch detection system called ODS to assess the correctness of APR patches. ODS first statically compares a patched program and a buggy program in order to extract code features at the abstract syntax tree (AST) level. Then, ODS uses supervised learning with the captured code features and patch correctness labels to automatically learn a probabilistic model. The learned ODS model can then finally be applied to classify new and unseen program repair patches. We conduct a large-scale experiment to evaluate the effectiveness of ODS on patch correctness classification based on 10,302 patches from Defects4J, Bugs.jar and Bears benchmarks. The empirical evaluation shows that ODS is able to correctly classify 71.9% of program repair patches from 26 projects, which improves the state-of-the-art. ODS is applicable in practice and can be employed as a post-processing procedure to classify the patches generated by different APR systems.