# Elixir: Effective object-oriented program repair

arXiv: https://arxiv.org/abs/2112.10915 · HTML: https://mlsanity.com/paper/2112.10915/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2021.

## Built on by (most cited first, 16 of 16)

- [Baldur: Whole-Proof Generation and Repair with Large Language Models](https://mlsanity.com/paper/2303.04910/) (2023)
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- [Agentic Bug Reproduction for Effective Automated Program Repair at Google](https://mlsanity.com/paper/2502.01821/) (2025)
- [Can Transformers Learn to Solve Problems Recursively?](https://mlsanity.com/paper/2305.14699/) (2023)
- [ITER: Iterative Neural Repair for Multi-Location Patches](https://mlsanity.com/paper/2304.12015/) (2023)
- [Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions](https://mlsanity.com/paper/2304.03816/) (2023)
- [Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion](https://mlsanity.com/paper/2401.12947/) (2024)
- [KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair](https://mlsanity.com/paper/2302.01857/) (2023)
- [RunBugRun -- An Executable Dataset for Automated Program Repair](https://mlsanity.com/paper/2304.01102/) (2023)
- [Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models](https://mlsanity.com/paper/2307.14349/) (2023)
- [NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History](https://mlsanity.com/paper/2203.00191/) (2022)
- [Revisiting the Plastic Surgery Hypothesis via Large Language Models](https://mlsanity.com/paper/2303.10494/) (2023)
- [The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models](https://mlsanity.com/paper/2505.02931/) (2025)
- [Out of Context: How important is Local Context in Neural Program Repair?](https://mlsanity.com/paper/2312.04986/) (2023)
- [NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repair](https://mlsanity.com/paper/2406.16526/) (2024)
- [Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework](https://mlsanity.com/paper/2410.15285/) (2024)

## Abstract (arXiv)

This work is motivated by the pervasive use of method invocations in object-oriented (OO) programs, and indeed their prevalence in patches of OO-program bugs. We propose a generate-and-validate repair technique, called ELIXIR designed to be able to generate such patches. ELIXIR aggressively uses method calls, on par with local variables, fields, or constants, to construct more expressive repair-expressions, that go into synthesizing patches. The ensuing enlargement of the repair space, on account of the wider use of method calls, is effectively tackled by using a machine-learnt model to rank concrete repairs. The machine-learnt model relies on four features derived from the program context, i.e., the code surrounding the potential repair location, and the bug report. We implement ELIXIR and evaluate it on two datasets, the popular Defects4J dataset and a new dataset Bugs.jar created by us, and against 2 baseline versions of our technique, and 5 other techniques representing the state of the art in program repair. Our evaluation shows that ELIXIR is able to increase the number of correctly repaired bugs in Defects4J by 85% (from 14 to 26) and by 57% in Bugs.jar (from 14 to 22), while also significantly out-performing other state-of-the-art repair techniques including ACS, HD-Repair, NOPOL, PAR, and jGenProg.